BREAST_CANCER_PREDICTION.ipynb

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      "cell_type": "markdown",
      "source": [
        "\n",
        "# <b>  Import the required Libraries  </b>\n"
      ],
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      }
    },
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      "cell_type": "code",
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      "metadata": {
        "id": "09aKe-4lcWxW"
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      "source": [
        "# importing libraries\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Collection and Pre-Processing**\n",
        "\n"
      ],
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        "id": "Sud8XKM0nV6P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# reading data from the file\n",
        "\n",
        "df=pd.read_csv(\"data.csv\")"
      ],
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        "id": "zB66Jt1ucoHz"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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              "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0    842302         M        17.99         10.38          122.80     1001.0   \n",
              "1    842517         M        20.57         17.77          132.90     1326.0   \n",
              "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
              "3  84348301         M        11.42         20.38           77.58      386.1   \n",
              "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
              "0  ...          17.33           184.60      2019.0            0.1622   \n",
              "1  ...          23.41           158.80      1956.0            0.1238   \n",
              "2  ...          25.53           152.50      1709.0            0.1444   \n",
              "3  ...          26.50            98.87       567.7            0.2098   \n",
              "4  ...          16.67           152.20      1575.0            0.1374   \n",
              "\n",
              "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
              "0             0.6656           0.7119                0.2654          0.4601   \n",
              "1             0.1866           0.2416                0.1860          0.2750   \n",
              "2             0.4245           0.4504                0.2430          0.3613   \n",
              "3             0.8663           0.6869                0.2575          0.6638   \n",
              "4             0.2050           0.4000                0.1625          0.2364   \n",
              "\n",
              "   fractal_dimension_worst  Unnamed: 32  \n",
              "0                  0.11890          NaN  \n",
              "1                  0.08902          NaN  \n",
              "2                  0.08758          NaN  \n",
              "3                  0.17300          NaN  \n",
              "4                  0.07678          NaN  \n",
              "\n",
              "[5 rows x 33 columns]"
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
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              "      <th>Unnamed: 32</th>\n",
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              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>M</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>M</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>M</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
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              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
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              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
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              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
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      "source": [
        "df.info()"
      ],
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        },
        "id": "VdEbjYarcvYk",
        "outputId": "7a1ca878-2fb7-45be-f7f9-f2c0af6b4040"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 33 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    object \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            " 32  Unnamed: 32              0 non-null      float64\n",
            "dtypes: float64(31), int64(1), object(1)\n",
            "memory usage: 146.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Check For the Null Values and Clean the Dataset*"
      ],
      "metadata": {
        "id": "-n5PWCCSoOLS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# return all the columns with null values count\n",
        "\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0lmg_6-czSz",
        "outputId": "815f90fd-dd23-4afa-dae8-7a80e73d24e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                           0\n",
              "diagnosis                    0\n",
              "radius_mean                  0\n",
              "texture_mean                 0\n",
              "perimeter_mean               0\n",
              "area_mean                    0\n",
              "smoothness_mean              0\n",
              "compactness_mean             0\n",
              "concavity_mean               0\n",
              "concave points_mean          0\n",
              "symmetry_mean                0\n",
              "fractal_dimension_mean       0\n",
              "radius_se                    0\n",
              "texture_se                   0\n",
              "perimeter_se                 0\n",
              "area_se                      0\n",
              "smoothness_se                0\n",
              "compactness_se               0\n",
              "concavity_se                 0\n",
              "concave points_se            0\n",
              "symmetry_se                  0\n",
              "fractal_dimension_se         0\n",
              "radius_worst                 0\n",
              "texture_worst                0\n",
              "perimeter_worst              0\n",
              "area_worst                   0\n",
              "smoothness_worst             0\n",
              "compactness_worst            0\n",
              "concavity_worst              0\n",
              "concave points_worst         0\n",
              "symmetry_worst               0\n",
              "fractal_dimension_worst      0\n",
              "Unnamed: 32                569\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# return the size of dataset\n",
        "\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gu_buHI5c7jI",
        "outputId": "8e1b0403-0398-4a4d-aa71-361a0c67db7d"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove the null value column\n",
        "\n",
        "df=df.dropna(axis=1)"
      ],
      "metadata": {
        "id": "c0xhpTHudAlG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# shape of dataset after removing the null column\n",
        "\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcE-1vsmdG8J",
        "outputId": "1d831d62-5ef1-432f-916e-d6bf8578752c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# describe the dataset\n",
        "\n",
        "df.describe().T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FqxxsHZAdLfh",
        "outputId": "ecb9ac69-22d5-4890-e764-f3bed0c78320"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         count          mean           std          min  \\\n",
              "id                       569.0  3.037183e+07  1.250206e+08  8670.000000   \n",
              "diagnosis                569.0  3.725835e-01  4.839180e-01     0.000000   \n",
              "radius_mean              569.0  1.412729e+01  3.524049e+00     6.981000   \n",
              "texture_mean             569.0  1.928965e+01  4.301036e+00     9.710000   \n",
              "perimeter_mean           569.0  9.196903e+01  2.429898e+01    43.790000   \n",
              "area_mean                569.0  6.548891e+02  3.519141e+02   143.500000   \n",
              "smoothness_mean          569.0  9.636028e-02  1.406413e-02     0.052630   \n",
              "compactness_mean         569.0  1.043410e-01  5.281276e-02     0.019380   \n",
              "concavity_mean           569.0  8.879932e-02  7.971981e-02     0.000000   \n",
              "concave points_mean      569.0  4.891915e-02  3.880284e-02     0.000000   \n",
              "symmetry_mean            569.0  1.811619e-01  2.741428e-02     0.106000   \n",
              "fractal_dimension_mean   569.0  6.279761e-02  7.060363e-03     0.049960   \n",
              "radius_se                569.0  4.051721e-01  2.773127e-01     0.111500   \n",
              "texture_se               569.0  1.216853e+00  5.516484e-01     0.360200   \n",
              "perimeter_se             569.0  2.866059e+00  2.021855e+00     0.757000   \n",
              "area_se                  569.0  4.033708e+01  4.549101e+01     6.802000   \n",
              "smoothness_se            569.0  7.040979e-03  3.002518e-03     0.001713   \n",
              "compactness_se           569.0  2.547814e-02  1.790818e-02     0.002252   \n",
              "concavity_se             569.0  3.189372e-02  3.018606e-02     0.000000   \n",
              "concave points_se        569.0  1.179614e-02  6.170285e-03     0.000000   \n",
              "symmetry_se              569.0  2.054230e-02  8.266372e-03     0.007882   \n",
              "fractal_dimension_se     569.0  3.794904e-03  2.646071e-03     0.000895   \n",
              "radius_worst             569.0  1.626919e+01  4.833242e+00     7.930000   \n",
              "texture_worst            569.0  2.567722e+01  6.146258e+00    12.020000   \n",
              "perimeter_worst          569.0  1.072612e+02  3.360254e+01    50.410000   \n",
              "area_worst               569.0  8.805831e+02  5.693570e+02   185.200000   \n",
              "smoothness_worst         569.0  1.323686e-01  2.283243e-02     0.071170   \n",
              "compactness_worst        569.0  2.542650e-01  1.573365e-01     0.027290   \n",
              "concavity_worst          569.0  2.721885e-01  2.086243e-01     0.000000   \n",
              "concave points_worst     569.0  1.146062e-01  6.573234e-02     0.000000   \n",
              "symmetry_worst           569.0  2.900756e-01  6.186747e-02     0.156500   \n",
              "fractal_dimension_worst  569.0  8.394582e-02  1.806127e-02     0.055040   \n",
              "\n",
              "                                   25%            50%           75%  \\\n",
              "id                       869218.000000  906024.000000  8.813129e+06   \n",
              "diagnosis                     0.000000       0.000000  1.000000e+00   \n",
              "radius_mean                  11.700000      13.370000  1.578000e+01   \n",
              "texture_mean                 16.170000      18.840000  2.180000e+01   \n",
              "perimeter_mean               75.170000      86.240000  1.041000e+02   \n",
              "area_mean                   420.300000     551.100000  7.827000e+02   \n",
              "smoothness_mean               0.086370       0.095870  1.053000e-01   \n",
              "compactness_mean              0.064920       0.092630  1.304000e-01   \n",
              "concavity_mean                0.029560       0.061540  1.307000e-01   \n",
              "concave points_mean           0.020310       0.033500  7.400000e-02   \n",
              "symmetry_mean                 0.161900       0.179200  1.957000e-01   \n",
              "fractal_dimension_mean        0.057700       0.061540  6.612000e-02   \n",
              "radius_se                     0.232400       0.324200  4.789000e-01   \n",
              "texture_se                    0.833900       1.108000  1.474000e+00   \n",
              "perimeter_se                  1.606000       2.287000  3.357000e+00   \n",
              "area_se                      17.850000      24.530000  4.519000e+01   \n",
              "smoothness_se                 0.005169       0.006380  8.146000e-03   \n",
              "compactness_se                0.013080       0.020450  3.245000e-02   \n",
              "concavity_se                  0.015090       0.025890  4.205000e-02   \n",
              "concave points_se             0.007638       0.010930  1.471000e-02   \n",
              "symmetry_se                   0.015160       0.018730  2.348000e-02   \n",
              "fractal_dimension_se          0.002248       0.003187  4.558000e-03   \n",
              "radius_worst                 13.010000      14.970000  1.879000e+01   \n",
              "texture_worst                21.080000      25.410000  2.972000e+01   \n",
              "perimeter_worst              84.110000      97.660000  1.254000e+02   \n",
              "area_worst                  515.300000     686.500000  1.084000e+03   \n",
              "smoothness_worst              0.116600       0.131300  1.460000e-01   \n",
              "compactness_worst             0.147200       0.211900  3.391000e-01   \n",
              "concavity_worst               0.114500       0.226700  3.829000e-01   \n",
              "concave points_worst          0.064930       0.099930  1.614000e-01   \n",
              "symmetry_worst                0.250400       0.282200  3.179000e-01   \n",
              "fractal_dimension_worst       0.071460       0.080040  9.208000e-02   \n",
              "\n",
              "                                  max  \n",
              "id                       9.113205e+08  \n",
              "diagnosis                1.000000e+00  \n",
              "radius_mean              2.811000e+01  \n",
              "texture_mean             3.928000e+01  \n",
              "perimeter_mean           1.885000e+02  \n",
              "area_mean                2.501000e+03  \n",
              "smoothness_mean          1.634000e-01  \n",
              "compactness_mean         3.454000e-01  \n",
              "concavity_mean           4.268000e-01  \n",
              "concave points_mean      2.012000e-01  \n",
              "symmetry_mean            3.040000e-01  \n",
              "fractal_dimension_mean   9.744000e-02  \n",
              "radius_se                2.873000e+00  \n",
              "texture_se               4.885000e+00  \n",
              "perimeter_se             2.198000e+01  \n",
              "area_se                  5.422000e+02  \n",
              "smoothness_se            3.113000e-02  \n",
              "compactness_se           1.354000e-01  \n",
              "concavity_se             3.960000e-01  \n",
              "concave points_se        5.279000e-02  \n",
              "symmetry_se              7.895000e-02  \n",
              "fractal_dimension_se     2.984000e-02  \n",
              "radius_worst             3.604000e+01  \n",
              "texture_worst            4.954000e+01  \n",
              "perimeter_worst          2.512000e+02  \n",
              "area_worst               4.254000e+03  \n",
              "smoothness_worst         2.226000e-01  \n",
              "compactness_worst        1.058000e+00  \n",
              "concavity_worst          1.252000e+00  \n",
              "concave points_worst     2.910000e-01  \n",
              "symmetry_worst           6.638000e-01  \n",
              "fractal_dimension_worst  2.075000e-01  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b801451a-cad4-4a35-aedc-43fdc686d70a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>569.0</td>\n",
              "      <td>3.037183e+07</td>\n",
              "      <td>1.250206e+08</td>\n",
              "      <td>8670.000000</td>\n",
              "      <td>869218.000000</td>\n",
              "      <td>906024.000000</td>\n",
              "      <td>8.813129e+06</td>\n",
              "      <td>9.113205e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diagnosis</th>\n",
              "      <td>569.0</td>\n",
              "      <td>3.725835e-01</td>\n",
              "      <td>4.839180e-01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.412729e+01</td>\n",
              "      <td>3.524049e+00</td>\n",
              "      <td>6.981000</td>\n",
              "      <td>11.700000</td>\n",
              "      <td>13.370000</td>\n",
              "      <td>1.578000e+01</td>\n",
              "      <td>2.811000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.928965e+01</td>\n",
              "      <td>4.301036e+00</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>2.180000e+01</td>\n",
              "      <td>3.928000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>9.196903e+01</td>\n",
              "      <td>2.429898e+01</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>1.041000e+02</td>\n",
              "      <td>1.885000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>6.548891e+02</td>\n",
              "      <td>3.519141e+02</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>7.827000e+02</td>\n",
              "      <td>2.501000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>9.636028e-02</td>\n",
              "      <td>1.406413e-02</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>1.053000e-01</td>\n",
              "      <td>1.634000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.043410e-01</td>\n",
              "      <td>5.281276e-02</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>1.304000e-01</td>\n",
              "      <td>3.454000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>8.879932e-02</td>\n",
              "      <td>7.971981e-02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>1.307000e-01</td>\n",
              "      <td>4.268000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>4.891915e-02</td>\n",
              "      <td>3.880284e-02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>7.400000e-02</td>\n",
              "      <td>2.012000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.811619e-01</td>\n",
              "      <td>2.741428e-02</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>1.957000e-01</td>\n",
              "      <td>3.040000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <td>569.0</td>\n",
              "      <td>6.279761e-02</td>\n",
              "      <td>7.060363e-03</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>6.612000e-02</td>\n",
              "      <td>9.744000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>4.051721e-01</td>\n",
              "      <td>2.773127e-01</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>4.789000e-01</td>\n",
              "      <td>2.873000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.216853e+00</td>\n",
              "      <td>5.516484e-01</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>1.474000e+00</td>\n",
              "      <td>4.885000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.866059e+00</td>\n",
              "      <td>2.021855e+00</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>3.357000e+00</td>\n",
              "      <td>2.198000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>4.033708e+01</td>\n",
              "      <td>4.549101e+01</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>4.519000e+01</td>\n",
              "      <td>5.422000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>7.040979e-03</td>\n",
              "      <td>3.002518e-03</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>8.146000e-03</td>\n",
              "      <td>3.113000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.547814e-02</td>\n",
              "      <td>1.790818e-02</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>3.245000e-02</td>\n",
              "      <td>1.354000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>3.189372e-02</td>\n",
              "      <td>3.018606e-02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>4.205000e-02</td>\n",
              "      <td>3.960000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.179614e-02</td>\n",
              "      <td>6.170285e-03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>1.471000e-02</td>\n",
              "      <td>5.279000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.054230e-02</td>\n",
              "      <td>8.266372e-03</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>2.348000e-02</td>\n",
              "      <td>7.895000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <td>569.0</td>\n",
              "      <td>3.794904e-03</td>\n",
              "      <td>2.646071e-03</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>4.558000e-03</td>\n",
              "      <td>2.984000e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.626919e+01</td>\n",
              "      <td>4.833242e+00</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>1.879000e+01</td>\n",
              "      <td>3.604000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.567722e+01</td>\n",
              "      <td>6.146258e+00</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>2.972000e+01</td>\n",
              "      <td>4.954000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.072612e+02</td>\n",
              "      <td>3.360254e+01</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>1.254000e+02</td>\n",
              "      <td>2.512000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>8.805831e+02</td>\n",
              "      <td>5.693570e+02</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>1.084000e+03</td>\n",
              "      <td>4.254000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.323686e-01</td>\n",
              "      <td>2.283243e-02</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>1.460000e-01</td>\n",
              "      <td>2.226000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.542650e-01</td>\n",
              "      <td>1.573365e-01</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>3.391000e-01</td>\n",
              "      <td>1.058000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.721885e-01</td>\n",
              "      <td>2.086243e-01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>3.829000e-01</td>\n",
              "      <td>1.252000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>1.146062e-01</td>\n",
              "      <td>6.573234e-02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>1.614000e-01</td>\n",
              "      <td>2.910000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>2.900756e-01</td>\n",
              "      <td>6.186747e-02</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>3.179000e-01</td>\n",
              "      <td>6.638000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <td>569.0</td>\n",
              "      <td>8.394582e-02</td>\n",
              "      <td>1.806127e-02</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>0.071460</td>\n",
              "      <td>0.080040</td>\n",
              "      <td>9.208000e-02</td>\n",
              "      <td>2.075000e-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b801451a-cad4-4a35-aedc-43fdc686d70a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b801451a-cad4-4a35-aedc-43fdc686d70a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b801451a-cad4-4a35-aedc-43fdc686d70a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the count of Malignant <M> and Benign <B> cells\n",
        "\n",
        "df['diagnosis'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBmNuQ5XdQYL",
        "outputId": "63ecf2b5-f2a7-4bc4-dfed-b550b57d6972"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "B    357\n",
              "M    212\n",
              "Name: diagnosis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.countplot(data=df, x='diagnosis',palette='husl')\n",
        "\n",
        "plt.title('Benign (0) vs Malignant (1) Tumors')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "HF3DybKXdY9O",
        "outputId": "7526b174-a991-47fe-baac-6ff8a376e6bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Benign (0) vs Malignant (1) Tumors')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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38OBB/f7776Uuhi1ZR+PGjS+5TS8vL3Xr1k1LlixRcXGxPvzwQ91+++2lApSLi4s6duyomTNnKiMjQ88++6y++OKLci/yLU+fPn20bt06NW7cuMy6pXNBbe3atXriiSf01FNP6e6771anTp3KvXPsUsLDw5WZmVnqj+Xl3CFXno8++kj16tXT0qVL1b9/f8XFxSk2Nvaqnhd0pUdWGjVqVOq9UhH/+9//VFhYeFnvl4spOVp1/Phxu/aSIzGVpeRncteuXaX6fv75Z9WuXVteXl6Xta769evrscce0+rVq/XTTz+psLBQL730UqXWi+uPsIMq68yZM1q9erXc3Nxsv5R79uyps2fP6umnny41vqioqNQv3Yq47bbb5O/vr7feesvuacULFy6s9HP9JdcvXBhqmjRpokaNGunNN9+0+y95zpw5slgsuvfee+3G5+bmKjMzU//3f/93Wdvt1auXDh48qLfffls//PCD3Sks6dwppQuVBJWyTq1dzIMPPqgpU6Zc9A9KyX/2F4aTWbNmXdG2SsTFxel///uf3VOl8/Pz9dZbb1VofeXVuGXLFtvpwIoo+QN9ue/bmJgY/fTTT1f8GlwoPT1dki77/VKekrvs1q9fb2s7deqUFixYcFXrvVBwcLCaN2+uBQsW2H2vfvrpJ61evVpdu3a95DpOnz5dKpjWr19fNWvWvOrvJxyPW89RZaxYsUI///yzJCknJ0cpKSnavXu3nnjiCfn4+EiS/v73v2vYsGFKTk7Wtm3b1LlzZ1WvXl27d+/WkiVL9Morr5QKAlfKzc1NU6dO1ahRo9ShQwf17NlTv/32m+bPn1/mdSBXo169emratKnWrFmjBx54wK5vxowZuvPOO9W5c2f17t1bP/30k15//XU9+OCDpf4jX7NmjQzDUI8ePS5ru127dlXNmjX1+OOPy9XVVYmJiXb906ZN0/r165WQkKDw8HDl5OTojTfeUJ06dWzPLLlc4eHhl3xCsI+Pj+0aijNnzujGG2/U6tWrK3wUY9iwYXr99dfVp08fPfroowoODrY9uVm68iMq0rlnFC1dulR33323EhIStHfvXs2dO1cRERG2I49XquQW8EceeURxcXFydXVV7969yx3fo0cPPf3000pLS1Pnzp3t+l5//XUdP35cBw8elCQtW7ZMv//+uyRp1KhRdk9cTk1NVVhY2FXfdt65c2eFhYVpyJAhGjdunFxdXfXuu+8qICBA+/fvv6p1X2jGjBmKj49XTEyMhgwZYrv13NfX97KeQP3LL7+oY8eO6tmzpyIiIlStWjV98sknOnTo0EW/56giHHIPGHAFyrr13MPDw2jevLkxZ86cMp/y+uabbxrR0dGGp6enUbNmTSMyMtIYP368cfDgQduYklugL3ThbcoX3npe4tVXXzXCw8MNd3d3o2XLlsaGDRuM6Ohoo0uXLqWWXbJkid2yJbcfX87TZmfOnGl4e3uXeev1J598YjRv3txwd3c36tSpY0yaNMkoLCwsNa5Xr15G27ZtL7mt8/Xr18+QZMTGxpbqW7t2rdGjRw8jJCTEcHNzM0JCQow+ffoYv/zyyyXXW973/Xxl3Xr++++/G3fffbdRq1Ytw9fX17jvvvuMgwcPGpKMKVOmlFr2YreeG4Zh/Prrr0ZCQoLh6elpBAQEGI899pjx8ccfG5KMzZs32y3bpEmTUjUOHDjQCA8Pt80XFxcbzz33nO09ccsttxjLly8vNa7ktS/rFucL96WoqMgYNWqUERAQYFgslsu6DT0qKsoYMmRIqfaSW7vLms7/Xp09e9YIDg42Jk2adMltna+8W9rT09ONVq1aGW5ubkZYWJgxc+bMcm89L+t9IclISkqyayvve7hmzRqjTZs2hqenp+Hj42N0797dyMjIsBtTcuv5hY9NOHz4sJGUlGQ0atTI8PLyMnx9fY1WrVoZixcvvqLvA5yTxTAq+UpM4C+quLhYAQEBuueee67qdMiFcnNzVa9ePU2fPl1Dhgy54uWzs7N10003adGiRZd9ZOevatasWRozZox+//133XjjjY4up0L+/e9/KykpSfv377c9IuBKfPrpp+rbt68yMzNtF8IDVR3X7AAVkJ+fX+r6kffee09Hjx4t9bEEV8vX11fjx4/XjBkzKnTX0axZsxQZGUnQucCFzyLKz8/Xv/71L918881VNuhI5573FBYWZvuoiSv1wgsvaOTIkQQdmApHdoAKWLduncaMGaP77rtP/v7++u677/TOO++ocePGSk9Pd9gnpOPyxcfHKywsTM2bN1dubq7ef/997dixQwsXLrzoLfAAqh4uUAYqoG7dugoNDdWrr76qo0ePys/PTwMGDNDzzz9P0Kki4uLi9Pbbb2vhwoU6e/asIiIitGjRolJ3ngGo+jiyAwAATI1rdgAAgKkRdgAAgKlxzY7O3TJ88OBB1axZ87p/8B0AAKgYwzB04sQJhYSEyMWl/OM3hB2d+zyhCz9VFwAAVA0HDhxQnTp1yu0n7EiqWbOmpHPfrJKPHQAAAM4tLy9PoaGhtr/j5SHs6P9/Do6Pjw9hBwCAKuZSl6BwgTIAADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADC1ao4uAACqus7z5jq6BMAprR483NElSOLIDgAAMDmHhp05c+YoKipKPj4+8vHxUUxMjFasWGHrb9++vSwWi900fLh9Sty/f78SEhJUo0YNBQYGaty4cSoqKrreuwIAAJyUQ09j1alTR88//7xuvvlmGYahBQsWqEePHvr+++/VpEkTSdLQoUM1bdo02zI1atSwfX327FklJCTIarVq48aNysrK0oABA1S9enU999xz131/AACA83Fo2Onevbvd/LPPPqs5c+Zo8+bNtrBTo0YNWa3WMpdfvXq1MjIytGbNGgUFBal58+Z6+umnNWHCBE2dOlVubm7XfB8AAIBzc5prds6ePatFixbp1KlTiomJsbUvXLhQtWvXVtOmTTVx4kSdPn3a1rdp0yZFRkYqKCjI1hYXF6e8vDzt2LHjutYPAACck8Pvxvrxxx8VExOj/Px8eXt765NPPlFERIQkqW/fvgoPD1dISIi2b9+uCRMmaNeuXVq6dKkkKTs72y7oSLLNZ2dnl7vNgoICFRQU2Obz8vIqe7cAAICTcHjYadiwobZt26bc3Fx99NFHGjhwoNLS0hQREaGHHnrINi4yMlLBwcHq2LGjMjMzVb9+/QpvMzk5WU899VRllA8AAJycw09jubm5qUGDBoqOjlZycrKaNWumV155pcyxrVq1kiTt2bNHkmS1WnXo0CG7MSXz5V3nI0kTJ05Ubm6ubTpw4EBl7AoAAHBCDg87FyouLrY7xXS+bdu2SZKCg4MlSTExMfrxxx+Vk5NjG5OamiofHx/bqbCyuLu72253L5kAAIA5OfQ01sSJExUfH6+wsDCdOHFCKSkpWrdunVatWqXMzEylpKSoa9eu8vf31/bt2zVmzBi1a9dOUVFRkqTOnTsrIiJC/fv31/Tp05Wdna1JkyYpKSlJ7u7ujtw1AADgJBwadnJycjRgwABlZWXJ19dXUVFRWrVqlTp16qQDBw5ozZo1mjVrlk6dOqXQ0FAlJiZq0qRJtuVdXV21fPlyjRgxQjExMfLy8tLAgQPtnssDAAD+2hwadt55551y+0JDQ5WWlnbJdYSHh+vzzz+vzLIAAICJON01OwAAAJWJsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEzNoWFnzpw5ioqKko+Pj3x8fBQTE6MVK1bY+vPz85WUlCR/f395e3srMTFRhw4dslvH/v37lZCQoBo1aigwMFDjxo1TUVHR9d4VAADgpBwadurUqaPnn39e6enp+vbbb9WhQwf16NFDO3bskCSNGTNGy5Yt05IlS5SWlqaDBw/qnnvusS1/9uxZJSQkqLCwUBs3btSCBQs0f/58TZ482VG7BAAAnIzFMAzD0UWcz8/PTzNmzNC9996rgIAApaSk6N5775Uk/fzzz2rcuLE2bdqk1q1ba8WKFerWrZsOHjyooKAgSdLcuXM1YcIE/fHHH3Jzc7usbebl5cnX11e5ubny8fG5ZvsGwJw6z5vr6BIAp7R68PBruv7L/fvtNNfsnD17VosWLdKpU6cUExOj9PR0nTlzRrGxsbYxjRo1UlhYmDZt2iRJ2rRpkyIjI21BR5Li4uKUl5dnOzpUloKCAuXl5dlNAADAnBwedn788Ud5e3vL3d1dw4cP1yeffKKIiAhlZ2fLzc1NtWrVshsfFBSk7OxsSVJ2drZd0CnpL+krT3Jysnx9fW1TaGho5e4UAABwGg4POw0bNtS2bdu0ZcsWjRgxQgMHDlRGRsY13ebEiROVm5trmw4cOHBNtwcAABynmqMLcHNzU4MGDSRJ0dHR2rp1q1555RX16tVLhYWFOn78uN3RnUOHDslqtUqSrFarvvnmG7v1ldytVTKmLO7u7nJ3d6/kPQEAAM7I4Ud2LlRcXKyCggJFR0erevXqWrt2ra1v165d2r9/v2JiYiRJMTEx+vHHH5WTk2Mbk5qaKh8fH0VERFz32gEAgPNx6JGdiRMnKj4+XmFhYTpx4oRSUlK0bt06rVq1Sr6+vhoyZIjGjh0rPz8/+fj4aNSoUYqJiVHr1q0lSZ07d1ZERIT69++v6dOnKzs7W5MmTVJSUhJHbgAAgCQHh52cnBwNGDBAWVlZ8vX1VVRUlFatWqVOnTpJkl5++WW5uLgoMTFRBQUFiouL0xtvvGFb3tXVVcuXL9eIESMUExMjLy8vDRw4UNOmTXPULgEAACfjdM/ZcQSeswPgavCcHaBsPGcHAADgOiDsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAU3No2ElOTlaLFi1Us2ZNBQYG6q677tKuXbvsxrRv314Wi8VuGj58uN2Y/fv3KyEhQTVq1FBgYKDGjRunoqKi67krAADASVVz5MbT0tKUlJSkFi1aqKioSP/4xz/UuXNnZWRkyMvLyzZu6NChmjZtmm2+Ro0atq/Pnj2rhIQEWa1Wbdy4UVlZWRowYICqV6+u55577rruDwAAcD4ODTsrV660m58/f74CAwOVnp6udu3a2dpr1Kghq9Va5jpWr16tjIwMrVmzRkFBQWrevLmefvppTZgwQVOnTpWbm9s13QcAAODcnOqandzcXEmSn5+fXfvChQtVu3ZtNW3aVBMnTtTp06dtfZs2bVJkZKSCgoJsbXFxccrLy9OOHTvK3E5BQYHy8vLsJgAAYE4OPbJzvuLiYo0ePVpt2rRR06ZNbe19+/ZVeHi4QkJCtH37dk2YMEG7du3S0qVLJUnZ2dl2QUeSbT47O7vMbSUnJ+upp566RnsCAACcidOEnaSkJP3000/6+uuv7dofeugh29eRkZEKDg5Wx44dlZmZqfr161doWxMnTtTYsWNt83l5eQoNDa1Y4QAAwKk5xWmskSNHavny5fryyy9Vp06di45t1aqVJGnPnj2SJKvVqkOHDtmNKZkv7zofd3d3+fj42E0AAMCcHBp2DMPQyJEj9cknn+iLL77QTTfddMlltm3bJkkKDg6WJMXExOjHH39UTk6ObUxqaqp8fHwUERFxTeoGAABVh0NPYyUlJSklJUWfffaZatasabvGxtfXV56ensrMzFRKSoq6du0qf39/bd++XWPGjFG7du0UFRUlSercubMiIiLUv39/TZ8+XdnZ2Zo0aZKSkpLk7u7uyN0DAABOwKFHdubMmaPc3Fy1b99ewcHBtunDDz+UJLm5uWnNmjXq3LmzGjVqpMcee0yJiYlatmyZbR2urq5avny5XF1dFRMTo/vvv18DBgywey4PAAD463LokR3DMC7aHxoaqrS0tEuuJzw8XJ9//nlllQUAAEzEKS5QBgAAuFYIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNQIOwAAwNSqObqAv4qsaa87ugTAKQVPHunoEgCYHEd2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqVUo7HTo0EHHjx8v1Z6Xl6cOHTpc9nqSk5PVokUL1axZU4GBgbrrrru0a9cuuzH5+flKSkqSv7+/vL29lZiYqEOHDtmN2b9/vxISElSjRg0FBgZq3LhxKioqqsiuAQAAk6lQ2Fm3bp0KCwtLtefn5+urr7667PWkpaUpKSlJmzdvVmpqqs6cOaPOnTvr1KlTtjFjxozRsmXLtGTJEqWlpengwYO65557bP1nz55VQkKCCgsLtXHjRi1YsEDz58/X5MmTK7JrAADAZK7ooYLbt2+3fZ2RkaHs7Gzb/NmzZ7Vy5UrdeOONl72+lStX2s3Pnz9fgYGBSk9PV7t27ZSbm6t33nlHKSkptiNG8+bNU+PGjbV582a1bt1aq1evVkZGhtasWaOgoCA1b95cTz/9tCZMmKCpU6fKzc3tSnYRAACYzBWFnebNm8tischisZR5usrT01OvvfZahYvJzc2VJPn5+UmS0tPTdebMGcXGxtrGNGrUSGFhYdq0aZNat26tTZs2KTIyUkFBQbYxcXFxGjFihHbs2KFbbrmlwvUAAICq74rCzt69e2UYhurVq6dvvvlGAQEBtj43NzcFBgbK1dW1QoUUFxdr9OjRatOmjZo2bSpJys7Olpubm2rVqmU3NigoyHZUKTs72y7olPSX9JWloKBABQUFtvm8vLwK1QwAAJzfFYWd8PBwSeeCSWVLSkrSTz/9pK+//rrS132h5ORkPfXUU9d8OwAAwPEq/EGgu3fv1pdffqmcnJxS4edKLw4eOXKkli9frvXr16tOnTq2dqvVqsLCQh0/ftzu6M6hQ4dktVptY7755hu79ZXcrVUy5kITJ07U2LFjbfN5eXkKDQ29opoBAEDVUKGw89Zbb2nEiBGqXbu2rFarLBaLrc9isVx22DEMQ6NGjdInn3yidevW6aabbrLrj46OVvXq1bV27VolJiZKknbt2qX9+/crJiZGkhQTE6Nnn31WOTk5CgwMlCSlpqbKx8dHERERZW7X3d1d7u7uV7zfAACg6qlQ2HnmmWf07LPPasKECVe18aSkJKWkpOizzz5TzZo1bdfY+Pr6ytPTU76+vhoyZIjGjh0rPz8/+fj4aNSoUYqJiVHr1q0lSZ07d1ZERIT69++v6dOnKzs7W5MmTVJSUhKBBgAAVCzsHDt2TPfdd99Vb3zOnDmSpPbt29u1z5s3T4MGDZIkvfzyy3JxcVFiYqIKCgoUFxenN954wzbW1dVVy5cv14gRIxQTEyMvLy8NHDhQ06ZNu+r6AABA1VehsHPfffdp9erVGj58+FVt3DCMS47x8PDQ7NmzNXv27HLHhIeH6/PPP7+qWgAAgDlVKOw0aNBATz75pDZv3qzIyEhVr17drv+RRx6plOIAAACuVoXCzptvvilvb2+lpaUpLS3Nrs9isRB2AACA06hQ2Nm7d29l1wEAAHBNVOiDQAEAAKqKCh3ZeeCBBy7a/+6771aoGAAAgMpW4VvPz3fmzBn99NNPOn78eJkfEAoAAOAoFQo7n3zySam24uJijRgxQvXr17/qogAAACpLpV2z4+LiorFjx+rll1+urFUCAABctUq9QDkzM1NFRUWVuUoAAICrUqHTWOd/Yrh07knIWVlZ+u9//6uBAwdWSmEAAACVoUJh5/vvv7ebd3FxUUBAgF566aVL3qkFAABwPVUo7Hz55ZeVXQcAAMA1UaGwU+KPP/7Qrl27JEkNGzZUQEBApRQFAABQWSp0gfKpU6f0wAMPKDg4WO3atVO7du0UEhKiIUOG6PTp05VdIwAAQIVVKOyMHTtWaWlpWrZsmY4fP67jx4/rs88+U1pamh577LHKrhEAAKDCKnQa6+OPP9ZHH32k9u3b29q6du0qT09P9ezZU3PmzKms+gAAAK5KhY7snD59WkFBQaXaAwMDOY0FAACcSoXCTkxMjKZMmaL8/Hxb259//qmnnnpKMTExlVYcAADA1arQaaxZs2apS5cuqlOnjpo1ayZJ+uGHH+Tu7q7Vq1dXaoEAAABXo0JhJzIyUrt379bChQv1888/S5L69Omjfv36ydPTs1ILBAAAuBoVCjvJyckKCgrS0KFD7drfffdd/fHHH5owYUKlFAcAAHC1KnTNzr/+9S81atSoVHuTJk00d+7cqy4KAACgslQo7GRnZys4OLhUe0BAgLKysq66KAAAgMpSobATGhqqDRs2lGrfsGGDQkJCrrooAACAylKha3aGDh2q0aNH68yZM+rQoYMkae3atRo/fjxPUAYAAE6lQmFn3LhxOnLkiB5++GEVFhZKkjw8PDRhwgRNnDixUgsEAAC4GhUKOxaLRS+88IKefPJJ7dy5U56enrr55pvl7u5e2fUBAABclQqFnRLe3t5q0aJFZdUCAABQ6Sp0gTIAAEBVQdgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACm5tCws379enXv3l0hISGyWCz69NNP7foHDRoki8ViN3Xp0sVuzNGjR9WvXz/5+PioVq1aGjJkiE6ePHkd9wIAADgzh4adU6dOqVmzZpo9e3a5Y7p06aKsrCzb9MEHH9j19+vXTzt27FBqaqqWL1+u9evX66GHHrrWpQMAgCriqj71/GrFx8crPj7+omPc3d1ltVrL7Nu5c6dWrlyprVu36rbbbpMkvfbaa+ratatefPFFhYSEVHrNAACganH6a3bWrVunwMBANWzYUCNGjNCRI0dsfZs2bVKtWrVsQUeSYmNj5eLioi1btpS7zoKCAuXl5dlNAADAnJw67HTp0kXvvfee1q5dqxdeeEFpaWmKj4/X2bNnJUnZ2dkKDAy0W6ZatWry8/NTdnZ2uetNTk6Wr6+vbQoNDb2m+wEAABzHoaexLqV37962ryMjIxUVFaX69etr3bp16tixY4XXO3HiRI0dO9Y2n5eXR+ABAMCknPrIzoXq1aun2rVra8+ePZIkq9WqnJwcuzFFRUU6evRoudf5SOeuA/Lx8bGbAACAOVWpsPP777/ryJEjCg4OliTFxMTo+PHjSk9Pt4354osvVFxcrFatWjmqTAAA4EQcehrr5MmTtqM0krR3715t27ZNfn5+8vPz01NPPaXExERZrVZlZmZq/PjxatCggeLi4iRJjRs3VpcuXTR06FDNnTtXZ86c0ciRI9W7d2/uxAIAAJIcfGTn22+/1S233KJbbrlFkjR27Fjdcsstmjx5slxdXbV9+3bdeeed+tvf/qYhQ4YoOjpaX331ldzd3W3rWLhwoRo1aqSOHTuqa9euatu2rd58801H7RIAAHAyDj2y0759exmGUW7/qlWrLrkOPz8/paSkVGZZAADARKrUNTsAAABXirADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMzaFhZ/369erevbtCQkJksVj06aef2vUbhqHJkycrODhYnp6eio2N1e7du+3GHD16VP369ZOPj49q1aqlIUOG6OTJk9dxLwAAgDNzaNg5deqUmjVrptmzZ5fZP336dL366quaO3eutmzZIi8vL8XFxSk/P982pl+/ftqxY4dSU1O1fPlyrV+/Xg899ND12gUAAODkqjly4/Hx8YqPjy+zzzAMzZo1S5MmTVKPHj0kSe+9956CgoL06aefqnfv3tq5c6dWrlyprVu36rbbbpMkvfbaa+ratatefPFFhYSEXLd9AQAAzslpr9nZu3evsrOzFRsba2vz9fVVq1attGnTJknSpk2bVKtWLVvQkaTY2Fi5uLhoy5Yt5a67oKBAeXl5dhMAADAnpw072dnZkqSgoCC79qCgIFtfdna2AgMD7fqrVasmPz8/25iyJCcny9fX1zaFhoZWcvUAAMBZOG3YuZYmTpyo3Nxc23TgwAFHlwQAAK4Rpw07VqtVknTo0CG79kOHDtn6rFarcnJy7PqLiop09OhR25iyuLu7y8fHx24CAADm5LRh56abbpLVatXatWttbXl5edqyZYtiYmIkSTExMTp+/LjS09NtY7744gsVFxerVatW171mAADgfBx6N9bJkye1Z88e2/zevXu1bds2+fn5KSwsTKNHj9Yzzzyjm2++WTfddJOefPJJhYSE6K677pIkNW7cWF26dNHQoUM1d+5cnTlzRiNHjlTv3r25EwsAAEhycNj59ttvdccdd9jmx44dK0kaOHCg5s+fr/Hjx+vUqVN66KGHdPz4cbVt21YrV66Uh4eHbZmFCxdq5MiR6tixo1xcXJSYmKhXX331uu8LAABwTg4NO+3bt5dhGOX2WywWTZs2TdOmTSt3jJ+fn1JSUq5FeQAAwASc9podAACAykDYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApubUYWfq1KmyWCx2U6NGjWz9+fn5SkpKkr+/v7y9vZWYmKhDhw45sGIAAOBsnDrsSFKTJk2UlZVlm77++mtb35gxY7Rs2TItWbJEaWlpOnjwoO655x4HVgsAAJxNNUcXcCnVqlWT1Wot1Z6bm6t33nlHKSkp6tChgyRp3rx5aty4sTZv3qzWrVtf71IBAIATcvojO7t371ZISIjq1aunfv36af/+/ZKk9PR0nTlzRrGxsbaxjRo1UlhYmDZt2nTRdRYUFCgvL89uAgAA5uTUYadVq1aaP3++Vq5cqTlz5mjv3r26/fbbdeLECWVnZ8vNzU21atWyWyYoKEjZ2dkXXW9ycrJ8fX1tU2ho6DXcCwAA4EhOfRorPj7e9nVUVJRatWql8PBwLV68WJ6enhVe78SJEzV27FjbfF5eHoEHAACTcuojOxeqVauW/va3v2nPnj2yWq0qLCzU8ePH7cYcOnSozGt8zufu7i4fHx+7CQAAmFOVCjsnT55UZmamgoODFR0drerVq2vt2rW2/l27dmn//v2KiYlxYJUAAMCZOPVprMcff1zdu3dXeHi4Dh48qClTpsjV1VV9+vSRr6+vhgwZorFjx8rPz08+Pj4aNWqUYmJiuBMLAADYOHXY+f3339WnTx8dOXJEAQEBatu2rTZv3qyAgABJ0ssvvywXFxclJiaqoKBAcXFxeuONNxxcNQAAcCZOHXYWLVp00X4PDw/Nnj1bs2fPvk4VAQCAqqZKXbMDAABwpQg7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1EwTdmbPnq26devKw8NDrVq10jfffOPokgAAgBMwRdj58MMPNXbsWE2ZMkXfffedmjVrpri4OOXk5Di6NAAA4GCmCDszZ87U0KFDNXjwYEVERGju3LmqUaOG3n33XUeXBgAAHKzKh53CwkKlp6crNjbW1ubi4qLY2Fht2rTJgZUBAABnUM3RBVytw4cP6+zZswoKCrJrDwoK0s8//1zmMgUFBSooKLDN5+bmSpLy8vKuWZ0n8v+8ZusGqjKva/hzd70U/cnPN1CWa/l39fz1G4Zx0XFVPuxURHJysp566qlS7aGhoQ6oBviLSx7v6AoAXCO+SWOvy3ZOnDghX1/fcvurfNipXbu2XF1ddejQIbv2Q4cOyWq1lrnMxIkTNXbs/38BiouLdfToUfn7+8tisVzTeuF4eXl5Cg0N1YEDB+Tj4+PocgBUIn6+/1oMw9CJEycUEhJy0XFVPuy4ubkpOjpaa9eu1V133SXpXHhZu3atRo4cWeYy7u7ucnd3t2urVavWNa4UzsbHx4dfhoBJ8fP913GxIzolqnzYkaSxY8dq4MCBuu2229SyZUvNmjVLp06d0uDBgx1dGgAAcDBThJ1evXrpjz/+0OTJk5Wdna3mzZtr5cqVpS5aBgAAfz2mCDuSNHLkyHJPWwHnc3d315QpU0qdygRQ9fHzjbJYjEvdrwUAAFCFVfmHCgIAAFwMYQcAAJgaYQcAAJgaYQcAAJgaYQemN2jQIFksFg0fPrxUX1JSkiwWiwYNGnT9CwNQKUp+xksmf39/denSRdu3b3d0aXAShB38JYSGhmrRokX687wPbMzPz1dKSorCwsIcWBmAytClSxdlZWUpKytLa9euVbVq1dStWzdHlwUnQdjBX8Ktt96q0NBQLV261Na2dOlShYWF6ZZbbnFgZQAqg7u7u6xWq6xWq5o3b64nnnhCBw4c0B9//OHo0uAECDv4y3jggQc0b9482/y7777LR4oAJnTy5Em9//77atCggfz9/R1dDpwAYQd/Gffff7++/vpr7du3T/v27dOGDRt0//33O7osAJVg+fLl8vb2lre3t2rWrKn//Oc/+vDDD+Xiwp85mOjjIoBLCQgIUEJCgubPny/DMJSQkKDatWs7uiwAleCOO+7QnDlzJEnHjh3TG2+8ofj4eH3zzTcKDw93cHVwNMIO/lIeeOAB22eozZ4928HVAKgsXl5eatCggW3+7bfflq+vr9566y0988wzDqwMzoCwg7+ULl26qLCwUBaLRXFxcY4uB8A1YrFY5OLiYncHJv66CDv4S3F1ddXOnTttXwMwh4KCAmVnZ0s6dxrr9ddf18mTJ9W9e3cHVwZnQNjBX46Pj4+jSwBQyVauXKng4GBJUs2aNdWoUSMtWbJE7du3d2xhcAoWwzAMRxcBAABwrXBPHgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgAAMDXCDgCHad++vUaPHi1Jqlu3rmbNmuXQeq7Ub7/9JovFom3btjm6FAAXwROUATiFrVu3ysvLy9FlXJHQ0FBlZWWpdu3aji4FwEUQdgA4hYCAAEeXcMVcXV1ltVodXQaAS+A0FoDr4tSpUxowYIC8vb0VHBysl156ya7/wtNYM2fOVGRkpLy8vBQaGqqHH35YJ0+etFvmrbfeUmhoqGrUqKG7775bM2fOVK1atWz9U6dOVfPmzfXvf/9bdevWla+vr3r37q0TJ07YxhQUFOiRRx5RYGCgPDw81LZtW23dutXWf+zYMfXr108BAQHy9PTUzTffrHnz5kkqfRrrYmMBOA5hB8B1MW7cOKWlpemzzz7T6tWrtW7dOn333XfljndxcdGrr76qHTt2aMGCBfriiy80fvx4W/+GDRs0fPhwPfroo9q2bZs6deqkZ599ttR6MjMz9emnn2r58uVavny50tLS9Pzzz9v6x48fr48//lgLFizQd999pwYNGiguLk5Hjx6VJD355JPKyMjQihUrtHPnTs2ZM6fc01ZXMhbAdWQAwDV24sQJw83NzVi8eLGt7ciRI4anp6fx6KOPGoZhGOHh4cbLL79c7jqWLFli+Pv72+Z79eplJCQk2I3p16+f4evra5ufMmWKUaNGDSMvL8/WNm7cOKNVq1aGYRjGyZMnjerVqxsLFy609RcWFhohISHG9OnTDcMwjO7duxuDBw8us6a9e/cakozvv//+kmMBOA5HdgBcc5mZmSosLFSrVq1sbX5+fmrYsGG5y6xZs0YdO3bUjTfeqJo1a6p///46cuSITp8+LUnatWuXWrZsabfMhfPSudNjNWvWtM0HBwcrJyfHVteZM2fUpk0bW3/16tXVsmVL7dy5U5I0YsQILVq0SM2bN9f48eO1cePGcmu+krEArh/CDgCn89tvv6lbt26KiorSxx9/rPT0dM2ePVuSVFhYeEXrql69ut28xWJRcXHxZS8fHx+vffv2acyYMTp48KA6duyoxx9//KrHArh+CDsArrn69eurevXq2rJli63t2LFj+uWXX8ocn56eruLiYr300ktq3bq1/va3v+ngwYN2Yxo2bGh3IbGkUvOXU5ebm5s2bNhgaztz5oy2bt2qiIgIW1tAQIAGDhyo999/X7NmzdKbb75Z7jqvZCyA64NbzwFcc97e3hoyZIjGjRsnf39/BQYG6p///KdcXMr+f6tBgwY6c+aMXnvtNXXv3l0bNmzQ3Llz7caMGjVK7dq108yZM9W9e3d98cUXWrFihSwWy2XX5eXlpREjRmjcuHHy8/NTWFiYpk+frtOnT2vIkCGSpMmTJys6OlpNmjRRQUGBli9frsaNG5e5visZC+D64cgOgOtixowZuv3229W9e3fFxsaqbdu2io6OLnNss2bNNHPmTL3wwgtq2rSpFi5cqOTkZLsxbdq00dy5czVz5kw1a9ZMK1eu1JgxY+Th4XFFdT3//PNKTExU//79deutt2rPnj1atWqVbrjhBkmSm5ubJk6cqKioKLVr106urq5atGhRmeu6krEArh+LYRiGo4sAgMowdOhQ/fzzz/rqq68cXQoAJ8JpLABV1osvvqhOnTrJy8tLK1as0IIFC/TGG284uiwAToYjOwCqrJ49e2rdunU6ceKE6tWrp1GjRmn48OGOLguAkyHsAAAAU+MCZQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGr/D1syKDlLfmOfAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Perform One - Hot Encoding on the Categorical Data Values and visualize the  set*"
      ],
      "metadata": {
        "id": "eXTNBvQ_p_Oe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# label encoding(convert the value of M and B into 1 and 0)\n",
        "# One Hot Encoding of the Categorical Data Values\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder_Y = LabelEncoder()\n",
        "df.iloc[:,1]=labelencoder_Y.fit_transform(df.iloc[:,1].values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvR_XbuydnJC",
        "outputId": "6f1302d0-5461-441a-fdc8-ce86fbbc2f6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-4875759c0308>:5: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
            "  df.iloc[:,1]=labelencoder_Y.fit_transform(df.iloc[:,1].values)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
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        "id": "gGtGduZxdt66",
        "outputId": "25a32135-6a37-4e7c-dcb3-c8ad0a91853f"
      },
      "execution_count": null,
      "outputs": [
        {
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              "         id  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
              "0    842302          1        17.99         10.38          122.80     1001.0   \n",
              "1    842517          1        20.57         17.77          132.90     1326.0   \n",
              "2  84300903          1        19.69         21.25          130.00     1203.0   \n",
              "3  84348301          1        11.42         20.38           77.58      386.1   \n",
              "4  84358402          1        20.29         14.34          135.10     1297.0   \n",
              "\n",
              "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
              "0          0.11840           0.27760          0.3001              0.14710   \n",
              "1          0.08474           0.07864          0.0869              0.07017   \n",
              "2          0.10960           0.15990          0.1974              0.12790   \n",
              "3          0.14250           0.28390          0.2414              0.10520   \n",
              "4          0.10030           0.13280          0.1980              0.10430   \n",
              "\n",
              "   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n",
              "0  ...         25.38          17.33           184.60      2019.0   \n",
              "1  ...         24.99          23.41           158.80      1956.0   \n",
              "2  ...         23.57          25.53           152.50      1709.0   \n",
              "3  ...         14.91          26.50            98.87       567.7   \n",
              "4  ...         22.54          16.67           152.20      1575.0   \n",
              "\n",
              "   smoothness_worst  compactness_worst  concavity_worst  concave points_worst  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   symmetry_worst  fractal_dimension_worst  \n",
              "0          0.4601                  0.11890  \n",
              "1          0.2750                  0.08902  \n",
              "2          0.3613                  0.08758  \n",
              "3          0.6638                  0.17300  \n",
              "4          0.2364                  0.07678  \n",
              "\n",
              "[5 rows x 32 columns]"
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
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              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
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              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
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              "      <th>0</th>\n",
              "      <td>842302</td>\n",
              "      <td>1</td>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>...</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>842517</td>\n",
              "      <td>1</td>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>...</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>84300903</td>\n",
              "      <td>1</td>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>...</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>...</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>1</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
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              "      <td>0.13280</td>\n",
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              "<p>5 rows × 32 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1aa9a43-f0c5-4536-a7a4-d2bbdd9450a8')\"\n",
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              "    .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-b1aa9a43-f0c5-4536-a7a4-d2bbdd9450a8');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "sns.pairplot(df.iloc[:,1:5],hue=\"diagnosis\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
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          "height": 776
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        "id": "w5UcLt8Edyzq",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7efeb59b9960>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 826.736x750 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the correlation of the columns\n",
        "\n",
        "df.iloc[:,1:32].corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GOnDaDKld2Fl",
        "outputId": "c7caa1b4-2b41-4115-a4f5-340bf7cd6bf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
              "diagnosis                 1.000000     0.730029      0.415185        0.742636   \n",
              "radius_mean               0.730029     1.000000      0.323782        0.997855   \n",
              "texture_mean              0.415185     0.323782      1.000000        0.329533   \n",
              "perimeter_mean            0.742636     0.997855      0.329533        1.000000   \n",
              "area_mean                 0.708984     0.987357      0.321086        0.986507   \n",
              "smoothness_mean           0.358560     0.170581     -0.023389        0.207278   \n",
              "compactness_mean          0.596534     0.506124      0.236702        0.556936   \n",
              "concavity_mean            0.696360     0.676764      0.302418        0.716136   \n",
              "concave points_mean       0.776614     0.822529      0.293464        0.850977   \n",
              "symmetry_mean             0.330499     0.147741      0.071401        0.183027   \n",
              "fractal_dimension_mean   -0.012838    -0.311631     -0.076437       -0.261477   \n",
              "radius_se                 0.567134     0.679090      0.275869        0.691765   \n",
              "texture_se               -0.008303    -0.097317      0.386358       -0.086761   \n",
              "perimeter_se              0.556141     0.674172      0.281673        0.693135   \n",
              "area_se                   0.548236     0.735864      0.259845        0.744983   \n",
              "smoothness_se            -0.067016    -0.222600      0.006614       -0.202694   \n",
              "compactness_se            0.292999     0.206000      0.191975        0.250744   \n",
              "concavity_se              0.253730     0.194204      0.143293        0.228082   \n",
              "concave points_se         0.408042     0.376169      0.163851        0.407217   \n",
              "symmetry_se              -0.006522    -0.104321      0.009127       -0.081629   \n",
              "fractal_dimension_se      0.077972    -0.042641      0.054458       -0.005523   \n",
              "radius_worst              0.776454     0.969539      0.352573        0.969476   \n",
              "texture_worst             0.456903     0.297008      0.912045        0.303038   \n",
              "perimeter_worst           0.782914     0.965137      0.358040        0.970387   \n",
              "area_worst                0.733825     0.941082      0.343546        0.941550   \n",
              "smoothness_worst          0.421465     0.119616      0.077503        0.150549   \n",
              "compactness_worst         0.590998     0.413463      0.277830        0.455774   \n",
              "concavity_worst           0.659610     0.526911      0.301025        0.563879   \n",
              "concave points_worst      0.793566     0.744214      0.295316        0.771241   \n",
              "symmetry_worst            0.416294     0.163953      0.105008        0.189115   \n",
              "fractal_dimension_worst   0.323872     0.007066      0.119205        0.051019   \n",
              "\n",
              "                         area_mean  smoothness_mean  compactness_mean  \\\n",
              "diagnosis                 0.708984         0.358560          0.596534   \n",
              "radius_mean               0.987357         0.170581          0.506124   \n",
              "texture_mean              0.321086        -0.023389          0.236702   \n",
              "perimeter_mean            0.986507         0.207278          0.556936   \n",
              "area_mean                 1.000000         0.177028          0.498502   \n",
              "smoothness_mean           0.177028         1.000000          0.659123   \n",
              "compactness_mean          0.498502         0.659123          1.000000   \n",
              "concavity_mean            0.685983         0.521984          0.883121   \n",
              "concave points_mean       0.823269         0.553695          0.831135   \n",
              "symmetry_mean             0.151293         0.557775          0.602641   \n",
              "fractal_dimension_mean   -0.283110         0.584792          0.565369   \n",
              "radius_se                 0.732562         0.301467          0.497473   \n",
              "texture_se               -0.066280         0.068406          0.046205   \n",
              "perimeter_se              0.726628         0.296092          0.548905   \n",
              "area_se                   0.800086         0.246552          0.455653   \n",
              "smoothness_se            -0.166777         0.332375          0.135299   \n",
              "compactness_se            0.212583         0.318943          0.738722   \n",
              "concavity_se              0.207660         0.248396          0.570517   \n",
              "concave points_se         0.372320         0.380676          0.642262   \n",
              "symmetry_se              -0.072497         0.200774          0.229977   \n",
              "fractal_dimension_se     -0.019887         0.283607          0.507318   \n",
              "radius_worst              0.962746         0.213120          0.535315   \n",
              "texture_worst             0.287489         0.036072          0.248133   \n",
              "perimeter_worst           0.959120         0.238853          0.590210   \n",
              "area_worst                0.959213         0.206718          0.509604   \n",
              "smoothness_worst          0.123523         0.805324          0.565541   \n",
              "compactness_worst         0.390410         0.472468          0.865809   \n",
              "concavity_worst           0.512606         0.434926          0.816275   \n",
              "concave points_worst      0.722017         0.503053          0.815573   \n",
              "symmetry_worst            0.143570         0.394309          0.510223   \n",
              "fractal_dimension_worst   0.003738         0.499316          0.687382   \n",
              "\n",
              "                         concavity_mean  concave points_mean  symmetry_mean  \\\n",
              "diagnosis                      0.696360             0.776614       0.330499   \n",
              "radius_mean                    0.676764             0.822529       0.147741   \n",
              "texture_mean                   0.302418             0.293464       0.071401   \n",
              "perimeter_mean                 0.716136             0.850977       0.183027   \n",
              "area_mean                      0.685983             0.823269       0.151293   \n",
              "smoothness_mean                0.521984             0.553695       0.557775   \n",
              "compactness_mean               0.883121             0.831135       0.602641   \n",
              "concavity_mean                 1.000000             0.921391       0.500667   \n",
              "concave points_mean            0.921391             1.000000       0.462497   \n",
              "symmetry_mean                  0.500667             0.462497       1.000000   \n",
              "fractal_dimension_mean         0.336783             0.166917       0.479921   \n",
              "radius_se                      0.631925             0.698050       0.303379   \n",
              "texture_se                     0.076218             0.021480       0.128053   \n",
              "perimeter_se                   0.660391             0.710650       0.313893   \n",
              "area_se                        0.617427             0.690299       0.223970   \n",
              "smoothness_se                  0.098564             0.027653       0.187321   \n",
              "compactness_se                 0.670279             0.490424       0.421659   \n",
              "concavity_se                   0.691270             0.439167       0.342627   \n",
              "concave points_se              0.683260             0.615634       0.393298   \n",
              "symmetry_se                    0.178009             0.095351       0.449137   \n",
              "fractal_dimension_se           0.449301             0.257584       0.331786   \n",
              "radius_worst                   0.688236             0.830318       0.185728   \n",
              "texture_worst                  0.299879             0.292752       0.090651   \n",
              "perimeter_worst                0.729565             0.855923       0.219169   \n",
              "area_worst                     0.675987             0.809630       0.177193   \n",
              "smoothness_worst               0.448822             0.452753       0.426675   \n",
              "compactness_worst              0.754968             0.667454       0.473200   \n",
              "concavity_worst                0.884103             0.752399       0.433721   \n",
              "concave points_worst           0.861323             0.910155       0.430297   \n",
              "symmetry_worst                 0.409464             0.375744       0.699826   \n",
              "fractal_dimension_worst        0.514930             0.368661       0.438413   \n",
              "\n",
              "                         ...  radius_worst  texture_worst  perimeter_worst  \\\n",
              "diagnosis                ...      0.776454       0.456903         0.782914   \n",
              "radius_mean              ...      0.969539       0.297008         0.965137   \n",
              "texture_mean             ...      0.352573       0.912045         0.358040   \n",
              "perimeter_mean           ...      0.969476       0.303038         0.970387   \n",
              "area_mean                ...      0.962746       0.287489         0.959120   \n",
              "smoothness_mean          ...      0.213120       0.036072         0.238853   \n",
              "compactness_mean         ...      0.535315       0.248133         0.590210   \n",
              "concavity_mean           ...      0.688236       0.299879         0.729565   \n",
              "concave points_mean      ...      0.830318       0.292752         0.855923   \n",
              "symmetry_mean            ...      0.185728       0.090651         0.219169   \n",
              "fractal_dimension_mean   ...     -0.253691      -0.051269        -0.205151   \n",
              "radius_se                ...      0.715065       0.194799         0.719684   \n",
              "texture_se               ...     -0.111690       0.409003        -0.102242   \n",
              "perimeter_se             ...      0.697201       0.200371         0.721031   \n",
              "area_se                  ...      0.757373       0.196497         0.761213   \n",
              "smoothness_se            ...     -0.230691      -0.074743        -0.217304   \n",
              "compactness_se           ...      0.204607       0.143003         0.260516   \n",
              "concavity_se             ...      0.186904       0.100241         0.226680   \n",
              "concave points_se        ...      0.358127       0.086741         0.394999   \n",
              "symmetry_se              ...     -0.128121      -0.077473        -0.103753   \n",
              "fractal_dimension_se     ...     -0.037488      -0.003195        -0.001000   \n",
              "radius_worst             ...      1.000000       0.359921         0.993708   \n",
              "texture_worst            ...      0.359921       1.000000         0.365098   \n",
              "perimeter_worst          ...      0.993708       0.365098         1.000000   \n",
              "area_worst               ...      0.984015       0.345842         0.977578   \n",
              "smoothness_worst         ...      0.216574       0.225429         0.236775   \n",
              "compactness_worst        ...      0.475820       0.360832         0.529408   \n",
              "concavity_worst          ...      0.573975       0.368366         0.618344   \n",
              "concave points_worst     ...      0.787424       0.359755         0.816322   \n",
              "symmetry_worst           ...      0.243529       0.233027         0.269493   \n",
              "fractal_dimension_worst  ...      0.093492       0.219122         0.138957   \n",
              "\n",
              "                         area_worst  smoothness_worst  compactness_worst  \\\n",
              "diagnosis                  0.733825          0.421465           0.590998   \n",
              "radius_mean                0.941082          0.119616           0.413463   \n",
              "texture_mean               0.343546          0.077503           0.277830   \n",
              "perimeter_mean             0.941550          0.150549           0.455774   \n",
              "area_mean                  0.959213          0.123523           0.390410   \n",
              "smoothness_mean            0.206718          0.805324           0.472468   \n",
              "compactness_mean           0.509604          0.565541           0.865809   \n",
              "concavity_mean             0.675987          0.448822           0.754968   \n",
              "concave points_mean        0.809630          0.452753           0.667454   \n",
              "symmetry_mean              0.177193          0.426675           0.473200   \n",
              "fractal_dimension_mean    -0.231854          0.504942           0.458798   \n",
              "radius_se                  0.751548          0.141919           0.287103   \n",
              "texture_se                -0.083195         -0.073658          -0.092439   \n",
              "perimeter_se               0.730713          0.130054           0.341919   \n",
              "area_se                    0.811408          0.125389           0.283257   \n",
              "smoothness_se             -0.182195          0.314457          -0.055558   \n",
              "compactness_se             0.199371          0.227394           0.678780   \n",
              "concavity_se               0.188353          0.168481           0.484858   \n",
              "concave points_se          0.342271          0.215351           0.452888   \n",
              "symmetry_se               -0.110343         -0.012662           0.060255   \n",
              "fractal_dimension_se      -0.022736          0.170568           0.390159   \n",
              "radius_worst               0.984015          0.216574           0.475820   \n",
              "texture_worst              0.345842          0.225429           0.360832   \n",
              "perimeter_worst            0.977578          0.236775           0.529408   \n",
              "area_worst                 1.000000          0.209145           0.438296   \n",
              "smoothness_worst           0.209145          1.000000           0.568187   \n",
              "compactness_worst          0.438296          0.568187           1.000000   \n",
              "concavity_worst            0.543331          0.518523           0.892261   \n",
              "concave points_worst       0.747419          0.547691           0.801080   \n",
              "symmetry_worst             0.209146          0.493838           0.614441   \n",
              "fractal_dimension_worst    0.079647          0.617624           0.810455   \n",
              "\n",
              "                         concavity_worst  concave points_worst  \\\n",
              "diagnosis                       0.659610              0.793566   \n",
              "radius_mean                     0.526911              0.744214   \n",
              "texture_mean                    0.301025              0.295316   \n",
              "perimeter_mean                  0.563879              0.771241   \n",
              "area_mean                       0.512606              0.722017   \n",
              "smoothness_mean                 0.434926              0.503053   \n",
              "compactness_mean                0.816275              0.815573   \n",
              "concavity_mean                  0.884103              0.861323   \n",
              "concave points_mean             0.752399              0.910155   \n",
              "symmetry_mean                   0.433721              0.430297   \n",
              "fractal_dimension_mean          0.346234              0.175325   \n",
              "radius_se                       0.380585              0.531062   \n",
              "texture_se                     -0.068956             -0.119638   \n",
              "perimeter_se                    0.418899              0.554897   \n",
              "area_se                         0.385100              0.538166   \n",
              "smoothness_se                  -0.058298             -0.102007   \n",
              "compactness_se                  0.639147              0.483208   \n",
              "concavity_se                    0.662564              0.440472   \n",
              "concave points_se               0.549592              0.602450   \n",
              "symmetry_se                     0.037119             -0.030413   \n",
              "fractal_dimension_se            0.379975              0.215204   \n",
              "radius_worst                    0.573975              0.787424   \n",
              "texture_worst                   0.368366              0.359755   \n",
              "perimeter_worst                 0.618344              0.816322   \n",
              "area_worst                      0.543331              0.747419   \n",
              "smoothness_worst                0.518523              0.547691   \n",
              "compactness_worst               0.892261              0.801080   \n",
              "concavity_worst                 1.000000              0.855434   \n",
              "concave points_worst            0.855434              1.000000   \n",
              "symmetry_worst                  0.532520              0.502528   \n",
              "fractal_dimension_worst         0.686511              0.511114   \n",
              "\n",
              "                         symmetry_worst  fractal_dimension_worst  \n",
              "diagnosis                      0.416294                 0.323872  \n",
              "radius_mean                    0.163953                 0.007066  \n",
              "texture_mean                   0.105008                 0.119205  \n",
              "perimeter_mean                 0.189115                 0.051019  \n",
              "area_mean                      0.143570                 0.003738  \n",
              "smoothness_mean                0.394309                 0.499316  \n",
              "compactness_mean               0.510223                 0.687382  \n",
              "concavity_mean                 0.409464                 0.514930  \n",
              "concave points_mean            0.375744                 0.368661  \n",
              "symmetry_mean                  0.699826                 0.438413  \n",
              "fractal_dimension_mean         0.334019                 0.767297  \n",
              "radius_se                      0.094543                 0.049559  \n",
              "texture_se                    -0.128215                -0.045655  \n",
              "perimeter_se                   0.109930                 0.085433  \n",
              "area_se                        0.074126                 0.017539  \n",
              "smoothness_se                 -0.107342                 0.101480  \n",
              "compactness_se                 0.277878                 0.590973  \n",
              "concavity_se                   0.197788                 0.439329  \n",
              "concave points_se              0.143116                 0.310655  \n",
              "symmetry_se                    0.389402                 0.078079  \n",
              "fractal_dimension_se           0.111094                 0.591328  \n",
              "radius_worst                   0.243529                 0.093492  \n",
              "texture_worst                  0.233027                 0.219122  \n",
              "perimeter_worst                0.269493                 0.138957  \n",
              "area_worst                     0.209146                 0.079647  \n",
              "smoothness_worst               0.493838                 0.617624  \n",
              "compactness_worst              0.614441                 0.810455  \n",
              "concavity_worst                0.532520                 0.686511  \n",
              "concave points_worst           0.502528                 0.511114  \n",
              "symmetry_worst                 1.000000                 0.537848  \n",
              "fractal_dimension_worst        0.537848                 1.000000  \n",
              "\n",
              "[31 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3d4fd53-cfde-4545-a798-7e5a2761b4ab\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>diagnosis</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>...</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>diagnosis</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.730029</td>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.696360</td>\n",
              "      <td>0.776614</td>\n",
              "      <td>0.330499</td>\n",
              "      <td>...</td>\n",
              "      <td>0.776454</td>\n",
              "      <td>0.456903</td>\n",
              "      <td>0.782914</td>\n",
              "      <td>0.733825</td>\n",
              "      <td>0.421465</td>\n",
              "      <td>0.590998</td>\n",
              "      <td>0.659610</td>\n",
              "      <td>0.793566</td>\n",
              "      <td>0.416294</td>\n",
              "      <td>0.323872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_mean</th>\n",
              "      <td>0.730029</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>...</td>\n",
              "      <td>0.969539</td>\n",
              "      <td>0.297008</td>\n",
              "      <td>0.965137</td>\n",
              "      <td>0.941082</td>\n",
              "      <td>0.119616</td>\n",
              "      <td>0.413463</td>\n",
              "      <td>0.526911</td>\n",
              "      <td>0.744214</td>\n",
              "      <td>0.163953</td>\n",
              "      <td>0.007066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_mean</th>\n",
              "      <td>0.415185</td>\n",
              "      <td>0.323782</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>...</td>\n",
              "      <td>0.352573</td>\n",
              "      <td>0.912045</td>\n",
              "      <td>0.358040</td>\n",
              "      <td>0.343546</td>\n",
              "      <td>0.077503</td>\n",
              "      <td>0.277830</td>\n",
              "      <td>0.301025</td>\n",
              "      <td>0.295316</td>\n",
              "      <td>0.105008</td>\n",
              "      <td>0.119205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_mean</th>\n",
              "      <td>0.742636</td>\n",
              "      <td>0.997855</td>\n",
              "      <td>0.329533</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.556936</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.850977</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>...</td>\n",
              "      <td>0.969476</td>\n",
              "      <td>0.303038</td>\n",
              "      <td>0.970387</td>\n",
              "      <td>0.941550</td>\n",
              "      <td>0.150549</td>\n",
              "      <td>0.455774</td>\n",
              "      <td>0.563879</td>\n",
              "      <td>0.771241</td>\n",
              "      <td>0.189115</td>\n",
              "      <td>0.051019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_mean</th>\n",
              "      <td>0.708984</td>\n",
              "      <td>0.987357</td>\n",
              "      <td>0.321086</td>\n",
              "      <td>0.986507</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>0.498502</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.823269</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>...</td>\n",
              "      <td>0.962746</td>\n",
              "      <td>0.287489</td>\n",
              "      <td>0.959120</td>\n",
              "      <td>0.959213</td>\n",
              "      <td>0.123523</td>\n",
              "      <td>0.390410</td>\n",
              "      <td>0.512606</td>\n",
              "      <td>0.722017</td>\n",
              "      <td>0.143570</td>\n",
              "      <td>0.003738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_mean</th>\n",
              "      <td>0.358560</td>\n",
              "      <td>0.170581</td>\n",
              "      <td>-0.023389</td>\n",
              "      <td>0.207278</td>\n",
              "      <td>0.177028</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.659123</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.553695</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>...</td>\n",
              "      <td>0.213120</td>\n",
              "      <td>0.036072</td>\n",
              "      <td>0.238853</td>\n",
              "      <td>0.206718</td>\n",
              "      <td>0.805324</td>\n",
              "      <td>0.472468</td>\n",
              "      <td>0.434926</td>\n",
              "      <td>0.503053</td>\n",
              "      <td>0.394309</td>\n",
              "      <td>0.499316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_mean</th>\n",
              "      <td>0.596534</td>\n",
              "      <td>0.506124</td>\n",
              "      <td>0.236702</td>\n",
              "      <td>0.556936</td>\n",
              "      <td>0.498502</td>\n",
              "      <td>0.659123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.883121</td>\n",
              "      <td>0.831135</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>...</td>\n",
              "      <td>0.535315</td>\n",
              "      <td>0.248133</td>\n",
              "      <td>0.590210</td>\n",
              "      <td>0.509604</td>\n",
              "      <td>0.565541</td>\n",
              "      <td>0.865809</td>\n",
              "      <td>0.816275</td>\n",
              "      <td>0.815573</td>\n",
              "      <td>0.510223</td>\n",
              "      <td>0.687382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_mean</th>\n",
              "      <td>0.696360</td>\n",
              "      <td>0.676764</td>\n",
              "      <td>0.302418</td>\n",
              "      <td>0.716136</td>\n",
              "      <td>0.685983</td>\n",
              "      <td>0.521984</td>\n",
              "      <td>0.883121</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.921391</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>...</td>\n",
              "      <td>0.688236</td>\n",
              "      <td>0.299879</td>\n",
              "      <td>0.729565</td>\n",
              "      <td>0.675987</td>\n",
              "      <td>0.448822</td>\n",
              "      <td>0.754968</td>\n",
              "      <td>0.884103</td>\n",
              "      <td>0.861323</td>\n",
              "      <td>0.409464</td>\n",
              "      <td>0.514930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_mean</th>\n",
              "      <td>0.776614</td>\n",
              "      <td>0.822529</td>\n",
              "      <td>0.293464</td>\n",
              "      <td>0.850977</td>\n",
              "      <td>0.823269</td>\n",
              "      <td>0.553695</td>\n",
              "      <td>0.831135</td>\n",
              "      <td>0.921391</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>...</td>\n",
              "      <td>0.830318</td>\n",
              "      <td>0.292752</td>\n",
              "      <td>0.855923</td>\n",
              "      <td>0.809630</td>\n",
              "      <td>0.452753</td>\n",
              "      <td>0.667454</td>\n",
              "      <td>0.752399</td>\n",
              "      <td>0.910155</td>\n",
              "      <td>0.375744</td>\n",
              "      <td>0.368661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_mean</th>\n",
              "      <td>0.330499</td>\n",
              "      <td>0.147741</td>\n",
              "      <td>0.071401</td>\n",
              "      <td>0.183027</td>\n",
              "      <td>0.151293</td>\n",
              "      <td>0.557775</td>\n",
              "      <td>0.602641</td>\n",
              "      <td>0.500667</td>\n",
              "      <td>0.462497</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.185728</td>\n",
              "      <td>0.090651</td>\n",
              "      <td>0.219169</td>\n",
              "      <td>0.177193</td>\n",
              "      <td>0.426675</td>\n",
              "      <td>0.473200</td>\n",
              "      <td>0.433721</td>\n",
              "      <td>0.430297</td>\n",
              "      <td>0.699826</td>\n",
              "      <td>0.438413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <td>-0.012838</td>\n",
              "      <td>-0.311631</td>\n",
              "      <td>-0.076437</td>\n",
              "      <td>-0.261477</td>\n",
              "      <td>-0.283110</td>\n",
              "      <td>0.584792</td>\n",
              "      <td>0.565369</td>\n",
              "      <td>0.336783</td>\n",
              "      <td>0.166917</td>\n",
              "      <td>0.479921</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.253691</td>\n",
              "      <td>-0.051269</td>\n",
              "      <td>-0.205151</td>\n",
              "      <td>-0.231854</td>\n",
              "      <td>0.504942</td>\n",
              "      <td>0.458798</td>\n",
              "      <td>0.346234</td>\n",
              "      <td>0.175325</td>\n",
              "      <td>0.334019</td>\n",
              "      <td>0.767297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_se</th>\n",
              "      <td>0.567134</td>\n",
              "      <td>0.679090</td>\n",
              "      <td>0.275869</td>\n",
              "      <td>0.691765</td>\n",
              "      <td>0.732562</td>\n",
              "      <td>0.301467</td>\n",
              "      <td>0.497473</td>\n",
              "      <td>0.631925</td>\n",
              "      <td>0.698050</td>\n",
              "      <td>0.303379</td>\n",
              "      <td>...</td>\n",
              "      <td>0.715065</td>\n",
              "      <td>0.194799</td>\n",
              "      <td>0.719684</td>\n",
              "      <td>0.751548</td>\n",
              "      <td>0.141919</td>\n",
              "      <td>0.287103</td>\n",
              "      <td>0.380585</td>\n",
              "      <td>0.531062</td>\n",
              "      <td>0.094543</td>\n",
              "      <td>0.049559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_se</th>\n",
              "      <td>-0.008303</td>\n",
              "      <td>-0.097317</td>\n",
              "      <td>0.386358</td>\n",
              "      <td>-0.086761</td>\n",
              "      <td>-0.066280</td>\n",
              "      <td>0.068406</td>\n",
              "      <td>0.046205</td>\n",
              "      <td>0.076218</td>\n",
              "      <td>0.021480</td>\n",
              "      <td>0.128053</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.111690</td>\n",
              "      <td>0.409003</td>\n",
              "      <td>-0.102242</td>\n",
              "      <td>-0.083195</td>\n",
              "      <td>-0.073658</td>\n",
              "      <td>-0.092439</td>\n",
              "      <td>-0.068956</td>\n",
              "      <td>-0.119638</td>\n",
              "      <td>-0.128215</td>\n",
              "      <td>-0.045655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_se</th>\n",
              "      <td>0.556141</td>\n",
              "      <td>0.674172</td>\n",
              "      <td>0.281673</td>\n",
              "      <td>0.693135</td>\n",
              "      <td>0.726628</td>\n",
              "      <td>0.296092</td>\n",
              "      <td>0.548905</td>\n",
              "      <td>0.660391</td>\n",
              "      <td>0.710650</td>\n",
              "      <td>0.313893</td>\n",
              "      <td>...</td>\n",
              "      <td>0.697201</td>\n",
              "      <td>0.200371</td>\n",
              "      <td>0.721031</td>\n",
              "      <td>0.730713</td>\n",
              "      <td>0.130054</td>\n",
              "      <td>0.341919</td>\n",
              "      <td>0.418899</td>\n",
              "      <td>0.554897</td>\n",
              "      <td>0.109930</td>\n",
              "      <td>0.085433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_se</th>\n",
              "      <td>0.548236</td>\n",
              "      <td>0.735864</td>\n",
              "      <td>0.259845</td>\n",
              "      <td>0.744983</td>\n",
              "      <td>0.800086</td>\n",
              "      <td>0.246552</td>\n",
              "      <td>0.455653</td>\n",
              "      <td>0.617427</td>\n",
              "      <td>0.690299</td>\n",
              "      <td>0.223970</td>\n",
              "      <td>...</td>\n",
              "      <td>0.757373</td>\n",
              "      <td>0.196497</td>\n",
              "      <td>0.761213</td>\n",
              "      <td>0.811408</td>\n",
              "      <td>0.125389</td>\n",
              "      <td>0.283257</td>\n",
              "      <td>0.385100</td>\n",
              "      <td>0.538166</td>\n",
              "      <td>0.074126</td>\n",
              "      <td>0.017539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_se</th>\n",
              "      <td>-0.067016</td>\n",
              "      <td>-0.222600</td>\n",
              "      <td>0.006614</td>\n",
              "      <td>-0.202694</td>\n",
              "      <td>-0.166777</td>\n",
              "      <td>0.332375</td>\n",
              "      <td>0.135299</td>\n",
              "      <td>0.098564</td>\n",
              "      <td>0.027653</td>\n",
              "      <td>0.187321</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.230691</td>\n",
              "      <td>-0.074743</td>\n",
              "      <td>-0.217304</td>\n",
              "      <td>-0.182195</td>\n",
              "      <td>0.314457</td>\n",
              "      <td>-0.055558</td>\n",
              "      <td>-0.058298</td>\n",
              "      <td>-0.102007</td>\n",
              "      <td>-0.107342</td>\n",
              "      <td>0.101480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_se</th>\n",
              "      <td>0.292999</td>\n",
              "      <td>0.206000</td>\n",
              "      <td>0.191975</td>\n",
              "      <td>0.250744</td>\n",
              "      <td>0.212583</td>\n",
              "      <td>0.318943</td>\n",
              "      <td>0.738722</td>\n",
              "      <td>0.670279</td>\n",
              "      <td>0.490424</td>\n",
              "      <td>0.421659</td>\n",
              "      <td>...</td>\n",
              "      <td>0.204607</td>\n",
              "      <td>0.143003</td>\n",
              "      <td>0.260516</td>\n",
              "      <td>0.199371</td>\n",
              "      <td>0.227394</td>\n",
              "      <td>0.678780</td>\n",
              "      <td>0.639147</td>\n",
              "      <td>0.483208</td>\n",
              "      <td>0.277878</td>\n",
              "      <td>0.590973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_se</th>\n",
              "      <td>0.253730</td>\n",
              "      <td>0.194204</td>\n",
              "      <td>0.143293</td>\n",
              "      <td>0.228082</td>\n",
              "      <td>0.207660</td>\n",
              "      <td>0.248396</td>\n",
              "      <td>0.570517</td>\n",
              "      <td>0.691270</td>\n",
              "      <td>0.439167</td>\n",
              "      <td>0.342627</td>\n",
              "      <td>...</td>\n",
              "      <td>0.186904</td>\n",
              "      <td>0.100241</td>\n",
              "      <td>0.226680</td>\n",
              "      <td>0.188353</td>\n",
              "      <td>0.168481</td>\n",
              "      <td>0.484858</td>\n",
              "      <td>0.662564</td>\n",
              "      <td>0.440472</td>\n",
              "      <td>0.197788</td>\n",
              "      <td>0.439329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_se</th>\n",
              "      <td>0.408042</td>\n",
              "      <td>0.376169</td>\n",
              "      <td>0.163851</td>\n",
              "      <td>0.407217</td>\n",
              "      <td>0.372320</td>\n",
              "      <td>0.380676</td>\n",
              "      <td>0.642262</td>\n",
              "      <td>0.683260</td>\n",
              "      <td>0.615634</td>\n",
              "      <td>0.393298</td>\n",
              "      <td>...</td>\n",
              "      <td>0.358127</td>\n",
              "      <td>0.086741</td>\n",
              "      <td>0.394999</td>\n",
              "      <td>0.342271</td>\n",
              "      <td>0.215351</td>\n",
              "      <td>0.452888</td>\n",
              "      <td>0.549592</td>\n",
              "      <td>0.602450</td>\n",
              "      <td>0.143116</td>\n",
              "      <td>0.310655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_se</th>\n",
              "      <td>-0.006522</td>\n",
              "      <td>-0.104321</td>\n",
              "      <td>0.009127</td>\n",
              "      <td>-0.081629</td>\n",
              "      <td>-0.072497</td>\n",
              "      <td>0.200774</td>\n",
              "      <td>0.229977</td>\n",
              "      <td>0.178009</td>\n",
              "      <td>0.095351</td>\n",
              "      <td>0.449137</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.128121</td>\n",
              "      <td>-0.077473</td>\n",
              "      <td>-0.103753</td>\n",
              "      <td>-0.110343</td>\n",
              "      <td>-0.012662</td>\n",
              "      <td>0.060255</td>\n",
              "      <td>0.037119</td>\n",
              "      <td>-0.030413</td>\n",
              "      <td>0.389402</td>\n",
              "      <td>0.078079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <td>0.077972</td>\n",
              "      <td>-0.042641</td>\n",
              "      <td>0.054458</td>\n",
              "      <td>-0.005523</td>\n",
              "      <td>-0.019887</td>\n",
              "      <td>0.283607</td>\n",
              "      <td>0.507318</td>\n",
              "      <td>0.449301</td>\n",
              "      <td>0.257584</td>\n",
              "      <td>0.331786</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.037488</td>\n",
              "      <td>-0.003195</td>\n",
              "      <td>-0.001000</td>\n",
              "      <td>-0.022736</td>\n",
              "      <td>0.170568</td>\n",
              "      <td>0.390159</td>\n",
              "      <td>0.379975</td>\n",
              "      <td>0.215204</td>\n",
              "      <td>0.111094</td>\n",
              "      <td>0.591328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radius_worst</th>\n",
              "      <td>0.776454</td>\n",
              "      <td>0.969539</td>\n",
              "      <td>0.352573</td>\n",
              "      <td>0.969476</td>\n",
              "      <td>0.962746</td>\n",
              "      <td>0.213120</td>\n",
              "      <td>0.535315</td>\n",
              "      <td>0.688236</td>\n",
              "      <td>0.830318</td>\n",
              "      <td>0.185728</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.359921</td>\n",
              "      <td>0.993708</td>\n",
              "      <td>0.984015</td>\n",
              "      <td>0.216574</td>\n",
              "      <td>0.475820</td>\n",
              "      <td>0.573975</td>\n",
              "      <td>0.787424</td>\n",
              "      <td>0.243529</td>\n",
              "      <td>0.093492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>texture_worst</th>\n",
              "      <td>0.456903</td>\n",
              "      <td>0.297008</td>\n",
              "      <td>0.912045</td>\n",
              "      <td>0.303038</td>\n",
              "      <td>0.287489</td>\n",
              "      <td>0.036072</td>\n",
              "      <td>0.248133</td>\n",
              "      <td>0.299879</td>\n",
              "      <td>0.292752</td>\n",
              "      <td>0.090651</td>\n",
              "      <td>...</td>\n",
              "      <td>0.359921</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.365098</td>\n",
              "      <td>0.345842</td>\n",
              "      <td>0.225429</td>\n",
              "      <td>0.360832</td>\n",
              "      <td>0.368366</td>\n",
              "      <td>0.359755</td>\n",
              "      <td>0.233027</td>\n",
              "      <td>0.219122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>perimeter_worst</th>\n",
              "      <td>0.782914</td>\n",
              "      <td>0.965137</td>\n",
              "      <td>0.358040</td>\n",
              "      <td>0.970387</td>\n",
              "      <td>0.959120</td>\n",
              "      <td>0.238853</td>\n",
              "      <td>0.590210</td>\n",
              "      <td>0.729565</td>\n",
              "      <td>0.855923</td>\n",
              "      <td>0.219169</td>\n",
              "      <td>...</td>\n",
              "      <td>0.993708</td>\n",
              "      <td>0.365098</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.977578</td>\n",
              "      <td>0.236775</td>\n",
              "      <td>0.529408</td>\n",
              "      <td>0.618344</td>\n",
              "      <td>0.816322</td>\n",
              "      <td>0.269493</td>\n",
              "      <td>0.138957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>area_worst</th>\n",
              "      <td>0.733825</td>\n",
              "      <td>0.941082</td>\n",
              "      <td>0.343546</td>\n",
              "      <td>0.941550</td>\n",
              "      <td>0.959213</td>\n",
              "      <td>0.206718</td>\n",
              "      <td>0.509604</td>\n",
              "      <td>0.675987</td>\n",
              "      <td>0.809630</td>\n",
              "      <td>0.177193</td>\n",
              "      <td>...</td>\n",
              "      <td>0.984015</td>\n",
              "      <td>0.345842</td>\n",
              "      <td>0.977578</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.209145</td>\n",
              "      <td>0.438296</td>\n",
              "      <td>0.543331</td>\n",
              "      <td>0.747419</td>\n",
              "      <td>0.209146</td>\n",
              "      <td>0.079647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoothness_worst</th>\n",
              "      <td>0.421465</td>\n",
              "      <td>0.119616</td>\n",
              "      <td>0.077503</td>\n",
              "      <td>0.150549</td>\n",
              "      <td>0.123523</td>\n",
              "      <td>0.805324</td>\n",
              "      <td>0.565541</td>\n",
              "      <td>0.448822</td>\n",
              "      <td>0.452753</td>\n",
              "      <td>0.426675</td>\n",
              "      <td>...</td>\n",
              "      <td>0.216574</td>\n",
              "      <td>0.225429</td>\n",
              "      <td>0.236775</td>\n",
              "      <td>0.209145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.568187</td>\n",
              "      <td>0.518523</td>\n",
              "      <td>0.547691</td>\n",
              "      <td>0.493838</td>\n",
              "      <td>0.617624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>compactness_worst</th>\n",
              "      <td>0.590998</td>\n",
              "      <td>0.413463</td>\n",
              "      <td>0.277830</td>\n",
              "      <td>0.455774</td>\n",
              "      <td>0.390410</td>\n",
              "      <td>0.472468</td>\n",
              "      <td>0.865809</td>\n",
              "      <td>0.754968</td>\n",
              "      <td>0.667454</td>\n",
              "      <td>0.473200</td>\n",
              "      <td>...</td>\n",
              "      <td>0.475820</td>\n",
              "      <td>0.360832</td>\n",
              "      <td>0.529408</td>\n",
              "      <td>0.438296</td>\n",
              "      <td>0.568187</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.892261</td>\n",
              "      <td>0.801080</td>\n",
              "      <td>0.614441</td>\n",
              "      <td>0.810455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concavity_worst</th>\n",
              "      <td>0.659610</td>\n",
              "      <td>0.526911</td>\n",
              "      <td>0.301025</td>\n",
              "      <td>0.563879</td>\n",
              "      <td>0.512606</td>\n",
              "      <td>0.434926</td>\n",
              "      <td>0.816275</td>\n",
              "      <td>0.884103</td>\n",
              "      <td>0.752399</td>\n",
              "      <td>0.433721</td>\n",
              "      <td>...</td>\n",
              "      <td>0.573975</td>\n",
              "      <td>0.368366</td>\n",
              "      <td>0.618344</td>\n",
              "      <td>0.543331</td>\n",
              "      <td>0.518523</td>\n",
              "      <td>0.892261</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.855434</td>\n",
              "      <td>0.532520</td>\n",
              "      <td>0.686511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>concave points_worst</th>\n",
              "      <td>0.793566</td>\n",
              "      <td>0.744214</td>\n",
              "      <td>0.295316</td>\n",
              "      <td>0.771241</td>\n",
              "      <td>0.722017</td>\n",
              "      <td>0.503053</td>\n",
              "      <td>0.815573</td>\n",
              "      <td>0.861323</td>\n",
              "      <td>0.910155</td>\n",
              "      <td>0.430297</td>\n",
              "      <td>...</td>\n",
              "      <td>0.787424</td>\n",
              "      <td>0.359755</td>\n",
              "      <td>0.816322</td>\n",
              "      <td>0.747419</td>\n",
              "      <td>0.547691</td>\n",
              "      <td>0.801080</td>\n",
              "      <td>0.855434</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.502528</td>\n",
              "      <td>0.511114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symmetry_worst</th>\n",
              "      <td>0.416294</td>\n",
              "      <td>0.163953</td>\n",
              "      <td>0.105008</td>\n",
              "      <td>0.189115</td>\n",
              "      <td>0.143570</td>\n",
              "      <td>0.394309</td>\n",
              "      <td>0.510223</td>\n",
              "      <td>0.409464</td>\n",
              "      <td>0.375744</td>\n",
              "      <td>0.699826</td>\n",
              "      <td>...</td>\n",
              "      <td>0.243529</td>\n",
              "      <td>0.233027</td>\n",
              "      <td>0.269493</td>\n",
              "      <td>0.209146</td>\n",
              "      <td>0.493838</td>\n",
              "      <td>0.614441</td>\n",
              "      <td>0.532520</td>\n",
              "      <td>0.502528</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.537848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <td>0.323872</td>\n",
              "      <td>0.007066</td>\n",
              "      <td>0.119205</td>\n",
              "      <td>0.051019</td>\n",
              "      <td>0.003738</td>\n",
              "      <td>0.499316</td>\n",
              "      <td>0.687382</td>\n",
              "      <td>0.514930</td>\n",
              "      <td>0.368661</td>\n",
              "      <td>0.438413</td>\n",
              "      <td>...</td>\n",
              "      <td>0.093492</td>\n",
              "      <td>0.219122</td>\n",
              "      <td>0.138957</td>\n",
              "      <td>0.079647</td>\n",
              "      <td>0.617624</td>\n",
              "      <td>0.810455</td>\n",
              "      <td>0.686511</td>\n",
              "      <td>0.511114</td>\n",
              "      <td>0.537848</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>31 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3d4fd53-cfde-4545-a798-7e5a2761b4ab')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f3d4fd53-cfde-4545-a798-7e5a2761b4ab button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f3d4fd53-cfde-4545-a798-7e5a2761b4ab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize the correlation\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(df.iloc[:,1:10].corr(),annot=True,fmt=\".0%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "_YqhVjRgd_p6",
        "outputId": "35b865bb-9f3d-405c-d3a3-3a464f179872"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Feature Scaling and Model Building**"
      ],
      "metadata": {
        "id": "h4ON1gQLqs1c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# split the dataset into dependent(X) and Independent(Y) datasets\n",
        "\n",
        "X=df.iloc[:,2:31].values\n",
        "Y=df.iloc[:,1].values"
      ],
      "metadata": {
        "id": "utLfs-fpeD8-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# spliting the data into 80% training and 20% test dataset\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.20,random_state=0)"
      ],
      "metadata": {
        "id": "wp1_Yp-IeDye"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# feature scaling\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "X_train=StandardScaler().fit_transform(X_train)\n",
        "X_test=StandardScaler().fit_transform(X_test)"
      ],
      "metadata": {
        "id": "2I5uyy69eJcp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# models/ Algorithms\n",
        "\n",
        "def models(X_train,Y_train):\n",
        "\n",
        "        # Logistic Regression\n",
        "        from sklearn.linear_model import LogisticRegression\n",
        "        log=LogisticRegression(random_state=0)\n",
        "        log.fit(X_train,Y_train)\n",
        "\n",
        "\n",
        "        # Decision Tree\n",
        "        from sklearn.tree import DecisionTreeClassifier\n",
        "        tree=DecisionTreeClassifier(random_state=0,criterion=\"entropy\")\n",
        "        tree.fit(X_train,Y_train)\n",
        "\n",
        "        # Random Forest\n",
        "        from sklearn.ensemble import RandomForestClassifier\n",
        "        forest=RandomForestClassifier(random_state=0,criterion=\"entropy\",n_estimators=10)\n",
        "        forest.fit(X_train,Y_train)\n",
        "\n",
        "        # K-NN\n",
        "        from sklearn.neighbors import KNeighborsClassifier\n",
        "        classifier= KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2 )\n",
        "        classifier.fit(X_train, Y_train)\n",
        "\n",
        "        # Support Vector Machines\n",
        "        from sklearn.svm import SVC\n",
        "        svm = SVC(gamma='auto')\n",
        "        svm.fit(X_train, Y_train)\n",
        "\n",
        "        print('1) Logistic Regression Accuracy:',log.score(X_train,Y_train) * 100, \"%\")\n",
        "        print('2) Decision Tree Accuracy:',tree.score(X_train,Y_train) * 100,\"%\")\n",
        "        print('3) Random Forest Accuracy:',forest.score(X_train,Y_train) * 100,\"%\")\n",
        "        print('4) KNN Accuracy:',classifier.score(X_train,Y_train) * 100, \"%\")\n",
        "        print('5) SVM Accuracy:',svm.score(X_train,Y_train) * 100,\"%\")\n",
        "\n",
        "        return log,tree,forest,classifier,svm"
      ],
      "metadata": {
        "id": "hiieOlX1eND_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=models(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghR-pM2Meoro",
        "outputId": "0bb43beb-90d2-48fb-99cc-6b6c4c2f84e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1) Logistic Regression Accuracy: 99.12087912087912 %\n",
            "2) Decision Tree Accuracy: 100.0 %\n",
            "3) Random Forest Accuracy: 99.78021978021978 %\n",
            "4) KNN Accuracy: 98.02197802197801 %\n",
            "5) SVM Accuracy: 98.46153846153847 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# testing the models accurracy on confusion matrix\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "for i in range(len(model)):\n",
        "  print('Model ', i)\n",
        "  cm=confusion_matrix(Y_test,model[i].predict(X_test))\n",
        "\n",
        "  TP=cm[0][0]\n",
        "  TN=cm[1][1]\n",
        "  FN=cm[1][0]\n",
        "  FP=cm[0][1]\n",
        "\n",
        "  print(cm)\n",
        "  print('Testing Accuracy=',((TP + TN)/(TP +TN +FN +FP)) * 100, \"%\")\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TnB4yDIw-l5z",
        "outputId": "d10c5726-ef03-4e0c-e495-2b96790624de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model  0\n",
            "[[66  1]\n",
            " [ 3 44]]\n",
            "Testing Accuracy= 96.49122807017544 %\n",
            "\n",
            "Model  1\n",
            "[[64  3]\n",
            " [ 4 43]]\n",
            "Testing Accuracy= 93.85964912280701 %\n",
            "\n",
            "Model  2\n",
            "[[67  0]\n",
            " [ 3 44]]\n",
            "Testing Accuracy= 97.36842105263158 %\n",
            "\n",
            "Model  3\n",
            "[[67  0]\n",
            " [ 4 43]]\n",
            "Testing Accuracy= 96.49122807017544 %\n",
            "\n",
            "Model  4\n",
            "[[67  0]\n",
            " [ 2 45]]\n",
            "Testing Accuracy= 98.24561403508771 %\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# testing the models/result\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "for i in range(len(model)):\n",
        "    print(\"Model\",i)\n",
        "    print(classification_report(Y_test,model[i].predict(X_test)))\n",
        "    print('Accuracy : ',accuracy_score(Y_test,model[i].predict(X_test)) * 100,\"%\")\n",
        "    print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdA86388eulo",
        "outputId": "678d60db-ea7e-4c5a-fce6-0a13db5af2ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model 0\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.97        67\n",
            "           1       0.98      0.94      0.96        47\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.97      0.96      0.96       114\n",
            "weighted avg       0.97      0.96      0.96       114\n",
            "\n",
            "Accuracy :  96.49122807017544 %\n",
            "\n",
            "Model 1\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.96      0.95        67\n",
            "           1       0.93      0.91      0.92        47\n",
            "\n",
            "    accuracy                           0.94       114\n",
            "   macro avg       0.94      0.94      0.94       114\n",
            "weighted avg       0.94      0.94      0.94       114\n",
            "\n",
            "Accuracy :  93.85964912280701 %\n",
            "\n",
            "Model 2\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98        67\n",
            "           1       1.00      0.94      0.97        47\n",
            "\n",
            "    accuracy                           0.97       114\n",
            "   macro avg       0.98      0.97      0.97       114\n",
            "weighted avg       0.97      0.97      0.97       114\n",
            "\n",
            "Accuracy :  97.36842105263158 %\n",
            "\n",
            "Model 3\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      1.00      0.97        67\n",
            "           1       1.00      0.91      0.96        47\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.97      0.96      0.96       114\n",
            "weighted avg       0.97      0.96      0.96       114\n",
            "\n",
            "Accuracy :  96.49122807017544 %\n",
            "\n",
            "Model 4\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.99        67\n",
            "           1       1.00      0.96      0.98        47\n",
            "\n",
            "    accuracy                           0.98       114\n",
            "   macro avg       0.99      0.98      0.98       114\n",
            "weighted avg       0.98      0.98      0.98       114\n",
            "\n",
            "Accuracy :  98.24561403508771 %\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Model                   |      Accuracy\n",
        "|-----------              | : ---------------\n",
        "|Logistic Regression      |        96.49%\n",
        "|Decision Tree            |        93.85%\n",
        "|Random Forest            |        97.36%\n",
        "|KNN                      |        96.49%\n",
        "|SVM                      |        98.24%"
      ],
      "metadata": {
        "id": "ycTP-HPYGTG7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perform K-Fold Cross Validation\n",
        "### To evaluate the model's ability when given new data. K-fold cross-validation has several advantages for predictive analytics, such as reducing the variance of the performance estimate and allowing you to use more data for training. It also helps you avoid overfitting, as it exposes your model to different subsets of the data."
      ],
      "metadata": {
        "id": "PHIONAcpX0_b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 5-Fold Cross-Validation\n",
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "def cross_validation(model, _X, _y, _cv=5):\n",
        "  '''Function to perform 5 Folds Cross-Validation\n",
        "\n",
        "  model: machine learning algorithm is add as argument,\n",
        "  _X: the matrix of features,\n",
        "  _y: the target variable,\n",
        "  _cv: number of folds for cross-validation. Default is 5.\n",
        "\n",
        "  Output:\n",
        "  Function returns a dictionary containing the metrics ('accuracy', 'precision', 'recall', 'f1')\n",
        "  for both training set [if the return_train_score=True] and validation set.\n",
        "  '''\n",
        "\n",
        "  scoring = ['accuracy', 'precision', 'recall', 'f1']\n",
        "  results = cross_validate(estimator=model,\n",
        "                         X=_X,\n",
        "                         y=_y,\n",
        "                         cv=_cv,\n",
        "                         scoring=scoring,\n",
        "                         return_train_score=True)\n",
        "\n",
        "  return {\"Training Accuracy scores\": results['train_accuracy'],\n",
        "          \"Mean Training Accuracy\": results['train_accuracy'].mean()*100,\n",
        "          \"Training Precision scores\": results['train_precision'],\n",
        "          \"Mean Training Precision\": results['train_precision'].mean(),\n",
        "          \"Training Recall scores\": results['train_recall'],\n",
        "          \"Mean Training Recall\": results['train_recall'].mean(),\n",
        "          \"Training F1 scores\": results['train_f1'],\n",
        "          \"Mean Training F1 Score\": results['train_f1'].mean(),\n",
        "          \"Validation Accuracy scores\": results['test_accuracy'],\n",
        "          \"Mean Validation Accuracy\": results['test_accuracy'].mean()*100,\n",
        "          \"Validation Precision scores\": results['test_precision'],\n",
        "          \"Mean Validation Precision\": results['test_precision'].mean(),\n",
        "          \"Validation Recall scores\": results['test_recall'],\n",
        "          \"Mean Validation Recall\": results['test_recall'].mean(),\n",
        "          \"Validation F1 scores\": results['test_f1'],\n",
        "          \"Mean Validation F1 Score\": results['test_f1'].mean()\n",
        "          }\n",
        "\n",
        "\n",
        "# Grouped Bar Chart for both training and validation data\n",
        "def plot_result(x_label, y_label, plot_title, train_data, val_data):\n",
        "    '''\n",
        "      Function to plot a grouped bar chart showing the training and validation\n",
        "      results of the ML model in each fold after applying K-fold cross validation.\n",
        "\n",
        "      x_label: str, name of the algorithm used as x_label e.g\n",
        "      y_label: str, y_label e.g Accuracy\n",
        "      plot_title: str, plot title e.g Accuracy Plot\n",
        "      train_data: list, list containing either training precision, accuracy, f1 score\n",
        "      val_data: list, list containing either validation precision, accuracy, f1 score\n",
        "    '''\n",
        "    # Accuracies Plot\n",
        "    plt.figure(figsize=(12,6))\n",
        "    # colour = ['green', 'blue', 'orange', 'violet', 'brown']\n",
        "\n",
        "    labels = [\"1st Fold\", \"2nd Fold\", \"3rd Fold\", \"4th Fold\", \"5th Fold\"]\n",
        "\n",
        "    X_axis = np.arange(len(labels))\n",
        "\n",
        "    ax = plt.gca()\n",
        "    plt.ylim(0.40000, 1)\n",
        "    plt.bar(X_axis-0.2, train_data, 0.4, color='blue', label='Training')\n",
        "    plt.bar(X_axis+0.2, val_data, 0.4, color='red', label='Validation')\n",
        "    plt.title(plot_title, fontsize=30)\n",
        "    plt.xticks(X_axis, labels)\n",
        "    plt.xlabel(x_label, fontsize=14)\n",
        "    plt.ylabel(y_label, fontsize=14)\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "ewA1zuRTJpXg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, recall_score\n",
        "from sklearn.metrics import precision_score, f1_score, ConfusionMatrixDisplay\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def evaluate_model(y_val, y_pred):\n",
        "  \"\"\"Function to evaluate model and return the metric of the model\n",
        "\n",
        "  It returns a dictionary with the classification metrics.\n",
        "  \"\"\"\n",
        "  accuracy = accuracy_score(y_val, y_pred)\n",
        "  precision = precision_score(y_val, y_pred, average='macro')\n",
        "  recall = recall_score(y_val, y_pred, average='macro')\n",
        "  f1 = f1_score(y_val, y_pred, average='macro')\n",
        "  result = {\"accuracy_score\": accuracy,\n",
        "            \"precision_score\": precision,\n",
        "            \"recall_score\": recall,\n",
        "            \"f1_score\": f1}\n",
        "  return result\n",
        "\n",
        "def plot_confusion_matrix(y_val, y_pred, label):\n",
        "  '''function to plot confusion matrix\n",
        "\n",
        "  Args\n",
        "  y_val: array. The validation set of the target variable.\n",
        "  y_pred: array. Model's prediction.\n",
        "  label: list. A list containing all the classes in the target variable\n",
        "\n",
        "  Returns\n",
        "  It returns a plot of the confusion matrix\n",
        "  '''\n",
        "  cm = confusion_matrix(y_val, y_pred)\n",
        "  fig, ax = plt.subplots(figsize=(10,10))\n",
        "  ax.grid(False)\n",
        "  ConfusionMatrixDisplay(cm, display_labels=label).plot(ax=ax, values_format='',\n",
        "                                                        xticks_rotation ='vertical')\n",
        "\n",
        "# class labels\n",
        "label = ['B', 'M']"
      ],
      "metadata": {
        "id": "oKebrg4LJ9K4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Training with Logistic Regression"
      ],
      "metadata": {
        "id": "7mZq5YQ5KHH_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using GridSearchCV to find the best parameters\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "logistic_regression_model_2 = LogisticRegression(max_iter=2000)\n",
        "\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'C': np.logspace(-5,8,15),\n",
        "               'penalty': ['l1', 'l2']}]\n",
        "# n_jobs=-1 uses all the system's processors\n",
        "grid_search = GridSearchCV(estimator=logistic_regression_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(X_train, Y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best f1 {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3TKqiP4VKK84",
        "outputId": "ba1af3a0-2bf3-4879-d2d5-40471378d2f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best f1 98.13 %\n",
            "Best Parameters: {'C': 0.4393970560760795, 'penalty': 'l2'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
            "75 fits failed out of a total of 150.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "75 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 1162, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py\", line 54, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [       nan 0.                nan 0.01176471        nan 0.76820835\n",
            "        nan 0.91034248        nan 0.95881824        nan 0.98134615\n",
            "        nan 0.95734506        nan 0.95769393        nan 0.94525689\n",
            "        nan 0.93937187        nan 0.9305071         nan 0.92777906\n",
            "        nan 0.93126088        nan 0.92819183        nan 0.93416198]\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the best parameters to train the logistic regression algorithm\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr_model = LogisticRegression(C=3.727593720314938, penalty='l2', max_iter=2000)\n",
        "lr_train_val_result = cross_validation(lr_model, X_train, Y_train, 5)\n",
        "print(lr_train_val_result)\n",
        "\n",
        "# Plot Training and Validation Result with F1-score as metric\n",
        "model_name = \"Logistic Regression\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=lr_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=lr_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "lr_model.fit(X_train, Y_train)\n",
        "lr_y_pred = lr_model.predict(X_test)\n",
        "# Get test result\n",
        "lr_test_result = evaluate_model(Y_test, lr_y_pred)\n",
        "print(lr_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(Y_test, lr_y_pred, label)\n",
        "print(classification_report(Y_test, lr_y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bRmtoq_bKvjs",
        "outputId": "27bdf83f-9e75-4742-8478-cbee1890ead8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.98901099, 0.98901099, 0.99725275, 0.98626374, 0.98351648]), 'Mean Training Accuracy': 98.9010989010989, 'Training Precision scores': array([1.        , 1.        , 1.        , 0.99224806, 0.9921875 ]), 'Mean Training Precision': 0.9968871124031008, 'Training Recall scores': array([0.96969697, 0.96969697, 0.99242424, 0.96969697, 0.96212121]), 'Mean Training Recall': 0.9727272727272727, 'Training F1 scores': array([0.98461538, 0.98461538, 0.99619772, 0.98084291, 0.97692308]), 'Mean Training F1 Score': 0.9846388953324838, 'Validation Accuracy scores': array([0.95604396, 0.96703297, 0.94505495, 0.98901099, 0.98901099]), 'Mean Validation Accuracy': 96.92307692307693, 'Validation Precision scores': array([0.91428571, 0.94117647, 0.96666667, 0.97058824, 1.        ]), 'Mean Validation Precision': 0.9585434173669469, 'Validation Recall scores': array([0.96969697, 0.96969697, 0.87878788, 1.        , 0.96969697]), 'Mean Validation Recall': 0.9575757575757576, 'Validation F1 scores': array([0.94117647, 0.95522388, 0.92063492, 0.98507463, 0.98461538]), 'Mean Validation F1 Score': 0.9573450566602455}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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4+BRIvSdOnLCZbtiwYa6XERoaajOd1W3cMqpatarD68gYsnv37i2LxZKrx7Bhw2yWcenSJbv1PP300woICLBOx8bGKiIiQmXLllWPHj00Y8YM7dy5s1jsObw1iDvC29vb+u+MhzkXlRo1atgdnr18+fJMt50Z5ffzVa1aNfn6+lqnT506JcMw8lXT8ePHbabr16+fq/kbNGjgcN+Mp9KsXbtWLVu2VNWqVfX0009r/vz5du8RABQUgjsAIEuurq5asmSJatWqZdOenJysVatWady4cbrvvvtUokQJtWvXTjNmzLDZ452ZjCElt4EsO5cvX7aZvnXvpqMyzpNxmVlx9DB5Sbp48WKuanJEZnuVS5UqpaVLlyowMNCmPTExUYsXL9bIkSMVFhamUqVKqWvXrvr4449Ney93Dw+Poi6hQAwYMEBly5a1TqelpWV6P3QzKojP161HKKSmpurq1av5qinjeHX0CIi89O/Xr59efvllu+sVxMTEaPbs2erXr5+Cg4MVHBysJ598UmvXrs33DxMAkI7gDgDIVrVq1bRnzx69+uqrdufopktKStL69eutF4J77rnnsvyDPGN7Qe1tl/4+IuBWt+51dVTGeRwNFrk5R70wwnFaWlqm7c2aNdOBAwc0evRolSpVKtM+165d048//qiIiAhVrlxZUVFRxWIvfHHk5ORkd3j24cOHi6aYXCrKz5ejNXl5eeWrnpxMmDBBW7duVdeuXeXs7JxpnxMnTuijjz7SP/7xDzVs2FCrVq3K1ToAIDMuRV0AAMD8vLy89O9//1tjx47V5s2btXbtWq1fv17bt2+3Oww5OTlZ7777rn766Sdt3LjRbo/6rYfKSvZ/eOdHxh8Bbj0k31EZ58lYb0HIGC7eeOMNNW3aNF/LrFevXpbPlSpVSv/5z3/0yiuvaP369Vq3bp02bNigXbt22QX0q1evasqUKVq9erV++umnXAch5KxSpUo20+fPny+iSnLHjJ+vjME7MTExV8vMy2to0aKFli1bpjNnzuinn37S+vXrtXHjRh07dsyu74EDB9S5c2dNmzZNI0eOzPW6ACAdwR0A4DAnJyfdd999uu+++zR58mSlpKRo165dWrFihebNm6ejR49a+x4+fFiDBg3S8uXLbZaRca9vbGxsgdVXsmRJm+m8HJKe8ersGZdZEDIeYly1alV16NChwNeTkZubmzp16mS9Iv3169e1bds2LV++XPPmzbO50v3mzZs1atQoffDBB4Ve190m49EZxeXohoL4fN06j7Ozc76De4kSJWymL1y4kKtl5ue0lcDAQA0cOFADBw6UJJ0+fVpr167V999/r8WLF1u3q2EYeuGFFxQeHm5zSzwAyA0OlQcA5Jmrq6vCw8M1ZcoU/f7773r//fdtrvz9448/2l2IrXz58jZ77g4fPlxge92rVKliM52XW21lnCfjMgtCxgvZ3fqDx+3k6empdu3a6a233lJMTIzGjx9v8/zHH3+s+Pj4IqntTnb27Fmb6VvPeTez/H6+jh07ZnNofOXKle3OF8+tatWq2UwfOHAgV/P/8ssv+Vr/rSpUqKB//vOf+uabb3TkyBE1b97c+pxhGPrvf/9bYOsCcPchuAMACoTFYtGzzz6rfv362bRv2rTJZtrFxUXh4eHW6bS0NK1YsaJAaggODra5ivqJEycyPXw1K1evXtWuXbtsas3Lrdpy0q5dO5vptWvXFvg6csvV1VWvvPKKWrVqZW1LSUmxuwUY8i/jZyLjofNmlXFvcW7Hbcb+BbH3+dZwLEkbNmzI1fy57e+oKlWqaN68eTZtGbc7AOQGwR0AUKBatmxpM53x0HNJdvdtf+eddwps/W3atLGZnjt3rsPzzp8/3+ac/WbNmhXKOd5hYWE2hx2vXbtWBw8eLPD15IUj2w95t2HDBv3xxx82bRnvb25W99xzj9zc3KzTmzZtytXRInPmzLGZzvhZzYuMF/r76quvHD71YN++fYqOjs53DVmpUaOGzQ+JfJYA5AfBHQBQoBw5R3zIkCE256H+/PPP+uijjwpk/UOHDrWZnj59uv78888c54uPj7e7T3NERESB1JSRq6urnn/+eeu0YRh66qmnTHGu8+04x/9ulZCQoOeee86mrUGDBnaHe5tViRIl9Mgjj1inDcPQqFGjHJr3m2++0datW63TPj4+euyxx/JdU926ddWiRQvr9F9//eXwD4GjR4/O9/qzk5ycbHOqCZ8lAPlBcAcAZKl///65OpT08uXL+vjjj23aMrtaeokSJTRixAibtuHDh+urr75yaD0pKSn6+eefM32uQ4cOatiwoXU6ISFBPXr0yPQ+5+mSk5P16KOP6syZM9a2wMBAu8P+C9KIESNs9sZt2rRJjzzySLZ1ZpSQkKB33nlHn3zyid1z8fHx6t+/v/bu3evw8mJiYvTNN99Yp52cnNSoUSOH57+bjBgxwuZifjm5cOGCHnzwQbtzqqdMmVLQpRWqkSNH2lzH4ocfftArr7yS7TzR0dF2P6gNHTpUfn5+BVLTrT+CSdL48eO1bt26bOeZOHFirm7TNm/ePL3++ut297LPzvvvv29zBE9+7xwB4O5GcAcAk9i8ebNWr15t99i9e7dNvxs3bmTab/Xq1QV+uPWyZcvUtm1b1a1bV5MnT9bWrVuVmJho1+/69ev6+uuv1aJFC504ccLaHhoaancOarpJkybZnOOanJysxx57TA8//LA2b96s1NRUm/43b97U7t27NWHCBFWvXl0TJ07MdLkWi0WffvqpzZW79+zZo0aNGmnRokVKTk62tqelpWnVqlVq1qyZfvrpJ5vlzJkzRx4eHtm8O/nj7++vr7/+2qbOxYsXq169epo2bZpOnjyZ6XynTp3SN998o3/+85+qUKGCRowYoVOnTtn1S0tL07x589SkSRM1b95cb775pvbs2WPz+tPFx8frk08+UXh4uM0PB927d1f58uUL4NXeed555x1Vq1ZNvXr10pdffqmYmJhM+506dUpvvfWWGjRoYHeOd8+ePdWrV6/bUG3Badasmd1tzSZOnKhHHnnE7sJwly9f1tSpU9WyZUubcVW9evUcw35u9O3b1+auDElJSerSpYumTJlid6u9gwcP6pFHHrGuPzg42KF1xMbG6t///rcqVqyoRx55RPPnz7f5rrvV77//ruHDh+uFF16waS+sI3gA3CUMAECOqlSpYkiyPtatW1fo68jLY+DAgQVak7+/v906nJ2djSpVqhiNGzc2WrRoYdSqVctwdXW16+fl5WXs3r072+X/+eefRr169TJ9Lb6+vkbdunWNFi1aGLVr1zY8PDxsnm/Tpk22y/7www8NJyenTJfboEEDo1GjRkbJkiUzXfeUKVNyfG8mT55cIGNi/vz5dq8t/REYGGiEhoYaYWFhRq1atbKsd/LkyXbLvXz5cqZ93dzcjGrVqhlNmzY1wsLCjOrVqxvOzs52/cqWLWucPHkyT68pK7l5z44fP56vsd2mTRub+QtaZu+tn5+fUatWLSMsLMxo3LixUaFChSw/q/fdd5+RmJhY4HWl+/TTT3P1ecmNGzduGB06dMj0dVWoUMFo1qxZlt8LpUuXzvF7wTByv/1OnTqV6Xeoi4uLERISYjRv3tyoWLGizXNBQUHGV199leNnyTAM47///W+mr7dEiRJG7dq1jXvuucdo0qSJUbZs2Uz79e3bN7dvMwDY4D7uAIBcSU1N1YkTJ7Lc2yRJQUFB+vrrr9WkSZNslxUUFKTNmzdrwIABWrx4sc1zV69ezdcRBBEREfL399fQoUNtbkF19epV7d+/P9N53N3dNW3aNA0bNizP682tvn37KiQkRP369dPvv/9u89yZM2dsDt/PjLOzsypUqODw+pKTk3O80n7t2rX1ww8/FJurnZtFfHx8jrfPc3Jy0qhRo/TKK6/Y3c+9uHB3d9eyZcv0xBNP6H//+5/Nc6dPn87yFIKQkBAtXrxYtWvXLvCaKlasqDVr1qhz5842F/+7efOmjhw5Yte/QoUKWrFiRb4vGHflyhVduXIl2z4RERH64IMP8rUeAOBQeQBAlnbs2KH//Oc/+sc//iFvb+8c+9eoUUMvv/yyDh8+bHPLt+z4+/vrhx9+0Lp169SpUyebq1ZnplatWpowYYI+//zzHJfdu3dvHT16VMOGDVOpUqWy7Ofr66sBAwbot99+u62hPV3Tpk118OBBff7557rnnnvk7OycbX93d3e1b99eU6dO1alTp/Tkk0/a9SlRooT27t2rqKgotWzZUu7u7jnW0bBhQ73zzjv65ZdfVLNmzTy/nrvBhx9+qL59+zr840b58uU1YsQIHT58WG+++WaxDe3p3Nzc9MUXX2j9+vVq3769XFyy3hdUvXp1TZs2TQcOHCiU0H7ren755Re9+OKLWV4Izt3dXREREdq3b5/q16/v8LKfeuop/fDDDxo6dKhq1KiRY393d3f16tVLP//8sz788MNs3x8AcITFMAyjqIsAAJhfamqqDh06pCNHjuivv/6y7sX29fVVUFCQGjVqpKpVq+Z7PQkJCdq8ebP+/PNPXbhwQampqfLz81PVqlXVsGFDVaxYMc/179ixQ0eOHFFsbKzS0tJUtmxZVatWTffee6+pglRcXJy2bdum06dP68KFC0pJSZGvr6/KlSun2rVrq1atWrk+/z4pKUkHDx7U0aNHdebMGV27dk0Wi0V+fn4KDg5Wo0aNFBQUVEiv6M528eJFHTp0SCdOnND58+eVkJAgZ2dnlSxZUmXKlFHjxo2LzZXj8+rKlSvatGmTTp8+rYsXL8rb21sBAQFq1KiRatWqddvrSU5O1oYNG3Ts2DFduHBB3t7eCgkJUevWrW3uaJFX58+f18GDB3Xs2DFdunRJiYmJ8vLyUsmSJVW7dm2FhoY69GMnADiK4A4AAAAAgIlxqDwAAAAAACZGcAcAAAAAwMRMF9w3btyo7t27q0KFCrJYLPr+++9znGf9+vVq0qSJ3N3dVaNGDc2dO7fQ6wQAAAAA4HYwXXBPSEhQaGio3n//fYf6Hz9+XA888IDatWun6OhoPf/88xo6dKhWrlxZyJUCAAAAAFD4TH1xOovFou+++049e/bMss+LL76oZcuW6cCBA9a2vn376sqVK1qxYsVtqBIAAAAAgMJT7G8quXXrVnXo0MGmrXPnznr++eeznCcpKUlJSUnW6bS0NF26dEmlS5eWxWIprFIBAAAAAJAkGYahq1evqkKFCnJyyv5g+GIf3M+ePauAgACbtoCAAMXHx+v69evy9PS0m+f111/XlClTbleJAAAAAABk6tSpU6pYsWK2fYp9cM+LcePGKTIy0jodFxenypUr6/jx4/L19S3CypBXKSkpWrdundq1aydXV9eiLgfIFcYvijPGL4ozxi+KO8Zw8Xb16lVVrVrVoQxa7IN7+fLlde7cOZu2c+fOyc/PL9O97ZLk7u4ud3d3u/ZSpUrJz8+vUOpE4UpJSZGXl5dKly7NlxaKHcYvijPGL4ozxi+KO8Zw8Za+zRw5Xdt0V5XPrfDwcK1Zs8ambdWqVQoPDy+iigAAdyqLhUfGh79/UW8VAADufKYL7teuXVN0dLSio6Ml/X27t+joaJ08eVLS34e5DxgwwNr/6aef1rFjxzRmzBj99ttv+uCDD7Rw4UKNHDmyKMoHAAAAAKBAmS6479q1S40bN1bjxo0lSZGRkWrcuLEmTZokSTpz5ow1xEtS1apVtWzZMq1atUqhoaGaNm2aPv74Y3Xu3LlI6gcAAAAAoCCZ7hz3tm3bKrtby8+dOzfTefbu3VuIVQEAkHsWi6ESJW7K1zdVd+rdRj08UuTi4qIbN24oNTW1qMspVM7OznJxceHWsQCA2850wR0AgDtB2bLJeuaZM2rWLFEuLrpjg7vFYqhMmfI6derUXRFovby8FBgYKDc3t6IuBQBwFyG4AwBQwFxc0jRt2nFVreosD48Kktwk3Zmh1mJJU6VK1+Tj4yMnJ9OdgVdgDMNQcnKyzp8/r+PHjyskJOSOfr0AAHMhuAMAUMACA5NVpkyaPDwqSfIq6nIKlcWSJje3ZHl4eNzxQdbT01Ourq46ceKEkpP/fs0AANwOd/b/YQEAKAJOTumHxvO/2TvNnf7jBADAnNjjDgAAgLubv790/XpRV2E+2VwwGsDtxc/GxZDFwiPjw9+/qLcKHFXUY8WMD8Yv7mTBwcGaMWOGw/3Xr18vi8WiK1euFFpNAHDH8fcv+j9ozPa4w7DHHQCA26R589u7vp07He/bvHn2f+REREzWk09G5aGGnfL29na4/7333qszZ87In1+0AACwIrgDAAD9+OMZ679XrVqg2bMn6ZtvDlvbvLx8rP82DEOpqalyccn5z4iyZcvmqg43NzeVL18+V/PAMXfgDqh88/SU5s8v6ioAIGccKg8AAFSmTHnrw8fHXxaLxTp94sRvatPGV5s3/6jHH2+qe+911759m/Tnn38oMrKnatasKT8/PzVv3lyrV6+2WW7GQ+UtFos+/vhj9erVS15eXgoJCdHixYutz2c8VH7u3LkqUaKEVq5cqTp16sjHx0ddunTRmTP/90PDzZs39dxzz6lEiRIqXbq0XnzxRQ0cOFA9e/YszLcMAIDbhuAOAAAc8v77YzV8+Bv6+utDqlGjoRITr6lly/v1/fffa/fu3erSpYu6d++ukydPZrucKVOmqHfv3vrll1/UtWtX9e/fX5cuXcqyf2JioqZOnaovvvhCGzdu1MmTJzVq1Cjr82+++aa+/PJLffrpp9q8ebPi4+P1/fffF9TLBgCgyBHcAQCAQ5566iW1aNFRFStWl79/KdWsGaqHH35KdevWVUhIiF5++WVVr17dZg96ZgYNGqTHHntMNWrU0GuvvaZr165px44dWfZPSUnRrFmz1KxZMzVp0kTDhw/XmjVrrM+/++67GjdunHr16qXatWvrvffeU4kSJQrqZQMwgaK+zplZH1wO5O7BOe4AAMAhdeo0s5lOTLymjz6arB07lurcuXO6efOmrl+/nuMe94YNG1r/7e3tLT8/P8XGxmbZ38vLS9WrV7dOBwYGWvvHxcXp3LlzCgsLsz7v7Oyspk2bKi0tLVevDwAAsyK4AwAAh3h62l4d/u23R2n79lV6440patCggby9vfXII48oOTk52+W4urraTFsslmxDdmb9De4vDQC4i3CoPAAAyJN9+zare/eB6tatmxo0aKDy5csrJibmttbg7++vgIAA7bzl3nepqanas2fPba0DAIDCxB53AACQJ5UqhWjduu+0f387+fj4aPLkyUVyePq//vUvvf7666pRo4Zq166td999V5cvX5aF+58BAO4Q7HEHAAB5MnLkdPn6llTnzp3Vo0cPde7cWU2aNLntdbz44ot67LHHNGDAAIWHh8vHx0edO3eWh4fHba8FAIDCwB53AABuk1uO5ja17t0HqXv3Qdbppk3baudO+3PKK1QI1uzZq1WtWrz8/Pzk5OSkYcOG2fTJeOh8Zuemp9+zXZLatm1r02fQoEEaNGiQTf+ePXva9HFxcdG7776rd999V5KUlpamOnXqqHfv3jm9VAAAigWCOwAAKNZOnDihn376SW3atFFSUpLee+89HT9+XP369Svq0gAAKBAcKg8AAIo1JycnzZ07V82bN1fLli21f/9+rV69WnXq1Cnq0gAAKBDscQcAAMVapUqVtHnz5qIuAwCAQsMedwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AABSItm3b6vnnn7dOBwcHa8aMGdnOY7FY9P333+d73QW1HAAAzIj7uAOAWfj7S9evF3UV5mMYRV1BgWnW3HJb17drp+Pv3ciR3XXzZorefXeF3XN79/6sJ59srXnz9ikkpKHDy9y5c6e8vb0d7u+IqKgoff/994qOjrZpP3PmjEqWLFmg6wIAwCzY4w4AANSjxxPasWOVzp370+65JUs+VZ06zXIV2iWpbNmy8vLyKqgSs1W+fHm5u7vflnUBAHC7EdwBAIBateqmEiXKaunSuTbtiYnXtGbN12rbtqfGj39MXbsGqVUrL/Xt20ArV87PdpkZD5U/cuSIWrduLQ8PD9WtW1erVq2ym+fFF19UzZo15eXlpWrVqmnixIlKSUmRJM2dO1dTpkzRvn37ZLFYZLFYNHfu3/VmPFR+//79at++vTw9PVW6dGk9+eSTunbtmvX5QYMGqWfPnpo6daoCAwNVunRpDRs2zLouAADMhEPlAQCAXFxc9MADA7R06VwNGTJeFsvfh/WvXv21UlNTdf/9/9Tq1V9rwIAX5e3tp82bl2ny5MdVqVJVVatWO8flp6Wl6aGHHlJAQIC2b9+uuLg4m/Ph0/n6+mru3LmqUKGC9u/fr4iICPn6+mrMmDHq06ePDhw4oBUrVmj16tWSJH9/f7tlJCQkqHPnzgoPD9fOnTsVGxuroUOHavjw4dagL0nr1q1TYGCg1q1bp6NHj6pPnz5q1KiRIiIi8vYmAgBQSNjjDgAAJEkPPjhEf/75h/bs2WBtW7r0U7Vv/7ACA6vo8cdHqVatRqpYsZr69PmXwsO7aNWqrx1a9urVq/Xbb7/p888/V2hoqFq3bq3XXnvNrt+ECRN07733Kjg4WN27d9eoUaO0cOFCSZKnp6d8fHzk4uKi8uXLq3z58vL09LRbxrx583Tjxg19/vnnql+/vtq3b6/33ntPX3zxhc6dO2ftV7JkSb333nuqXbu2unXrpgceeEBr1qzJ7dsGAEChY487AACQJAUH11bDhvdq8eI5atq0rU6dOqq9e3/WrFkvKTU1VZ9++ppWr16o8+f/UkpKspKTk+ThYR+cM3Po0CFVqlRJFSpUsLaFh4fb9VuwYIHeeecd/fHHH7p27Zpu3rwpPz+/XL2OQ4cOKTQ01ObCeC1btlRaWpoOHz6sgIAASVK9evXk7Oxs7RMYGKj9+/fnal0AANwO7HEHAABWPXo8obVrFykh4aqWLPlUFStWV5MmbfTFF2/pq6/e1oABL2rmzHX68stohYd3VkpKcoGte+vWrerfv7+6du2qpUuXau/evRo/frySkwtuHbdydXW1mbZYLEpLSyuUdQEAkB8EdwAAYNWhQ285OTlp5cp5Wrbscz344BBZLBbt27dZbdr0UNeu/1TNmqEKCqqmkyd/d3i5derU0alTp3TmzBlr27Zt22z6bNmyRVWqVNH48ePVrFkzhYSE6MSJEzZ93NzclJqamuO69u3bp4SEBGvb5s2b5eTkpFq1ajlcMwAAZkFwBwAAVl5ePurYsY/ef3+cLl48o27dBkmSKlcO0fbtq7Rv3xYdP35Ir732lC5ePJf9wm7RoUMH1axZUwMHDtS+ffv0888/a/z48TZ9QkJCdPLkSX311Vf6448/9M477+i7776z6RMcHKzjx48rOjpaFy5cUFJSkt26+vfvLw8PDw0cOFAHDhzQunXr9K9//UuPP/649TB5AACKE4I7AACw8eCDTyg+/rLuuaezypb9+5z0IUMmqHbtJnruuc56+um2Kl26vNq27enwMp2cnPTdd9/p+vXrCgsL09ChQ/Xqq69mWO+DGjlypIYPH65GjRppy5Ytmjhxok2fhx9+WF26dFG7du1UtmxZzZ9vf0s6Ly8vrVy5UpcuXVLz5s31yCOP6B//+Ifee++93L8ZAACYgMUwDKOoiyhq8fHx8vf3V1xcXK4vgFMU/v8denALT88UzZ+/XF27drU7ZxHmwvi1Zx2/jz0m1+vXi7oc8zHR/6YcHb9VqtzQrFnHVaZMVUkehVpTUXNySlO1avHy8/OTk9Odvz/gxo0bOn78uKpWrSoPj+K1bfn+tcf3bw6K4ffv3YYxnA0Tjd+s5CaHclV53Fn8/SW+tGwVgy8tAAAAAFm7838aBwAAAACgGCO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAChghpF+XUguDnmn4WY8AICiQHAHAKCAXbzoquRkSUos6lJQwBIT/96m3HoUAHA7cTs4AAAKWEKCsxYvLqHHHotViRKS5CXpzrwJsWGkKTk5WTdu3Lij7+NuGIYSExMVGxurEiVKyNnZuahLAgDcRQjuAAAUgk8/LS9JevDBWLm5SZY7M7fLYjEkXZenp6csd+qLvEWJEiVUvnz5oi4DAHCXIbgDAFAIDMOiOXMC9dVX5VSmTModG9w9PFI0bdpGtW7d+o4/fNzV1ZU97QCAIkFwBwCgECUmOuvkyTs37Hl6OuvmzZvy8PC444M7AABF5c49GQ0AAAAAgDsAwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiXFVeQAAkH/+/tL160VdhfkYRlFXAAC4A7DHHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYqYM7u+//76Cg4Pl4eGhFi1aaMeOHVn2TUlJ0UsvvaTq1avLw8NDoaGhWrFixW2sFgAAAACAwmO64L5gwQJFRkZq8uTJ2rNnj0JDQ9W5c2fFxsZm2n/ChAmaPXu23n33XR08eFBPP/20evXqpb17997mygEAAAAAKHimC+7Tp09XRESEBg8erLp162rWrFny8vLSnDlzMu3/xRdf6N///re6du2qatWq6ZlnnlHXrl01bdq021w5AAAAAAAFz1TBPTk5Wbt371aHDh2sbU5OTurQoYO2bt2a6TxJSUny8PCwafP09NSmTZsKtVYAAAAAAG4Hl6Iu4FYXLlxQamqqAgICbNoDAgL022+/ZTpP586dNX36dLVu3VrVq1fXmjVr9O233yo1NTXL9SQlJSkpKck6HR8fL+nv8+VTUlIK4JUULk/Poq7AfDw9/95uKbw59kw2ptlE9hi/OTDRGGYT2WP85oDxa2qM3xwwfk2PMZwNE43frOQme1oMwzAKsZZcOX36tIKCgrRlyxaFh4db28eMGaMNGzZo+/btdvOcP39eERERWrJkiSwWi6pXr64OHTpozpw5un79eqbriYqK0pQpU+za582bJy8vr4J7QQAAAAAAZCIxMVH9+vVTXFyc/Pz8su1rqj3uZcqUkbOzs86dO2fTfu7cOZUvXz7TecqWLavvv/9eN27c0MWLF1WhQgWNHTtW1apVy3I948aNU2RkpHU6Pj5elSpVUqdOnXJ8w8zA37+oKzAfT88UzZmzSh2HDJFrFj/Y3LXi4oq6AhuMX3uM3xyYaAwzfu0xfnPA+DU1xm8OGL+mxxjOhonGb1bSj/x2hKmCu5ubm5o2bao1a9aoZ8+ekqS0tDStWbNGw4cPz3ZeDw8PBQUFKSUlRYsWLVLv3r2z7Ovu7i53d3e7dldXV7m6uubrNdwOfCaz5nr9Ol9aGZlsTLN5ssb4zYKJxjCbJ2uM3ywwfosFxm8WGL/FBmM4EyYav1nJTfY0VXCXpMjISA0cOFDNmjVTWFiYZsyYoYSEBA0ePFiSNGDAAAUFBen111+XJG3fvl1//fWXGjVqpL/++ktRUVFKS0vTmDFjivJlAAAAAABQIEwX3Pv06aPz589r0qRJOnv2rBo1aqQVK1ZYL1h38uRJOTn938Xwb9y4oQkTJujYsWPy8fFR165d9cUXX6hEiRJF9AoAAAAAACg4pgvukjR8+PAsD41fv369zXSbNm108ODB21AVAAAAAAC3n6nu4w4AAAAAAGwR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJmbK4P7+++8rODhYHh4eatGihXbs2JFt/xkzZqhWrVry9PRUpUqVNHLkSN24ceM2VQsAAAAAQOExXXBfsGCBIiMjNXnyZO3Zs0ehoaHq3LmzYmNjM+0/b948jR07VpMnT9ahQ4f0ySefaMGCBfr3v/99mysHAAAAAKDgmS64T58+XRERERo8eLDq1q2rWbNmycvLS3PmzMm0/5YtW9SyZUv169dPwcHB6tSpkx577LEc99IDAAAAAFAcuBR1AbdKTk7W7t27NW7cOGubk5OTOnTooK1bt2Y6z7333qv//e9/2rFjh8LCwnTs2DEtX75cjz/+eJbrSUpKUlJSknU6Pj5ekpSSkqKUlJQCejWFx9OzqCswH0/Pv7dbCm+OPZONaTaRPcZvDkw0htlE9hi/OWD8mhrjNweMX9NjDGfDROM3K7nJnhbDMIxCrCVXTp8+raCgIG3ZskXh4eHW9jFjxmjDhg3avn17pvO98847GjVqlAzD0M2bN/X0009r5syZWa4nKipKU6ZMsWufN2+evLy88v9CAAAAAADIRmJiovr166e4uDj5+fll29dUe9zzYv369Xrttdf0wQcfqEWLFjp69KhGjBihl19+WRMnTsx0nnHjxikyMtI6HR8fr0qVKqlTp045vmFm4O9f1BWYj6dniubMWaWOQ4bI9fr1oi7HXOLiiroCG4xfe4zfHJhoDDN+7TF+c8D4NTXGbw4Yv6bHGM6GicZvVtKP/HaEqYJ7mTJl5OzsrHPnztm0nzt3TuXLl890nokTJ+rxxx/X0KFDJUkNGjRQQkKCnnzySY0fP15OTvan8bu7u8vd3d2u3dXVVa6urgXwSgoXn8msuV6/zpdWRiYb02yerDF+s2CiMczmyRrjNwuM32KB8ZsFxm+xwRjOhInGb1Zykz1NdXE6Nzc3NW3aVGvWrLG2paWlac2aNTaHzt8qMTHRLpw7OztLkkx0FgAAAAAAAHliqj3ukhQZGamBAweqWbNmCgsL04wZM5SQkKDBgwdLkgYMGKCgoCC9/vrrkqTu3btr+vTpaty4sfVQ+YkTJ6p79+7WAA8AAAAAQHFluuDep08fnT9/XpMmTdLZs2fVqFEjrVixQgEBAZKkkydP2uxhnzBhgiwWiyZMmKC//vpLZcuWVffu3fXqq68W1UsAAAAAAKDAmC64S9Lw4cM1fPjwTJ9bv369zbSLi4smT56syZMn34bKAAAAAAC4vUx1jjsAAAAAALBFcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYi75mXnv3r2aP3++fvvtNyUmJmr16tWSpBMnTmj79u3q0KGDSpUqVSCFAgAAAABwN8pzcB8zZoymTZsmwzAkSRaLxfqcYRjq16+fpk2bphEjRuS/SgAAAAAA7lJ5OlT+008/1dSpU9WtWzf98ssvGjdunM3zwcHBCgsL0+LFiwukSAAAAAAA7lZ52uP+wQcfqE6dOlq0aJFcXFzk5uZm16d27drWQ+cBAAAAAEDe5GmP+8GDB9WxY0e5uGSd+wMCAhQbG5vnwgAAAAAAQB6Du4uLi5KTk7Ptc/r0afn4+OSpKAAAAAAA8Lc8BfcGDRpo7dq1Sk1NzfT59CvMN23aNF/FAQAAAABwt8tTcB8yZIh+//13Pf3000pKSrJ5Lj4+XoMGDdLZs2cVERFRIEUCAAAAAHC3ytPF6YYMGaLVq1frk08+0YIFC1SiRAlJUlhYmA4dOqSEhAQNGjRIjzzySEHWCgAAAADAXSdPe9wlad68eZo9e7aqVq2qv/76S4ZhaNeuXapcubJmzpypOXPmFGSdAAAAAADclfK0xz1dRESEIiIidP36dV2+fFl+fn5ckA4AAAAAgAKUpz3uzs7O6t+/v3Xa09NTFSpUILQDAAAAAFDA8hTc/fz8VKlSpYKuBQAAAAAAZJCn4B4WFqZ9+/YVdC0AAAAAACCDPAX3qKgorV27Vp9//nlB1wMAAAAAAG6Rp4vTrVq1Sm3bttXgwYP17rvvqnnz5goICJDFYrHpZ7FYNHHixAIpFAAAAACAu1GegntUVJT137t379bu3bsz7UdwBwAAAAAgf/IU3NetW1fQdQAAAAAAgEzkKbi3adOmoOsAAAAAAACZyNPF6QAAAAAAwO2Rpz3u6TZv3qy5c+cqOjpa8fHx8vPzU+PGjTVgwAC1atWqoGoEAAAAAOCulefgPnLkSL3zzjsyDEPS3xeiMwxDu3fv1ieffKIRI0Zo+vTpBVYoAAAAAAB3ozwdKv/ZZ5/p7bffVkhIiL788kudPn1aN2/e1JkzZzRv3jzVrFlTb7/9Nvd5BwAAAAAgn/IU3GfOnKmKFStq+/bteuyxx1S+fHlZLBYFBASob9++2rZtm4KCgvTBBx8UdL0AAAAAANxV8hTcf/31Vz388MPy9/fP9Hl/f389/PDD+vXXX/NVHAAAAAAAd7tCu6q8xWIprEUDAAAAAHDXyFNwr1evnhYtWqRr165l+vzVq1e1aNEi1atXL1/FAQAAAABwt8tTcH/qqaf0559/Kjw8XIsWLdKFCxckSRcuXNA333yje++9V3/++aeeeeaZAi0WAAAAAIC7TZ5uBzd48GDt3btX7733nnr37i1JcnJyUlpamiTJMAz961//0sCBAwuuUgAAAAAA7kJ5vo/7O++8o0cffVRz585VdHS04uPj5efnp8aNG2vgwIG67777CrJOAAAAAADuSnkO7pJ03333EdABAAAAAChEhXZVeQAAAAAAkH95Cu6fffaZmjZtqtOnT2f6/OnTp9W0aVPNmzcvX8UBAAAAAHC3y1Nwnzt3rtzc3FShQoVMn69QoYI8PT31ySef5Ks4AAAAAADudnkK7gcPHlTjxo2z7dOoUSMdPHgwT0UBAAAAAIC/5Sm4x8XFqWTJktn28fPz0+XLl/NUFAAAAAAA+FuegnuFChUUHR2dbZ99+/YpICAgL4uXJL3//vsKDg6Wh4eHWrRooR07dmTZt23btrJYLHaPBx54IM/rBwAAAADADPIU3Dt06KCVK1dq1apVmT7/008/acWKFercuXOeilqwYIEiIyM1efJk7dmzR6GhoercubNiY2Mz7f/tt9/qzJkz1seBAwfk7OysRx99NE/rBwAAAADALPIU3MeNGydvb2917dpVQ4YM0fz587Vx40bNnz9fgwcP1gMPPCA/Pz+NGzcuT0VNnz5dERERGjx4sOrWratZs2bJy8tLc+bMybR/qVKlVL58eetj1apV8vLyIrgDAAAAAIo9l7zMVLVqVS1btkx9+/bV3Llz9dlnn1mfMwxDFStW1MKFC1W1atVcLzs5OVm7d++2Cf1OTk7q0KGDtm7d6tAyPvnkE/Xt21fe3t65Xj8AAAAAAGaSp+AuSa1atdKxY8f0ww8/aMeOHYqLi1OJEiUUFhamBx98UG5ubnla7oULF5Sammp3fnxAQIB+++23HOffsWOHDhw4kO2t6JKSkpSUlGSdjo+PlySlpKQoJSUlT3XfTp6eRV2B+Xh6/r3dUnhz7JlsTLOJ7DF+c2CiMcwmssf4zQHj19QYvzlg/JoeYzgbJhq/WclN9rQYhmEUYi25dvr0aQUFBWnLli0KDw+3to8ZM0YbNmzQ9u3bs53/qaee0tatW/XLL79k2ScqKkpTpkyxa583b568vLzyXjwAAAAAAA5ITExUv379FBcXJz8/v2z75nmPe2bSb/+W063islOmTBk5Ozvr3LlzNu3nzp1T+fLls503ISFBX331lV566aVs+40bN06RkZHW6fj4eFWqVEmdOnXK8Q0zA3//oq7AfDw9UzRnzip1HDJErtevF3U55hIXV9QV2GD82mP85sBEY5jxa4/xmwPGr6kxfnPA+DU9xnA2TDR+s5J+5LcjHA7u58+f1/79+1WrVi0FBQXZPLdz5049+eST1r3cderU0cyZM3Xfffc5XEg6Nzc3NW3aVGvWrFHPnj0lSWlpaVqzZo2GDx+e7bxff/21kpKS9M9//jPbfu7u7nJ3d7drd3V1laura65rvt34TGbN9fp1vrQyMtmYZvNkjfGbBRONYTZP1hi/WWD8FguM3ywwfosNxnAmTDR+s5Kb7OnwVeVnzpypjh076sqVKzbtZ8+eVefOnbVv3z65ubnJ29tbBw8e1P33368//vjD4UJuFRkZqY8++kifffaZDh06pGeeeUYJCQkaPHiwJGnAgAGZXrH+k08+Uc+ePVW6dOk8rRcAAAAAALNxOLhv3LhRtWrVUr169Wza3377bV25ckWPPPKILl++rLi4OM2YMUOJiYmaMWNGnorq06ePpk6dqkmTJqlRo0aKjo7WihUrrBesO3nypM6cOWMzz+HDh7Vp0yY98cQTeVonAAAAAABm5PCh8kePHlWHDh3s2pcsWSJnZ2e999578vDwkCQ999xz+vzzz7V27do8FzZ8+PAsD41fv369XVutWrVksuvsAQAAAACQbw7vcT9//rwCAwNt2q5evapDhw6padOmKleunM1z99xzj06cOFEwVQIAAAAAcJdyOLhLsju/fc+ePTIMQ82aNbPrW6JECaWmpuarOAAAAAAA7nYOB/eqVatqy5YtNm3r1q2TxWLRPffcY9c/NjbWek46AAAAAADIG4eDe7du3RQdHa3XXntN8fHx2rVrl2bNmiU3Nzd17drVrv/27dtVrVq1Ai0WAAAAAIC7jcPBfcyYMQoKCtLEiRNVsmRJtWjRQrGxsRoxYoRKlSpl0/fw4cPav3+/2rdvX+AFAwAAAABwN3H4qvKlSpXS1q1bNWnSJG3btk2lS5dW7969M73y+7JlyxQaGqru3bsXaLEAAAAAANxtHA7ukhQUFKRPPvkkx36RkZGKjIzMc1EAAAAAAOBvubqqPAAAAAAAuL0I7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZWaMF99OjRql69emEtHgAAAACAu0KhBfcLFy4oJiamsBYPAAAAAMBdgUPlAQAAAAAwMRdHOw4ZMiRXC960aVOuiwEAAAAAALYcDu5z586VxWKRYRgOL9xiseSpKAAAAAAA8DeHg7ufn5+CgoI0Z84ch/q//PLL+vHHH/NcGAAAAAAAyEVwb9KkiXbu3KmwsDCH9qSXLVs2X4UBAAAAAIBcXJyuSZMmSkxM1G+//VaY9QAAAAAAgFs4vMe9R48eunjxoq5fv+5Q/6FDh6pt27Z5rQsAAAAAACgXwf2+++7Tfffd5/CCW7ZsqZYtW+apKAAAAAAA8Dfu4w4AAAAAgIk5HNzj4+OVnJxcmLUAAAAAAIAMHA7uJUuW1JtvvmnTtn37dr3zzjsFXhQAAAAAAPibw8HdMAwZhmHTtmLFCo0cObLAiwIAAAAAAH/jHHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJuaSm87/+9//tG3bNuv00aNHJUldu3bNtL/FYtGyZcvyUR4AAAAAAHe3XAX3o0ePWsP6rVasWJFpf4vFkreqAAAAAACApFwE9+PHjxdmHQAAAAAAIBMOB/cqVaoUZh0AAAAAACATXJwOAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJmbK4P7+++8rODhYHh4eatGihXbs2JFt/ytXrmjYsGEKDAyUu7u7atasqeXLl9+magEAAAAAKDwuRV1ARgsWLFBkZKRmzZqlFi1aaMaMGercubMOHz6scuXK2fVPTk5Wx44dVa5cOX3zzTcKCgrSiRMnVKJEidtfPAAAAAAABcx0wX369OmKiIjQ4MGDJUmzZs3SsmXLNGfOHI0dO9au/5w5c3Tp0iVt2bJFrq6ukqTg4ODbWTIAAAAAAIXGVME9OTlZu3fv1rhx46xtTk5O6tChg7Zu3ZrpPIsXL1Z4eLiGDRumH374QWXLllW/fv304osvytnZOdN5kpKSlJSUZJ2Oj4+XJKWkpCglJaUAX1Hh8PQs6grMx9Pz7+2Wwptjz2Rjmk1kj/GbAxONYTaRPcZvDhi/psb4zQHj1/QYw9kw0fjNSm6yp8UwDKMQa8mV06dPKygoSFu2bFF4eLi1fcyYMdqwYYO2b99uN0/t2rUVExOj/v3769lnn9XRo0f17LPP6rnnntPkyZMzXU9UVJSmTJli1z5v3jx5eXkV3AsCAAAAACATiYmJ6tevn+Li4uTn55dtX1Ptcc+LtLQ0lStXTh9++KGcnZ3VtGlT/fXXX3rrrbeyDO7jxo1TZGSkdTo+Pl6VKlVSp06dcnzDzMDfv6grMB9PzxTNmbNKHYcMkev160VdjrnExRV1BTYYv/YYvzkw0Rhm/Npj/OaA8WtqjN8cMH5NjzGcDRON36ykH/ntCFMF9zJlysjZ2Vnnzp2zaT937pzKly+f6TyBgYFydXW1OSy+Tp06Onv2rJKTk+Xm5mY3j7u7u9zd3e3aXV1drefJmxmfyay5Xr/Ol1ZGJhvTbJ6sMX6zYKIxzObJGuM3C4zfYoHxmwXGb7HBGM6EicZvVnKTPU11Ozg3Nzc1bdpUa9assbalpaVpzZo1NofO36ply5Y6evSo0tLSrG2///67AgMDMw3tAAAAAAAUJ6YK7pIUGRmpjz76SJ999pkOHTqkZ555RgkJCdarzA8YMMDm4nXPPPOMLl26pBEjRuj333/XsmXL9Nprr2nYsGFF9RIAAAAAACgwpjpUXpL69Omj8+fPa9KkSTp79qwaNWqkFStWKCAgQJJ08uRJOTn93+8NlSpV0sqVKzVy5Eg1bNhQQUFBGjFihF588cWiegkAAAAAABQY0wV3SRo+fLiGDx+e6XPr16+3awsPD9e2bdsKuSoAAAAAAG4/0x0qDwAAAAAA/g/BHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEzNtcH///fcVHBwsDw8PtWjRQjt27Miy79y5c2WxWGweHh4et7FaAAAAAAAKhymD+4IFCxQZGanJkydrz549Cg0NVefOnRUbG5vlPH5+fjpz5oz1ceLEidtYMQAAAAAAhcOUwX369OmKiIjQ4MGDVbduXc2aNUteXl6aM2dOlvNYLBaVL1/e+ggICLiNFQMAAAAAUDhcirqAjJKTk7V7926NGzfO2ubk5KQOHTpo69atWc537do1ValSRWlpaWrSpIlee+011atXL9O+SUlJSkpKsk7Hx8dLklJSUpSSklJAr6TweHoWdQXm4+n593ZL4c2xZ7IxzSayx/jNgYnGMJvIHuM3B4xfU2P85oDxa3qM4WyYaPxmJTfZ02IYhlGIteTa6dOnFRQUpC1btig8PNzaPmbMGG3YsEHbt2+3m2fr1q06cuSIGjZsqLi4OE2dOlUbN27Ur7/+qooVK9r1j4qK0pQpU+za582bJy8vr4J9QQAAAAAAZJCYmKh+/fopLi5Ofn5+2fY13R73vAgPD7cJ+ffee6/q1Kmj2bNn6+WXX7brP27cOEVGRlqn4+PjValSJXXq1CnHN8wM/P2LugLz8fRM0Zw5q9RxyBC5Xr9e1OWYS1xcUVdgg/Frj/GbAxONYcavPcZvDhi/psb4zQHj1/QYw9kw0fjNSvqR344wXXAvU6aMnJ2dde7cOZv2c+fOqXz58g4tw9XVVY0bN9bRo0czfd7d3V3u7u6Zzufq6pr7om8zPpNZc71+nS+tjEw2ptk8WWP8ZsFEY5jNkzXGbxYYv8UC4zcLjN9igzGcCRON36zkJnua7uJ0bm5uatq0qdasWWNtS0tL05o1a2z2qmcnNTVV+/fvV2BgYGGVCQAAAADAbWG6Pe6SFBkZqYEDB6pZs2YKCwvTjBkzlJCQoMGDB0uSBgwYoKCgIL3++uuSpJdeekn33HOPatSooStXruitt97SiRMnNHTo0KJ8GQAAAAAA5Jspg3ufPn10/vx5TZo0SWfPnlWjRo20YsUK6y3eTp48KSen/ztY4PLly4qIiNDZs2dVsmRJNW3aVFu2bFHdunWL6iUAAAAAAFAgTBncJWn48OEaPnx4ps+tX7/eZvq///2v/vvf/96GqgAAAAAAuL1Md447AAAAAAD4PwR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMzLTB/f3331dwcLA8PDzUokUL7dixw6H5vvrqK1ksFvXs2bNwCwQAAAAA4DYwZXBfsGCBIiMjNXnyZO3Zs0ehoaHq3LmzYmNjs50vJiZGo0aN0n333XebKgUAAAAAoHCZMrhPnz5dERERGjx4sOrWratZs2bJy8tLc+bMyXKe1NRU9e/fX1OmTFG1atVuY7UAAAAAABQel6IuIKPk5GTt3r1b48aNs7Y5OTmpQ4cO2rp1a5bzvfTSSypXrpyeeOIJ/fzzz9muIykpSUlJSdbpuLg4SdKlS5eUkpKSz1dQ+Dw8iroC8/HwSFFiYqIuenjI1TCKuhxzuXixqCuwwfi1x/jNgYnGMOPXHuM3B4xfU2P85oDxa3qM4WyYaPxm5erVq5Ikw4FtZ7rgfuHCBaWmpiogIMCmPSAgQL/99lum82zatEmffPKJoqOjHVrH66+/rilTpti1V61aNdf1whxu3JD69SvqKkyqTJmirgA5YPzmgDFsaozfHDB+TY3xmwPGr+kxhrNRjMbv1atX5e/vn20f0wX33Lp69aoef/xxffTRRyrj4MYZN26cIiMjrdNpaWm6dOmSSpcuLYvFUlilohDFx8erUqVKOnXqlPz8/Iq6HCBXGL8ozhi/KM4YvyjuGMPFm2EYunr1qipUqJBjX9MF9zJlysjZ2Vnnzp2zaT937pzKly9v1/+PP/5QTEyMunfvbm1LS0uTJLm4uOjw4cOqXr26zTzu7u5yd3e3aStRokQBvQIUJT8/P760UGwxflGcMX5RnDF+UdwxhouvnPa0pzPdxenc3NzUtGlTrVmzxtqWlpamNWvWKDw83K5/7dq1tX//fkVHR1sfDz74oNq1a6fo6GhVqlTpdpYPAAAAAECBMt0ed0mKjIzUwIED1axZM4WFhWnGjBlKSEjQ4MGDJUkDBgxQUFCQXn/9dXl4eKh+/fo286fvPc/YDgAAAABAcWPK4N6nTx+dP39ekyZN0tmzZ9WoUSOtWLHCesG6kydPysnJdAcLoAi5u7tr8uTJdqdAAMUB4xfFGeMXxRnjF8UdY/juYTEcufY8AAAAAAAoEuy2BgAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gD/19wcLBmzJiRbR+LxaLvv//+ttSDO8/69etlsVh05cqVQltHTmM0JiZGFotF0dHRhVYD7jxRUVFq1KhRoS3fkc/G3LlzrXeNAbJS2GM1M46MzaKoC8UP4xfZIbij0G3cuFHdu3dXhQoV8hR827Ztq+eff96hfhaLxe5x8+bNvBWOu9brr7+u5s2by9fXV+XKlVPPnj11+PDh27Lu4OBguzFcsWLF27Ju3Flmzpyphg0bys/PT35+fgoPD9ePP/5YKOtK/0Eo4+Of//xnoawPd5c33nhDFovF7m+BgvoxPf2Ho4yPCRMm5HvZuDtFRUXZjafatWvb9GH8IrdMeTs43FkSEhIUGhqqIUOG6KGHHirUdUVEROill16yaXNxYZgjdzZs2KBhw4apefPmunnzpv7973+rU6dOOnjwoLy9vQt9/S+99JIiIiKs087OzoW+Ttx5KlasqDfeeEMhISEyDEOfffaZevToob1796pevXqZzpOSkiJXV9c8r3P16tU2y/b09MzzsgBJ2rlzp2bPnq2GDRsW+roOHz4sPz8/67SPj0+hrxN3rnr16mn16tXW6cL+e5Txe+djjzsK3f33369XXnlFvXr1yrLPBx98oJCQEHl4eCggIECPPPKIJGnQoEHasGGD3n77besviDExMVkux8vLS+XLl7d5pFu0aJHq1asnd3d3BQcHa9q0adnWfeTIEbVu3VoeHh6qW7euVq1albsXjmJrxYoVGjRokOrVq6fQ0FDNnTtXJ0+e1O7du619LBaLPv74Y/Xq1UteXl4KCQnR4sWLbZazfPly1axZU56enmrXrl22Y/dWvr6+NmO4bNmy1udmzpyp6tWry83NTbVq1dIXX3yR7bJ27Nihxo0by8PDQ82aNdPevXsdfyNQrHXv3l1du3ZVSEiIatasqVdffVU+Pj7atm2btY/FYtHMmTP14IMPytvbW6+++qqkv/dwBgQEyNfXV0888YRu3Ljh0DpLly5tM3b9/f0lSUlJSXruuedUrlw5eXh4qFWrVtq5c2e2y5o7d64qV64sLy8v9erVSxcvXszjO4Hi6tq1a+rfv78++ugjlSxZ0ua54OBgSVKvXr1ksVis0+m++OILBQcHy9/fX3379tXVq1dzXF+5cuVsxm968Ll8+bIGDBigkiVLysvLS/fff7+OHDmS7bLy+hnCncPFxcVmPJUpU8b6HOMXeUFwR5HbtWuXnnvuOb300ks6fPiwVqxYodatW0uS3n77bYWHhysiIkJnzpzRmTNnVKlSpVyvY/fu3erdu7f69u2r/fv3KyoqShMnTtTcuXMz7Z+WlqaHHnpIbm5u2r59u2bNmqUXX3wxPy8TxVhcXJwkqVSpUjbtU6ZMUe/evfXLL7+oa9eu6t+/vy5duiRJOnXqlB566CF1795d0dHRGjp0qMaOHZuvOr777juNGDFCL7zwgg4cOKCnnnpKgwcP1rp16zLtf+3aNXXr1k1169bV7t27FRUVpVGjRuWrBhRPqamp+uqrr5SQkKDw8HCb56KiotSrVy/t379fQ4YM0cKFCxUVFaXXXntNu3btUmBgoD744IN8rX/MmDFatGiRPvvsM+3Zs0c1atRQ586drZ+XjLZv364nnnhCw4cPV3R0tNq1a6dXXnklXzWg+Bk2bJgeeOABdejQwe659B9+Pv30U505c8bmh6A//vhD33//vZYuXaqlS5dqw4YNeuONN/Jcx6BBg7Rr1y4tXrxYW7dulWEY6tq1q1JSUjLtXxifIRQ/R44cUYUKFVStWjX1799fJ0+etD7H+EWeGMBtJMn47rvvbNoWLVpk+Pn5GfHx8ZnO06ZNG2PEiBE5LrtNmzaGq6ur4e3tbX1ERkYahmEY/fr1Mzp27GjTf/To0UbdunWt01WqVDH++9//GoZhGCtXrjRcXFyMv/76y/r8jz/+mGn9uLOlpqYaDzzwgNGyZUubdknGhAkTrNPXrl0zJBk//vijYRiGMW7cOJvxZRiG8eKLLxqSjMuXL2e5vipVqhhubm424/jtt982DMMw7r33XiMiIsKm/6OPPmp07drVpq70MTp79myjdOnSxvXr163Pz5w505Bk7N271+H3AMXXL7/8Ynh7exvOzs6Gv7+/sWzZMpvnJRnPP/+8TVt4eLjx7LPP2rS1aNHCCA0NzXI9x48fNyQZnp6eNmN3z549xrVr1wxXV1fjyy+/tPZPTk42KlSoYPznP/8xDMMw1q1bZ/PZeOyxx2zGtWEYRp8+fQx/f/9cvgMorubPn2/Ur1/f+v2V2d8Cmf0/efLkyYaXl5fN3xSjR482WrRokeW60sffrWPX29vbuHDhgvH7778bkozNmzdb+1+4cMHw9PQ0Fi5caBiGYXz66ac2YzMvnyHcWZYvX24sXLjQ2Ldvn7FixQojPDzcqFy5ss24ZPwitzj5F0WuY8eOqlKliqpVq6YuXbqoS5cu1sOPc6t///4aP368dTr9KpmHDh1Sjx49bPq2bNlSM2bMUGpqqt05xIcOHVKlSpVUoUIFa1vGvVS4OwwbNkwHDhzQpk2b7J679ZxLb29v+fn5KTY2VtLfY6hFixY2/R0dQ6NHj9agQYOs0+mH1x06dEhPPvmkTd+WLVvq7bffznQ5hw4dUsOGDeXh4ZHrGnBnqFWrlqKjoxUXF6dvvvlGAwcO1IYNG1S3bl1rn2bNmtnMc+jQIT399NM2beHh4Vke2XGrBQsWqE6dOtbpSpUq6fDhw0pJSVHLli2t7a6urgoLC9OhQ4cyXc6hQ4fsTq8KDw/XihUrcqwBxd+pU6c0YsQIrVq1yub7y1HBwcHy9fW1TgcGBlq/m7Pz888/28xXsmRJbd68WS4uLjbf56VLl1atWrWyHb95/QzhznD//fdb/92wYUO1aNFCVapU0cKFC/XEE09kOy/jF1khuKPI+fr6as+ePVq/fr1++uknTZo0SVFRUdq5c2eub/3j7++vGjVqFE6huOsMHz5cS5cu1caNGzO9snvGi3hZLBalpaXle71lypRhHKNAuLm5WcdS06ZNtXPnTr399tuaPXu2tU9BXnCxUqVKjF3k2+7duxUbG6smTZpY21JTU7Vx40a99957SkpKyvainXn9bq5atSq3HEShKFGihGrWrKmjR4/m2Jfxi6xwjjtMwcXFRR06dNB//vMf/fLLL4qJidHatWsl/f2HZ2pqar6WX6dOHW3evNmmbfPmzapZs2am//OvU6eOTp06pTNnzljbbr2gE+5shmFo+PDh+u6777R27VpVrVo118uoU6eOduzYYdOW3zGU1Ti+de9pxv6//PKLzUVlGMd3t7S0NCUlJWXbp06dOtq+fbtNW37GTfrFFG8duykpKdq5c2e2Y7cga0Dx8o9//EP79+9XdHS09dGsWTP1799f0dHR1v9vu7q65vvvg5zUqVNHN2/etBmPFy9e1OHDhxm/cNi1a9f0xx9/KDAw0NrG+EVusccdhe7atWs2vzAeP35c0dHRKlWqlCpXrqylS5fq2LFjat26tUqWLKnly5crLS1NtWrVkvT3IUPbt29XTEyMfHx8VKpUKTk55e43pxdeeEHNmzfXyy+/rD59+mjr1q167733srzYRocOHVSzZk0NHDhQb731luLj420OwcedbdiwYZo3b55++OEH+fr66uzZs5L+PqLD0dtbPf3005o2bZpGjx6toUOHavfu3VleDNFRo0ePVu/evdW4cWN16NBBS5Ys0bfffmtzu5lb9evXT+PHj1dERITGjRunmJgYTZ06NV81oPgYN26c7r//flWuXFlXr17VvHnztH79eq1cuTLb+UaMGKFBgwapWbNmatmypb788kv9+uuvqlatWp7q8Pb21jPPPKPRo0dbv/f/85//KDExMctDRp977jm1bNlSU6dOVY8ePbRy5UoOk7+L+Pr6qn79+jZt3t7eKl26tE17cHCw1qxZo5YtW8rd3d3uyvMFISQkRD169FBERIRmz54tX19fjR07VkFBQXan4KUr6M8Qip9Ro0ape/fuqlKlik6fPq3JkyfL2dlZjz32mLUP4xe5xR53FLpdu3apcePGaty4sSQpMjJSjRs31qRJkyT9ffjQt99+q/bt26tOnTqaNWuW5s+fb70X8KhRo+Ts7Ky6deuqbNmyNlfldFSTJk20cOFCffXVV6pfv74mTZqkl156yeY84ls5OTnpu+++0/Xr1xUWFqahQ4dab5OEO9/MmTMVFxentm3bKjAw0PpYsGCBw8uoXLmyFi1apO+//16hoaGaNWuWXnvttXzV1bNnT7399tuaOnWq6tWrp9mzZ+vTTz9V27ZtM+3v4+OjJUuWaP/+/WrcuLHGjx+vN998M181oPiIjY3VgAEDVKtWLf3jH//Qzp07tXLlSnXs2DHb+fr06aOJEydqzJgxatq0qU6cOKFnnnkmX7W88cYbevjhh/X444+rSZMmOnr0qFauXJnlH6r33HOPPvroI7399tsKDQ3VTz/9pAkTJuSrBtx5pk2bplWrVqlSpUrWvzEKw6effqqmTZuqW7duCg8Pl2EYWr58ud0hzekK4zOE4uXPP//UY489plq1aql3794qXbq0tm3bZnN7V8YvcstiGIZR1EUAAAAAAIDMsccdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAUovXr18tisSgqKqpQlm+xWNS2bdtCWTZyJyoqShaLRevXry/qUgAAdxiCOwDgjhMTEyOLxaIuXboUdSn51rZtW1ksltu+3vQQeuvDy8tL9evX1/jx4xUfH3/bawIA4G7lUtQFAABwJwsLC9OhQ4dUpkyZQln+oUOH5OXlVSjLlqSHH35Y9evXlySdO3dOy5cv12uvvaalS5dqx44dcnd3L7R1FzfDhw9X3759Vbly5aIuBQBwhyG4AwBQiLy8vFS7du1CW35hLluSHnnkEfXt29c6fePGDd1zzz3at2+f5s2bp8GDBxfq+ouTMmXKFNoPNACAuxuHygMA7nonTpzQE088oaCgILm5ualixYp64okndPLkyUz7//LLL+ratat8fX3l7++vrl276sCBAxo0aJAsFotiYmKsfbM6x/3IkSMaPHiwqlatKnd3d5UqVUqhoaF6/vnnZRiGpL/PX9+wYYP13+mPQYMGWZeT1TnuycnJ+u9//6vmzZvL19dXPj4+qlu3riIjI3X58uU8v1ceHh7q37+/JGn37t12zx8/flxDhw5V5cqV5e7ursDAQA0aNEgnTpzIdHnffvutmjVrJk9PTwUEBCgiIkKXL19WcHCwgoODbfqmv7/Hjh3TtGnTVLduXbm7u9u8H7GxsRo5cqRq1Kghd3d3lSlTRg8//LAOHDhgt25HtoEknTlzRiNGjFBISIg8PT1VokQJ1alTR08//bTi4uKs/bI7x33JkiVq166d/P395enpqdDQUE2fPl03b9606Zd+msegQYN09OhR9erVSyVLlpS3t7c6dOigffv2Zfo+AgDubOxxBwDc1X7//Xe1atVK58+fV/fu3VWvXj0dOHBAc+bM0ZIlS7Rp0ybVrFnT2n/fvn267777lJCQoIceekghISHatWuXWrVqpdDQUIfWefr0aYWFhSkhIUEPPPCA+vTpo4SEBB05ckQffPCBpk6dKhcXF02ePFlz587ViRMnNHnyZOv8jRo1ynb5169fV8eOHbV582aFhIRo8ODBcnd315EjRzR79mwNGDBAJUuWzNP7dSsXF9s/I7Zv367OnTsrISFB3bp1U0hIiGJiYvTll1/qxx9/1NatW1WtWjVr/zlz5uiJJ56Qn5+fBgwYIH9/fy1fvlwdO3ZUSkqKXF1dM13vv/71L23btk0PPPCAunfvrnLlykmS/vjjD7Vt21Z//vmnOnXqpJ49eyo2NlaLFi3SypUrtWbNGrVo0UKS49sgMTFRLVu2VExMjDp16qRevXopOTlZx48f1xdffKFRo0bJ398/2/dp+vTpeuGFF1SqVCn169dP3t7eWrx4sV544QX9/PPP+vbbb+2uYxATE6N77rlH9erV05AhQ/THH3/ohx9+ULt27XTo0CEFBATkensBAIoxAwCAO8zx48cNSUbnzp1z7NuuXTtDkjF79myb9vfff9+QZLRv396mvVWrVoYk48svv7RpnzhxoiHJkGQcP37c2r5u3TpDkjF58mRr2zvvvGNIMmbMmGFXz8WLF22m27RpY2T3v2tJRps2bWzaXnjhBUOS8fjjjxs3b960ee7KlSvG1atXs1xeusmTJxuSjPnz59u0X79+3QgNDTUkGV9//bW1PTk52QgODjZ8fX2NPXv22Mzz888/G87Ozka3bt2sbZcvXzZ8fHwMb29v4/fff7e2p6SkGO3btzckGVWqVLFZzsCBAw1JRsWKFY0TJ07Y1Xzvvfcazs7OxooVK2zaDx8+bPj6+hoNGjSwtjm6DRYvXmxIMp5//nm7flevXjVu3LhhnU5/z9atW2dtO3r0qOHi4mKUK1fOOHnypLX9xo0b1rH0+eefW9vTx64k44033rBZ34QJEwxJxuuvv25XCwDgzsah8gCAu9bJkye1bt061a1bVxERETbPPf3006pdu7bWrl2rU6dOSfr7kPpNmzYpNDRU/fr1s+n/4osv5novtqenp11bqVKlcvkqbN28eVMffvih/P399fbbb8vZ2dnmeX9/f/n4+Di8vG+++UZRUVGKiorSs88+q1q1amnfvn3q1auXHnroIWu/pUuXKiYmRqNHj1bjxo1tltGqVSv16NFDy5cvt16N/ocfftC1a9f0xBNPKCQkxNrXxcVFr7zySrY1jR492u4CcHv37tWWLVs0cOBAde7c2ea5mjVrKiIiQvv377c7ZN7RbZBZPx8fnxwvzjdv3jzdvHlTL7zwgipVqmRtd3d315tvvilJmjt3rt18VatW1ejRo23annjiCUnSzp07s10nAODOw6HyAIC7VnR0tCSpTZs2docqOzk5qXXr1vrtt98UHR2tSpUqWc8vbtmypd2yvL291ahRI61bty7H9Xbv3l3jxo3TsGHDtGbNGnXp0kVt2rSxOYw8r3777TddvXpVHTp0KJDD4RctWqRFixbZtD366KNasGCBzXu2bds2SdLhw4czvWf92bNnlZaWpt9//13NmjWzvpetWrWy69uiRQu7w/BvFRYWZteWvv5z585luv7ffvvN+t/69es7vA1at26twMBAvfHGG9q3b5+6deumNm3aqE6dOg7dpm/v3r2SlOl1CMLDw+Xh4WEdh7dq1KiRnJxs969UrFhRknTlypUc1wsAuLMQ3AEAd630vb9ZnS8cGBho0y/9v+nnVGfk6HnHwcHB2rZtm6KiorR8+XItXLhQ0t9XiH/ppZf06KOPOv4iMki/WFpQUFCel3Gr+fPnq2/fvrp586YOHz6sUaNG6euvv1atWrX08ssvW/tdunRJkvTll19mu7yEhARJ2b+XTk5O2V6dPbP3OX39y5Yt07Jly3Jcv6PbwN/fX9u2bdOkSZO0ZMkSLV++XJJUqVIljR07Vs8++2y2rze7MWaxWBQQEKC//vrL7jk/Pz+7tvQfM1JTU7NdJwDgzsOh8gCAu1Z6ODp37lymz589e9amX/p/Y2NjM+2f1XIyU79+fX3zzTe6dOmStm7dqkmTJuns2bPq06ePNm/e7PByMipRooQkZRoG88PFxUX16tXTd999pxo1aujVV1/Vnj17rM+nvzdLliyRYRhZPtq0aWPTP7P3Mi0tTRcuXMiylsz2dKcv79133812/QMHDrTO4+g2qFy5subOnavz589r7969evPNN5WWlqZhw4Zp/vz52b5v2Y0xwzB07ty5TEM6AAC3IrgDAO5a6Vdn37hxo83tv6S/Q9XGjRtt+qVfNX7Lli12y0pMTMzTrbpcXV11zz33aMqUKXrnnXdkGIaWLl1qfT79HHVH97LWqlVLfn5+2rlzZ75u+5YVDw8PTZ06VYZhaOzYsdb29Ku1b9261aHlpL+Xmf1IsWPHDrvbpOUkt+u/VU7bIJ2Tk5MaNWqkMWPGWAP74sWLs112+vn+md0ibvv27bpx40aOdwkAAIDgDgC4a1WuXFnt2rXTr7/+qjlz5tg89+GHH+rQoUNq37699aJiVapUUcuWLRUdHa0FCxbY9H/rrbesh2vnZPfu3dZDqG+VvlfWw8PD2pZ+obT0C+TlxMXFRU899ZTi4uI0YsQIu8AfFxena9euObSsrPTo0UNNmjTRqlWr9PPPP1vbKleurOnTp1t/8LhVSkqKNm3aZLMMHx8fffLJJ/rjjz+s7Tdv3tTEiRNzXVNYWJhatGih+fPn220b6e+9+Bs2bLBOO7oNfv3110z3lme2rTLTr18/ubi4aPr06Tp9+rS1PTk5WS+++KIk2dyHHgCAzHCOOwDgjrV///4sQ1Ht2rU1duxYzZw5U61atVJERISWLFmiunXr6tdff9XixYtVtmxZzZw502a+d999V61bt1b//v21aNEi1ahRQ3v27NG2bdvUunVrbdy40e6iYhl98cUXmj17tlq3bq3q1avLz89PBw8e1PLly1WqVCkNHjzY2rd9+/b65ptv9PDDD+v++++Xh4eHQkND1b179yyX/9JLL2nbtm364osvtG3bNt1///1yd3fXsWPHtGLFCm3atCnfe3mjoqL04IMPatKkSVq3bp3c3d31zTff6P7771ebNm3Uvn17NWjQQBaLRSdOnNDPP/+s0qVLWy8SV6JECU2fPl1PPvmkmjZtqr59+1rv4+7u7q4KFSrk+D5mNH/+fLVr1059+/bVjBkz1KRJE3l6eurkyZPaunWrzp8/rxs3bkhyfBusWrVKo0ePVsuWLVWzZk2VLl1ax44d0+LFi+Xh4aFhw4ZlW1P16tX15ptv6oUXXlDDhg3Vu3dveXt7a8mSJTp8+LB69Oihf/7zn3nYAgCAu8rtvfscAACF79Z7YWf1uPXe5zExMcbgwYONwMBAw8XFxQgMDDQGDx5sxMTEZLr8vXv3Gp07dzZ8fHwMX19f4/777zf2799vdOvWzZBkXL582do3s/u4b9u2zXjqqaeM+vXrGyVKlDA8PT2NkJAQY/jw4Xb3J09JSTHGjBljVK5c2XBxcTEkGQMHDrQ+n/G1pLtx44YxdepUo1GjRoanp6fh4+Nj1K1b13jhhRds6stKVvdxv1WzZs0MScaaNWusbX/++acxYsQIIyQkxHB3dzf8/PyMOnXqGEOHDrXpl+7rr782GjdubLi7uxvlypUzhg4daly8eNHw8fExQkNDbfqm38f9+PHjWdZ06dIlY8KECUb9+vWtrzskJMTo16+f8e2331r7OboNDh48aIwYMcJo3LixUbp0acPd3d2oVq2aMXDgQOPXX3/N9D279T7u6X744QejTZs2hq+vr+Hu7m40aNDAmDZtmpGSkmLTL33s3rqNb5XV9gYA3NkshpHhpD4AAJBrqampql69uq5fv56ri9TB3tGjRxUSEqLevXtnetg7AAB3G85xBwAgF27evJnpFc/feOMNnThxQj179rz9RRVTly9fVlJSkk3b9evXNXLkSEnivQQA4P9jjzsAALlw5coVBQQEqGPHjqpZs6ZSUlK0fft27dy5U4GBgdq9e7f1/u/I3vfff68nnnhCnTp1UuXKlXXhwgWtXbtWMTExat++vVatWpXr89wBALgTEdwBAMiF5ORkPf/881q7dq1Onz6tGzduKDAwUPfff78mTpyooKCgoi6x2Dhy5IgmTpyoLVu26Pz585KkGjVqqE+fPho1alSOV2wHAOBuQXAHAAAAAMDEOP4MAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAAT+3+6duGQqSTxkwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9649122807017544, 'precision_score': 0.9671497584541062, 'recall_score': 0.9606224198158145, 'f1_score': 0.9635549872122762}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.99      0.97        67\n",
            "           1       0.98      0.94      0.96        47\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.97      0.96      0.96       114\n",
            "weighted avg       0.97      0.96      0.96       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Training with KNN"
      ],
      "metadata": {
        "id": "xHwcxFjnLZUn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use GridSearchCV to find the best parameters\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn_model_2 = KNeighborsClassifier()\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'n_neighbors':np.arange(1,15),\n",
        "               'metric':['minkowski'],\n",
        "               'p':np.arange(1,4)}\n",
        "              ]\n",
        "# n_jobs=-1 uses all the system's processors\n",
        "grid_search = GridSearchCV(estimator=knn_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(X_train, Y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best F1 score {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0i4KlDpLb3U",
        "outputId": "c5a967f5-35ec-4e81-ea34-bad60c42ed23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 score 95.90 %\n",
            "Best Parameters: {'metric': 'minkowski', 'n_neighbors': 4, 'p': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn_model = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=1)\n",
        "knn_train_val_result = cross_validation(knn_model, X_train, Y_train, 5)\n",
        "print(knn_train_val_result)\n",
        "\n",
        "model_name = \"KNN\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=knn_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=knn_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "knn_model.fit(X_train, Y_train)\n",
        "knn_y_pred = knn_model.predict(X_test)\n",
        "# Get test result\n",
        "knn_test_result = evaluate_model(Y_test, knn_y_pred)\n",
        "print(lr_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(Y_test, knn_y_pred, label)\n",
        "print(classification_report(Y_test, knn_y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YKSaUXyFMP-y",
        "outputId": "b866ee8b-4c00-4de9-bcc3-8ec2cb894b71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.97527473, 0.98076923, 0.98351648, 0.96703297, 0.97802198]), 'Mean Training Accuracy': 97.69230769230768, 'Training Precision scores': array([0.97674419, 0.98449612, 0.98461538, 0.98387097, 0.97692308]), 'Mean Training Precision': 0.9813299478715832, 'Training Recall scores': array([0.95454545, 0.96212121, 0.96969697, 0.92424242, 0.96212121]), 'Mean Training Recall': 0.9545454545454547, 'Training F1 scores': array([0.96551724, 0.97318008, 0.97709924, 0.953125  , 0.96946565]), 'Mean Training F1 Score': 0.9676774407007693, 'Validation Accuracy scores': array([0.94505495, 0.96703297, 0.94505495, 0.98901099, 0.97802198]), 'Mean Validation Accuracy': 96.48351648351647, 'Validation Precision scores': array([0.9375    , 0.96875   , 0.96666667, 1.        , 0.96969697]), 'Mean Validation Precision': 0.9685227272727273, 'Validation Recall scores': array([0.90909091, 0.93939394, 0.87878788, 0.96969697, 0.96969697]), 'Mean Validation Recall': 0.9333333333333333, 'Validation F1 scores': array([0.92307692, 0.95384615, 0.92063492, 0.98461538, 0.96969697]), 'Mean Validation F1 Score': 0.9503740703740704}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9649122807017544, 'precision_score': 0.9671497584541062, 'recall_score': 0.9606224198158145, 'f1_score': 0.9635549872122762}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.99      0.96        67\n",
            "           1       0.98      0.91      0.95        47\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.96      0.95      0.95       114\n",
            "weighted avg       0.96      0.96      0.96       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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9qT/kb0+al7WGdOb5WQPSf0AZvy8AeKMBa1Xz7H8NyO3fGZYzrv5tb5cDrEZKESIOP/zwfOtb3+p+PWzYsOy444756le/mm222aYXK+OD6vpp62Wdkcty0kVzus+1jVrW45oZ56+f/7PHohx92tzucyM37HkNQJk9/NOWPPzTlt4uA0rF6kyNKc2D1XvvvXfmzp2buXPn5s4770zfvn3zyU9+srfL4gPqwf+vNZtu+8ec89kNc/DWW+bYPTfNj64d1v1+tZr8/M6WfGjjjvzDIRvn4K23zOf33SQ/+3djAADAmq80IaK5uTltbW1pa2vLdtttl1NOOSVz5szJ73//+94ujQ+gubP759Zr1snIjTpy3nXP5pOT/pBLT9sgd1y/dpJkwX/3zWtL+uS731gvH9391Uz9zrPZZe+FOfvoDfOfDwzq5eoBAN5bpRhn+lOLFy/Ot7/97YwZMybDhw9f6TUdHR3p6Ojofr1o0aL3qzw+AGrVZJNtXsuRp64YVRqz9Wv57ZMD8sN/XSd7HvxKatUV142bsCgTP7si6H54q9fy+MOD8sNr1sk245b0VukAwLtgnKkxpelE3HrrrRk8eHAGDx6cIUOG5Oabb853v/vdNDWtvMSpU6emtbW1+2hvb3+fK2ZNNmy91zN606U9zrVvsjS/e7FfkqRlWGf69K295TUAAGuq0oSI3XffPY8++mgeffTR/PznP8+ECROyzz775Pnnn1/p9aeeemoWLlzYfcyZM2el18E7scWOSzLnmeYe5158tjnrfWjF0q39+tey6bZ/zAsru8byrgDAGq40IWLQoEEZM2ZMxowZkx133DFXXnlllixZkiuuuGKl1zc3N6elpaXHAavKxM/+Lk/+clC+c/F6efG5/vnJjUPzo28Pz/5H/Hf3NX997O9y981D86Nrh+XF5/rn365eJw/e0Zr9Jv33W3wyQHkMWKszG2/5WjbecsX+N23ty7Lxlq9l3Q9ZaY4Prq5xprIdZVPKZyKSpFKppKmpKa+99trbXwyr2GbbvZbTr3ou06eun2svbEtb+7Icc/aL2WPiK93X7LLPwnz+/Bcy8xsjculpG2SDjVdsNLfVTp6HAFYPm277Wv75+890vz7mrJeSJP/fd9fOBVNG9VZZwGqgNCGio6Mj8+bNS5K88sor+cY3vpHFixdnv/326+XK+KDaec9F2XnPt35gf8IhL2fCIS+/TxUBrFr/+cDgTBi5bW+XAayGShMibrvttqy//vpJkiFDhmTs2LG54YYbsttuu/VuYQAAfGCUcXyobPUkJQkRM2bMyIwZM3q7DAAAoAGlebAaAABYPZSiEwEAAGVQq1VSK9n4UNnqSXQiAACAgoQIAACgEONMAABQV00l1ZRrfKhs9SQ6EQAAQEFCBAAAUIhxJgAAqLPZXGN0IgAAgEKECAAAoBDjTAAAUGezucboRAAAAIUIEQAAQCHGmQAAoM7qTI3RiQAAAAoRIgAAgEKMMwEAQJ3VmRqjEwEAABQiRAAAAIUYZwIAgLpaCVdnMs4EAACs9oQIAACgEONMAABQV0tSq/V2FT2VrJwkOhEAAEBBQgQAAFCIcSYAAKirppJKyrUaUrVk9SQ6EQAAQEFCBAAAUIhxJgAAqKvVKqXb3K1s9SQ6EQAAQEFCBAAAUIhxJgAAqKvWKqmUbHyoWrJ6Ep0IAACgICECAAAoxDgTAADU1WorjjIpWz2JTgQAAFCQEAEAABRinAkAAOpsNtcYnQgAAKAQIQIAACjEOBMAANQZZ2qMTgQAAFCIEAEAABRinAkAAOqqtUoqJRsfqpasnkQnAgAAKEiIAAAACjHOBAAAdbXaiqNMylZPohMBAAAUJEQAAACFGGcCAIC6FeNM5VoNyTgTAACw2hMiAACAQowzAQBAXa1WKeE4U7nqSXQiAACAgoQIAACgEONMAABQV6sfZVK2ehKdCAAAoCAhAgAAKMQ4EwAA1FmdqTE6EQAAQCFCBAAAUIhxJgAA6GJ5poboRAAAAIUIEQAAQCHGmQAAoEsJV2dK2eqJTgQAAFCQEAEAABQiRAAAQF2tVs7jnTr//PNTqVRywgkndJ9bunRpJk+enOHDh2fw4ME56KCDMn/+/EKfK0QAAMAa6Be/+EUuv/zybLPNNj3OT5kyJbfccktuuOGG3H333XnppZcyceLEQp8tRAAAwBpm8eLFOfTQQ3PFFVdk7bXX7j6/cOHCXHXVVfmXf/mX7LHHHtlhhx0yffr0/OxnP8uDDz7Y8OcLEQAAUFerr85UtiNJFi1a1OPo6Oh40z/H5MmTs++++2b8+PE9zj/yyCNZvnx5j/Njx47NqFGj8sADDzT8cxIiAABgNdDe3p7W1tbuY+rUqSu9bubMmfnlL3+50vfnzZuX/v37Z+jQoT3OjxgxIvPmzWu4FvtEAADAamDOnDlpaWnpft3c3LzSa77whS/kjjvuyIABA96zWoQIAADoUquUb3O3ej0tLS09QsTKPPLII/nd736Xj3zkI93nOjs7c8899+Qb3/hGbr/99ixbtiwLFizo0Y2YP39+2traGi5JiAAAgDXEn//5n+exxx7rce6II47I2LFj86UvfSnt7e3p169f7rzzzhx00EFJklmzZmX27NkZN25cw/cRIgAAYA0xZMiQbLXVVj3ODRo0KMOHD+8+f9RRR+XEE0/MsGHD0tLSkuOPPz7jxo3Lzjvv3PB9hAgAAKh7t5u7vRdWdT0XXnhhmpqactBBB6WjoyMTJkzIJZdcUugzhAgAAFiD3XXXXT1eDxgwINOmTcu0adPe8Wda4hUAAChEJwIAALrU6keZlK2e6EQAAAAFCREAAEAhxpkAAKCuVqukVrLN5spWT6ITAQAAFCREAAAAhRhnAgCA/62EqyGVjU4EAABQiBABAAAUYpwJAADqrM7UGJ0IAACgECECAAAoxDgTAAB0qaV8qzOVrZ7oRAAAAAUJEQAAQCHGmQAAoFulfpRJ2erRiQAAAAoSIgAAgEKMMwEAQBerMzVEJwIAAChEiAAAAAoxzgQAAF2MMzVEJwIAAChEiAAAAAoxzgQAAF1qlRVHmZStnuhEAAAABQkRAABAIcaZAACgrlZbcZRJ2epJdCIAAICChAgAAKAQ40wAANDFZnMN0YkAAAAKESIAAIBCjDMBAEAXm801RCcCAAAoRIgAAAAKMc4EAAB1ldqKo0zKVk/SYIi4+eabG/7A/fff/x0XAwAAlF9DIeLAAw9s6MMqlUo6OzvfTT0AAEDJNRQiqtXqe10HAAD0PpvNNeRdPVi9dOnSVVUHAACwmigcIjo7O/OVr3wlH/rQhzJ48OA8++yzSZLTTjstV1111SovEAAAKJfCIeLcc8/NjBkz8tWvfjX9+/fvPr/VVlvlyiuvXKXFAQDA+6prs7myHSVTOERcc801+eY3v5lDDz00ffr06T6/7bbb5sknn1ylxQEAAOVTOES8+OKLGTNmzBvOV6vVLF++fJUUBQAAlFfhELHFFlvk3nvvfcP5733ve9l+++1XSVEAANAraiU9SqbwjtWnn356Jk2alBdffDHVajU33nhjZs2alWuuuSa33nrre1EjAABQIoU7EQcccEBuueWW/PjHP86gQYNy+umn54knnsgtt9ySPffc872oEQAAKJHCnYgk2XXXXXPHHXes6loAAKB3lXF8qGz15B2GiCR5+OGH88QTTyRZ8ZzEDjvssMqKAgAAyqtwiHjhhRdyyCGH5P7778/QoUOTJAsWLMif/dmfZebMmdlggw1WdY0AAECJFH4m4uijj87y5cvzxBNP5OWXX87LL7+cJ554ItVqNUcfffR7USMAALw/ensVpjV1daa77747P/vZz7LZZpt1n9tss83y9a9/PbvuuusqLQ4AACifwp2I9vb2lW4q19nZmZEjR66SogAAgPIqHCL++Z//Occff3wefvjh7nMPP/xwvvCFL+T//b//t0qLAwCA91WtUs6jZBoaZ1p77bVTqfxP8UuWLMlOO+2Uvn1X/PbXX389ffv2zZFHHpkDDzzwPSkUAAAoh4ZCxEUXXfQelwEAAKwuGgoRkyZNeq/rAACAXleprTjKpGz1JO9is7kkWbp0aZYtW9bjXEtLy7sqCAAAKLfCD1YvWbIkxx13XNZbb70MGjQoa6+9do8DAABYsxUOESeffHJ+8pOf5NJLL01zc3OuvPLKnHXWWRk5cmSuueaa96JGAAB4f/T2pnJr6mZzt9xyS6655prstttuOeKII7LrrrtmzJgxGT16dK699toceuih70WdAABASRTuRLz88svZeOONk6x4/uHll19OknzsYx/LPffcs2qrAwAASqdwiNh4443z3HPPJUnGjh2b66+/PsmKDsXQoUNXaXEAAED5FA4RRxxxRH71q18lSU455ZRMmzYtAwYMyJQpU/LFL35xlRcIAACUS+FnIqZMmdL96/Hjx+fJJ5/MI488kjFjxmSbbbZZpcUBAADl8672iUiS0aNHZ/To0auiFgAA6FWVlG9zt0pvF7ASDYWIiy++uOEP/PznP/+OiwEAAMqvoRBx4YUXNvRhlUql10PEX/2fXdK30r9XawBYVX73b+v3dgkAq0znHzuST/V2FawKDYWIrtWYAABgjVarrDjKpGz15B2szgQAAHywCREAAEAh73p1JgAAWGPU6keZlK2e6EQAAAAFCREAAEAh7yhE3HvvvTnssMMybty4vPjii0mSf/3Xf8199923SosDAID3Va2kR8kUDhHf//73M2HChAwcODD/8R//kY6OjiTJwoULc955563yAgEAgHIpHCLOOeecXHbZZbniiivSr1+/7vO77LJLfvnLX67S4gAAgPIpvDrTrFmz8vGPf/wN51tbW7NgwYJVURMAAPSKSm3FUSZlqyd5B52Itra2PP300284f99992XjjTdeJUUBAADlVThEfOYzn8kXvvCFPPTQQ6lUKnnppZdy7bXX5qSTTsrnPve596JGAACgRAqPM51yyimpVqv58z//8/zxj3/Mxz/+8TQ3N+ekk07K8ccf/17UCAAA748yroZUtnryDkJEpVLJP/7jP+aLX/xinn766SxevDhbbLFFBg8e/F7UBwAAlEzhENGlf//+2WKLLVZlLQAAwGqgcIjYfffdU6lU3vT9n/zkJ++qIAAA6DXGmRpSOERst912PV4vX748jz76aH79619n0qRJq6ouAACgpAqHiAsvvHCl588888wsXrz4XRcEAACUW+ElXt/MYYcdlquvvnpVfRwAALzvujabK9tRNqssRDzwwAMZMGDAqvo4AACgpAqPM02cOLHH61qtlrlz5+bhhx/OaaedtsoKAwAAyqlwiGhtbe3xuqmpKZtttlnOPvvs7LXXXqusMAAAeN/VKiuOMilbPSkYIjo7O3PEEUdk6623ztprr/1e1QQAAJRYoWci+vTpk7322isLFix4j8oBAADKrvCD1VtttVWeffbZ96IWAADoXbWSHiVTOEScc845Oemkk3Lrrbdm7ty5WbRoUY8DAABYszX8TMTZZ5+dv//7v89f/MVfJEn233//VCr/85BHrVZLpVJJZ2fnqq8SAAAojYZDxFlnnZVjjjkmP/3pT9/LegAAoNeUcXO3stWTFAgRtdqK6j/xiU+8Z8UAAADlV+iZiP89vgQAAHwwFdonYtNNN33bIPHyyy+/q4IAAKDXlHE1pLLVk4Ih4qyzznrDjtUAAMAHS6EQ8alPfSrrrbfee1ULAACwGmg4RHgeAgCANV4JV2cq4zhTww9Wd63OBAAAfLA13ImoVqvvZR0AAMBqotAzEQAAsEazOlNDCu0TAQAAlNell16abbbZJi0tLWlpacm4cePy7//+793vL126NJMnT87w4cMzePDgHHTQQZk/f37h+wgRAACwhthggw1y/vnn55FHHsnDDz+cPfbYIwcccED+67/+K0kyZcqU3HLLLbnhhhty991356WXXsrEiRML38c4EwAAdFnNx5n222+/Hq/PPffcXHrppXnwwQezwQYb5Kqrrsp1112XPfbYI0kyffr0bL755nnwwQez8847N3wfnQgAAFgNLFq0qMfR0dHxltd3dnZm5syZWbJkScaNG5dHHnkky5cvz/jx47uvGTt2bEaNGpUHHnigUC1CBAAArAba29vT2trafUydOnWl1z322GMZPHhwmpubc8wxx+QHP/hBtthii8ybNy/9+/fP0KFDe1w/YsSIzJs3r1AtxpkAAKCuUsLN5rrqmTNnTlpaWrrPNzc3r/T6zTbbLI8++mgWLlyY733ve5k0aVLuvvvuVVqTEAEAAKuBrhWX3k7//v0zZsyYJMkOO+yQX/ziF/na176Wv/mbv8myZcuyYMGCHt2I+fPnp62trVAtxpkAAGANVq1W09HRkR122CH9+vXLnXfe2f3erFmzMnv27IwbN67QZ+pEAADAGuLUU0/NPvvsk1GjRuXVV1/Nddddl7vuuiu33357Wltbc9RRR+XEE0/MsGHD0tLSkuOPPz7jxo0rtDJTIkQAAMAa43e/+10+/elPZ+7cuWltbc0222yT22+/PXvuuWeS5MILL0xTU1MOOuigdHR0ZMKECbnkkksK30eIAACANcRVV131lu8PGDAg06ZNy7Rp097VfYQIAADosppvNvd+8WA1AABQiBABAAAUYpwJAADqyrzZXJnoRAAAAIUIEQAAQCHGmQAA4H8r4fhQ2ehEAAAAhQgRAABAIcaZAACgi83mGqITAQAAFCJEAAAAhRhnAgCAOpvNNUYnAgAAKESIAAAACjHOBAAAXazO1BCdCAAAoBAhAgAAKMQ4EwAA1FmdqTE6EQAAQCFCBAAAUIhxJgAA6GJ1poboRAAAAIUIEQAAQCHGmQAAoItxpoboRAAAAIUIEQAAQCHGmQAAoM5mc43RiQAAAAoRIgAAgEKMMwEAQBerMzVEJwIAAChEiAAAAAoxzgQAAF2MMzVEJwIAAChEiAAAAAoxzgQAAHU2m2uMTgQAAFCIEAEAABRinAkAALpYnakhOhEAAEAhQgQAAFCIcSYAAKizOlNjdCIAAIBChAgAAKAQ40wAANDF6kwN0YkAAAAKESIAAIBCjDMBAEAX40wN0YkAAAAKESIAAIBCjDMBAEBdpX6USdnqSXQiAACAgoQIAACgEONMAADQxepMDdGJAAAAChEiAACAQowzAQBAXaW24iiTstWT6EQAAAAFCREAAEAhxpkAAKCL1ZkaohMBAAAUIkQAAACFGGcCAID/rYTjQ2WjEwEAABQiRAAAAIUYZwIAgDqbzTVGJwIAAChEiAAAAAoxzgQAAF1sNtcQnQgAAKAQIQIAACjEOBMAANRZnakxOhEAAEAhQgQAAFCIcSYAAOhidaaG6EQAAACFCBEAAEAhxpkAAKDO6kyN0YkAAAAKESIAAIBCjDMBAEAXqzM1RCcCAAAoRIgAAAAKMc4EAABdjDM1RCcCAAAoRIgAAAAKMc4EAAB1NptrjE4EAABQiBABAAAUYpwJAAC6WJ2pIToRAABAIUIEAABQiHEmAACoq9RqqdTKNT9UtnoSnQgAAKAgIQIAACjEOBMAAHSxOlNDdCIAAIBChAgAAKAQ40wAAFBXqa04yqRs9SQ6EQAAQEFCBLxDf3307Pzo8Xvy2VOe6e1SAApb63t/yHoHPJnBV87vPjfkknkZ/nfPZN2/npV1/vaptJ77Qvq80NGLVQJlZZwJ3oFNtno1+xw8N88+Oai3SwEorO9Tr2Xg7QuyfMPmHueXf3hAln6iJZ3r9E3T4moGfee/M/SMOfnDNz+c9Kn0UrXwPrM6U0N6tRNx+OGHp1Kp5JhjjnnDe5MnT06lUsnhhx/+/hcGb2HAWp05+atP5uIzNs3iRXI4sHqpvFZNy7+8lEWT21Ib3POfAUsnDM3yLddKdUT/vP7hAVl82Drp89+vp8/vlvdStUBZ9fo4U3t7e2bOnJnXXnut+9zSpUtz3XXXZdSoUb1YGazcsV9+Kj+/e1gefWDt3i4FoLDBl8/Lsh0GZ/l2b9NJXVrNwB8vTOeIfulcp9/7Uxyw2uj1EPGRj3wk7e3tufHGG7vP3XjjjRk1alS23377XqwM3ujj+/wuY7ZYnBkXbtTbpQAU1nzPovR7tiOLP73um14z8EevZJ2/mZX1/uY36f/LJVlwVnvSzygTHxxdqzOV7SibXg8RSXLkkUdm+vTp3a+vvvrqHHHEEW/5ezo6OrJo0aIeB7yX1mlbmr879Zl89eSxWb6sFP/pADSs6ffLM+TK+Vl44vpJ/zf/O2zpJ1ryyoUb5ZXzRqVzZP+0/POLybLq+1gpsDooxb+EDjvssNx33315/vnn8/zzz+f+++/PYYcd9pa/Z+rUqWltbe0+2tvb36dq+aDaZMvFWXud5fn6936ZW/7zntzyn/dkm/+zMPsf9mJu+c970tRUwq8JAOr6PrM0TQs7M2zKb7PuXz6Zdf/yyfT/9WsZeOsrWfcvn0w6V/wdVhvUJ50j+2f5lmtl4Zc+lL4vLEvzg4t7uXqgUVOnTs2OO+6YIUOGZL311suBBx6YWbNm9bhm6dKlmTx5coYPH57BgwfnoIMOyvz589/kE1euFE+Frrvuutl3330zY8aM1Gq17LvvvllnnXXe8veceuqpOfHEE7tfL1q0SJDgPfXoA0Pzuf136HFuyrmz8sJza+WGK9tTrWr3A+W1fJu18oeLe45itlw8N50b9M+SicPfZPWlWlJLKst1IvgAWc1XZ7r77rszefLk7Ljjjnn99dfzD//wD9lrr73y+OOPZ9CgFc9CTZkyJT/84Q9zww03pLW1Nccdd1wmTpyY+++/v+H7lCJEJCtGmo477rgkybRp0972+ubm5jQ3N7/tdbCqvPbHvnn+6Z7/ySx9rU8WLeiX55+21CtQbrW1+qRzdJ+e5wZUUh3SJ52jm9M0b1kG3Pdqlm03KNXWPmn67+UZ9P2XU2uupGOHwb1UNVDUbbfd1uP1jBkzst566+WRRx7Jxz/+8SxcuDBXXXVVrrvuuuyxxx5JkunTp2fzzTfPgw8+mJ133rmh+5QmROy9995ZtmxZKpVKJkyY0NvlAMAHS79K+j3+x6x188upLOlMtbVvlm+5Vl45f3RqQ0vzzwWgoIULFyZJhg0bliR55JFHsnz58owfP777mrFjx2bUqFF54IEHVr8Q0adPnzzxxBPdv4bVwSmHb9vbJQC8YwvOHd396+rwfll4urFgKONqSF31/OlCQm83mVOtVnPCCSdkl112yVZbbZUkmTdvXvr375+hQ4f2uHbEiBGZN29ewzWV4sHqLi0tLWlpaentMgAAoHTa29t7LCw0derUt7x+8uTJ+fWvf52ZM2eu8lp6tRMxY8aMt3z/pptuel/qAACAspszZ06PL9zfqgtx3HHH5dZbb80999yTDTbYoPt8W1tbli1blgULFvToRsyfPz9tbW0N11KqTgQAAPSqWkmP/M/UTtexshBRq9Vy3HHH5Qc/+EF+8pOfZKONeq7KtsMOO6Rfv3658847u8/NmjUrs2fPzrhx4xr+MZXmmQgAAODdmTx5cq677rr827/9W4YMGdL9nENra2sGDhyY1tbWHHXUUTnxxBMzbNiwtLS05Pjjj8+4ceMafqg6ESIAAGCNcemllyZJdttttx7np0+fnsMPPzxJcuGFF6apqSkHHXRQOjo6MmHChFxyySWF7iNEAADA/1K21ZmKqNXevvgBAwZk2rRpDe3N9mY8EwEAABQiRAAAAIUYZwIAgC612oqjTMpWT3QiAACAgoQIAACgEONMAABQV6mVb3WmstWT6EQAAAAFCREAAEAhxpkAAKBLrX6USdnqiU4EAABQkBABAAAUYpwJAADqKtUVR5mUrZ5EJwIAAChIiAAAAAoxzgQAAF2sztQQnQgAAKAQIQIAACjEOBMAANRVaiuOMilbPYlOBAAAUJAQAQAAFGKcCQAAutRqK44yKVs90YkAAAAKEiIAAIBCjDMBAECd1ZkaoxMBAAAUIkQAAACFCBEAAEAhnokAAIAutfpRJmWrJzoRAABAQUIEAABQiHEmAACos8RrY3QiAACAQoQIAACgEONMAADQpVZbcZRJ2eqJTgQAAFCQEAEAABRinAkAAOqsztQYnQgAAKAQIQIAACjEOBMAAHSp1Y8yKVs90YkAAAAKEiIAAIBCjDMBAECd1ZkaoxMBAAAUIkQAAACFGGcCAIAu1dqKo0zKVk90IgAAgIKECAAAoBDjTAAA0MVmcw3RiQAAAAoRIgAAgEKMMwEAQF0l5dvcrdLbBayETgQAAFCIEAEAABRinAkAALrUaiuOMilbPdGJAAAAChIiAACAQowzAQBAXaVWwtWZSlZPohMBAAAUJEQAAACFGGcCAIAutfpRJmWrJzoRAABAQUIEAABQiHEmAACoq9RqqZRsc7ey1ZPoRAAAAAUJEQAAQCHGmQAAoEu1fpRJ2eqJTgQAAFCQEAEAABRinAkAAOqsztQYnQgAAKAQIQIAACjEOBMAAHSp1Y8yKVs90YkAAAAKEiIAAIBCjDMBAECXWm3FUSZlqyc6EQAAQEFCBAAAUIhxJgAAqKvUVhxlUrZ6Ep0IAACgICECAAAoxDgTAAB0sTpTQ3QiAACAQoQIAACgEONMAABQV6muOMqkbPUkOhEAAEBBQgQAAFCIcSYAAOhidaaG6EQAAACFCBEAAEAhxpkAAKBLrX6USdnqiU4EAABQkBABAAAUYpwJAADqKrVaKiVbDals9SQ6EQAAQEFCBAAAUIhxJgAA6GKzuYboRAAAAIUIEQAAQCHGmQAAoEstSbW3i/gT5Ztm0okAAACKESIAAIBCjDMBAECdzeYaoxMBAAAUIkQAAACFGGcCAIAutZRvc7eSlZPoRAAAAAUJEQAAQCFCBAAAdKnVynkUcM8992S//fbLyJEjU6lUctNNN/3JH7GW008/Peuvv34GDhyY8ePH56mnnip0DyECAADWIEuWLMm2226badOmrfT9r371q7n44otz2WWX5aGHHsqgQYMyYcKELF26tOF7eLAaAADWIPvss0/22Weflb5Xq9Vy0UUX5ctf/nIOOOCAJMk111yTESNG5KabbsqnPvWphu6hEwEAAF2qJT2SLFq0qMfR0dFR+I/33HPPZd68eRk/fnz3udbW1uy000554IEHGv4cIQIAAFYD7e3taW1t7T6mTp1a+DPmzZuXJBkxYkSP8yNGjOh+rxHGmQAAYDUwZ86ctLS0dL9ubm7utVqECAAAqKvUaqmUbLO5rnpaWlp6hIh3oq2tLUkyf/78rL/++t3n58+fn+22267hzzHOBAAAHxAbbbRR2tracuedd3afW7RoUR566KGMGzeu4c/RiQAAgDXI4sWL8/TTT3e/fu655/Loo49m2LBhGTVqVE444YScc8452WSTTbLRRhvltNNOy8iRI3PggQc2fA8hAgAAuryDzd3ecwXrefjhh7P77rt3vz7xxBOTJJMmTcqMGTNy8sknZ8mSJfnsZz+bBQsW5GMf+1huu+22DBgwoOF7CBEAALAG2W233VJ7i+BRqVRy9tln5+yzz37H9/BMBAAAUIhOBAAAdFkDxpneDzoRAABAIUIEAABQiHEmAADoYpypIToRAABAIUIEAABQiHEmAADoUk1S6e0i/kS1twt4I50IAACgECECAAAoxDgTAADUVWq1VEq2GlLZ6kl0IgAAgIKECAAAoBDjTAAA0MVmcw3RiQAAAAoRIgAAgEKMMwEAQJdqLamUbHyoWrJ6ohMBAAAUJEQAAACFGGcCAIAuVmdqiE4EAABQiBABAAAUYpwJAAC6lXCcKWWrRycCAAAoaI3pRNTqifH12rJergRg1en8Y0dvlwCwynT9nVYr3Tf9FLXGhIhXX301SXL3wu/2ciUAq9CnersAgFXv1VdfTWtra2+XsXJWZ2rIGhMiRo4cmTlz5mTIkCGpVCq9XQ5rsEWLFqW9vT1z5sxJS0tLb5cD8K75e433S61Wy6uvvpqRI0f2dim8S2tMiGhqasoGG2zQ22XwAdLS0uJ/tsAaxd9rvB9K24GgkDUmRAAAwLtWraV0qyFVS1ZPrM4EAAAUJERAQc3NzTnjjDPS3Nzc26UArBL+XgOKqtSssQUAwAfcokWL0tramvGjjk3fpnIF6terHfnx7EuycOHC0jy3pBMBAAAUIkQAAACFWJ0JAAC62GyuIToRAABAIUIEAABQiHEmaNAf/vCHDB8+PEkyZ86cXHHFFXnttdey//77Z9ddd+3l6gCAVcJmcw0RIuBtPPbYY9lvv/0yZ86cbLLJJpk5c2b23nvvLFmyJE1NTbnwwgvzve99LwceeGBvlwrQkCOPPLKh666++ur3uBJgdWWcCd7GySefnK233jr33HNPdtttt3zyk5/Mvvvum4ULF+aVV17J3/3d3+X888/v7TIBGjZjxoz89Kc/zYIFC/LKK6+86QHwZmw2B29jnXXWyU9+8pNss802Wbx4cVpaWvKLX/wiO+ywQ5LkySefzM4775wFCxb0bqEADZo8eXK+853vZPTo0TniiCNy2GGHZdiwYb1dFvSq7s3mRv5dOTebe+lym83B6uTll19OW1tbkmTw4MEZNGhQ1l577e7311577bz66qu9VR5AYdOmTcvcuXNz8skn55Zbbkl7e3sOPvjg3H777fHdItAIIQIaUKlU3vI1wOqmubk5hxxySO644448/vjj2XLLLXPsscdmww03zOLFi3u7PKDkPFgNDTj88MPT3Lyitbl06dIcc8wxGTRoUJKko6OjN0sDeNeamppSqVRSq9XS2dnZ2+VA76qlfJu7laycRIiAtzVp0qQerw877LA3XPPpT3/6/SoHYJXo6OjIjTfemKuvvjr33XdfPvnJT+Yb3/hG9t577zQ1GVQA3poQAW9j+vTpvV0CwCp17LHHZubMmWlvb8+RRx6Z73znO1lnnXV6uyxgNWJ1JgD4gGlqasqoUaOy/fbbv+UzXjfeeOP7WBX0ru7Vmdo+m75N/Xu7nB5ery7Lj+d9s1SrM+lEAMAHzKc//WkLRADvihABAB8wM2bM6O0SgNWcEAEAAF2q1STV3q6ip2rJ6ol9IgAAgIKECAAAoBAhAuBdOvzww3PggQd2v95tt91ywgknvO913HXXXalUKlmwYMGbXlOpVHLTTTc1/Jlnnnlmtttuu3dV129/+9tUKpU8+uij7+pzAN4XtVo5j5IRIoA10uGHH55KpZJKpZL+/ftnzJgxOfvss/P666+/5/e+8cYb85WvfKWhaxv5hz8AlI0Hq4E11t57753p06eno6MjP/rRjzJ58uT069cvp5566huuXbZsWfr3XzXrgg8bNmyVfA4AlJVOBLDGam5uTltbW0aPHp3Pfe5zGT9+fG6++eYk/zOCdO6552bkyJHZbLPNkiRz5szJwQcfnKFDh2bYsGE54IAD8tvf/rb7Mzs7O3PiiSdm6NChGT58eE4++eT86Z6dfzrO1NHRkS996Utpb29Pc3NzxowZk6uuuiq//e1vs/vuuydJ1l577VQqlRx++OFJkmq1mqlTp2ajjTbKwIEDs+222+Z73/tej/v86Ec/yqabbpqBAwdm991371Fno770pS9l0003zVprrZWNN944p512WpYvX/6G6y6//PK0t7dnrbXWysEHH5yFCxf2eP/KK6/M5ptvngEDBmTs2LG55JJLCtcCUAq9PbZknAmgXAYOHJhly5Z1v77zzjsza9as3HHHHbn11luzfPnyTJgwIUOGDMm9996b+++/P4MHD87ee+/d/fsuuOCCzJgxI1dffXXuu+++vPzyy/nBD37wlvf99Kc/ne985zu5+OKL88QTT+Tyyy/P4MGD097enu9///tJklmzZmXu3Ln52te+liSZOnVqrrnmmlx22WX5r//6r0yZMiWHHXZY7r777iQrws7EiROz33775dFHH83RRx+dU045pfDPZMiQIZkxY0Yef/zxfO1rX8sVV1yRCy+8sMc1Tz/9dK6//vrccsstue222/If//EfOfbYY7vfv/baa3P66afn3HPPzRNPPJHzzjsvp512Wr71rW8VrgeA1YNxJmCNV6vVcuedd+b222/P8ccf331+0KBBufLKK7vHmL797W+nWq3myiuv7N7Nd/r06Rk6dGjuuuuu7LXXXrnoooty6qmnZuLEiUmSyy67LLfffvub3vs3v/lNrr/++txxxx0ZP358kmTjjTfufr9r9Gm99dbL0KFDk6zoXJx33nn58Y9/nHHjxnX/nvvuuy+XX355PvGJT+TSSy/Nhz/84VxwwQVJks022yyPPfZY/umf/qnQz+bLX/5y96833HDDnHTSSZk5c2ZOPvnk7vNLly7NNddckw996ENJkq9//evZd999c8EFF6StrS1nnHFGLrjggu6fyUYbbZTHH388l19+eSZNmlSoHgBWD0IEsMa69dZbM3jw4CxfvjzVajX/9//+35x55pnd72+99dY9noP41a9+laeffjpDhgzp8TlLly7NM888k4ULF2bu3LnZaaedut/r27dvPvrRj75hpKnLo48+mj59+uQTn/hEw3U//fTT+eMf/5g999yzx/lly5Zl++23T5I88cQTPepI0h04ivjud7+biy++OM8880wWL16c119/PS0tLT2uGTVqVHeA6LpPtVrNrFmzMmTIkDzzzDM56qij8pnPfKb7mtdffz2tra2F6wHoddVakpKND1VLVk+ECGANtvvuu+fSSy9N//79M3LkyPTt2/OvvEGDBvV4vXjx4uywww659tpr3/BZ66677juqYeDAgYV/z+LFi5MkP/zhD3v84z1Z8ZzHqvLAAw/k0EMPzVlnnZUJEyaktbU1M2fO7O5uFKn1iiuueEOo6dOnzyqrFYByESKANdagQYMyZsyYhq//yEc+ku9+97tZb7313vBtfJf1118/Dz30UD7+8Y8nWfGN+yOPPJKPfOQjK71+6623TrVazd133909zvS/dXVCOjs7u89tscUWaW5uzuzZs9+0g7H55pt3PyTe5cEHH3z7P+T/8rOf/SyjR4/OP/7jP3afe/75599w3ezZs/PSSy9l5MiR3fdpamrKZpttlhEjRmTkyJF59tlnc+ihhxa6PwCrLw9WA9QdeuihWWeddXLAAQfk3nvvzXPPPZe77rorn//85/PCCy8kSb7whS/k/PPPz0033ZQnn3wyxx577Fvu8bDhhhtm0qRJOfLII3PTTTd1f+b111+fJBk9enQqlUpuvfXW/P73v8/ixYszZMiQnHTSSZkyZUq+9a1v5Zlnnskvf/nLfP3rX+9+WPmYY47JU089lS9+8YuZNWtWrrvuusyYMaPQn3eTTTbJ7NmzM3PmzDzzzDO5+OKLV/qQ+IABAzJp0qT86le/yr333pvPf/7zOfjgg9PW1pYkOeusszJ16tRcfPHF+c1vfpPHHnss06dPz7/8y78UqgegDGq1aimPshEiAOrWWmut3HPPPRk1alQmTpyYzTffPEcddVSWLl3a3Zn4+7//+/zt3/5tJk2alHHjxmXIkCH5y7/8y7f83EsvvTR/9Vd/lWOPPTZjx47NZz7zmSxZsiRJ8qEPfShnnXVWTjnllIwYMSLHHXdckuQrX/lKTjvttEydOjWbb7559t577/zwhz/MRhttlGTFcwrf//73c9NNN2XbbbfNZZddlvPOO6/Qn3f//ffPlClTctxxx2W77bbLz372s5x22mlvuG7MmDGZOHFi/uIv/iJ77bVXttlmmx5LuB599NG58sorM3369Gy99db5xCc+kRkzZnTXCsCap1J7s6cBAQDgA2LRokVpbW3Nn689KX2bVs3mo6vK69VlufOVb2XhwoVvOm77fvNMBAAAdKnVyrcaUgm/8zfOBAAAFCJEAAAAhRhnAgCALrUSbjZnnAkAAFjdCREAAEAhxpkAAKBLtZpUSra5m83mAACA1Z0QAQAAFGKcCQAAulidqSE6EQAAQCFCBAAAUIhxJgAAqKtVq6mVbHWmmtWZAACA1Z0QAQAAFGKcCQAAulidqSE6EQAAQCFCBAAAUIhxJgAA6FKtJZWSjQ8ZZwIAAFZ3QgQAAFCIcSYAAOhSqyUp2eZuxpkAAIDVnRABAAAUYpwJAADqatVaaiVbnalmnAkAAFjdCREAAEAhxpkAAKBLrZryrc5UsnqiEwEAABQkRAAAAIUYZwIAgDqrMzVGJwIAAChEiAAAAAoxzgQAAF2sztQQnQgAAKAQnQgAAKh7PcuTkj3H/HqW93YJbyBEAADwgde/f/+0tbXlvnk/6u1SVqqtrS39+/fv7TK6VWplXDMKAADeZ0uXLs2yZct6u4yV6t+/fwYMGNDbZXQTIgAAgEI8WA0AABQiRAAAAIUIEQAAQCFCBAAAUIgQAQAAFCJEAAAAhQgRAABAIf8/MQ79H5SzJ8wAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training with SVM"
      ],
      "metadata": {
        "id": "f2uH-MluMvlE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "svm_model_2 = SVC()\n",
        "\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'C':np.logspace(-5,8,15),\n",
        "               'kernel':['linear', 'rbf', 'sigmoid', 'poly']}]\n",
        "# n_jobs=-1 uses all the system's processors\n",
        "grid_search = GridSearchCV(estimator=svm_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(X_train, Y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best F1 score {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DRO2SuFM56Y",
        "outputId": "dbce3b70-946c-49d7-ea17-ebf3837b9e8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 score 97.20 %\n",
            "Best Parameters: {'C': 0.4393970560760795, 'kernel': 'linear'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the best parameters to train the SVM algorithm\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "svm_model = SVC(C=3.727593720314938, kernel='rbf')\n",
        "svm_train_val_result = cross_validation(svm_model, X_train, Y_train, 5)\n",
        "print(svm_train_val_result)\n",
        "\n",
        "# Plot Training and Validation Result with F1-score as metric\n",
        "model_name = \"SVM\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=svm_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=svm_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "svm_model.fit(X_train, Y_train)\n",
        "svm_y_pred = svm_model.predict(X_test)\n",
        "# Get test result\n",
        "svm_test_result = evaluate_model(Y_test, svm_y_pred)\n",
        "print(svm_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(Y_test, svm_y_pred, label)\n",
        "print(classification_report(Y_test, svm_y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iMQ0nvGtNRdQ",
        "outputId": "449fcdf3-e029-41f1-925f-9a3ebb31c04b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.98626374, 0.98901099, 0.99725275, 0.98626374, 0.98901099]), 'Mean Training Accuracy': 98.95604395604396, 'Training Precision scores': array([1., 1., 1., 1., 1.]), 'Mean Training Precision': 1.0, 'Training Recall scores': array([0.96212121, 0.96969697, 0.99242424, 0.96212121, 0.96969697]), 'Mean Training Recall': 0.9712121212121213, 'Training F1 scores': array([0.98069498, 0.98461538, 0.99619772, 0.98069498, 0.98461538]), 'Mean Training F1 Score': 0.9853636898503819, 'Validation Accuracy scores': array([0.98901099, 0.98901099, 0.93406593, 0.97802198, 1.        ]), 'Mean Validation Accuracy': 97.80219780219781, 'Validation Precision scores': array([0.97058824, 1.        , 0.96551724, 0.94285714, 1.        ]), 'Mean Validation Precision': 0.9757925239061143, 'Validation Recall scores': array([1.        , 0.96969697, 0.84848485, 1.        , 1.        ]), 'Mean Validation Recall': 0.9636363636363636, 'Validation F1 scores': array([0.98507463, 0.98461538, 0.90322581, 0.97058824, 1.        ]), 'Mean Validation F1 Score': 0.9687008106453574}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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EzWnydevWVZkyZYqk3iNHjthMN2rUKN/rCAsLs5nO6TZuWVWvXt3hbWQN2d27d5fFYsnXY9CgQTbrOHv2rN12nnrqKQUEBFinExISFB0drYoVK+qBBx7QxIkTtWnTplJx5PDaIO4Ib29v67+znuZcUmrVqmV3evZPP/2U7XtnRoX9fNWoUUM+Pj7W6WPHjskwjELVdOjQIZvpW265JV/L33rrrQ6PzfpVmhUrVqhly5aqXr26nnrqKc2aNcvuNQKAokJwBwDkyNXVVT/88IPq1q1rMz8tLU1Lly7V8OHD1bp1a/n7++vOO+/UxIkTbY54ZydrSMlvIMvNuXPnbKavPbrpqKzLZF1nThw9TV6Szpw5k6+aHJHdUeVy5crpxx9/VFBQkM38lJQULViwQM8//7wiIiJUrlw5derUSZ999plp7+Xu4eFR0iUUiT59+qhixYrW6czMzGzvh25GRfH5uvYMhYyMDF24cKFQNWXtV0fPgCjI+F69eunVV1+1u17B4cOHNXXqVPXq1UuhoaEKDQ3VE088oRUrVhT6DxMAcBXBHQCQqxo1amjr1q16/fXX7b6je1VqaqpWrVplvRDcs88+m+Mv5FnnF9XRdunfMwKude1RV0dlXcbRYJGf76gXRzjOzMzMdn6zZs20e/duvfDCCypXrly2Yy5evKiff/5Z0dHRqlq1qmJjY0vFUfjSyMnJye707H379pVMMflUkp8vR2vy8vIqVD15GTlypNavX69OnTrJ2dk52zFHjhzRp59+qrvuukuNGjXS0qVL87UNAMiOS0kXAAAwPy8vL7388st66aWXtHbtWq1YsUKrVq3Shg0b7E5DTktL04cffqglS5ZozZo1dkfUrz1VVrL/xbswsv4R4NpT8h2VdZms9RaFrOHirbfeUnh4eKHW2bBhwxyfK1eunN555x299tprWrVqlVauXKnVq1dr8+bNdgH9woULGjt2rJYtW6YlS5bkOwghb1WqVLGZPnXqVAlVkj9m/HxlDd4pKSn5WmdB9qFFixZauHCh4uPjtWTJEq1atUpr1qzRwYMH7cbu3r1bHTp00Pjx4/X888/ne1sAcBXBHQDgMCcnJ7Vu3VqtW7fWmDFjlJ6ers2bN2vRokWaOXOmDhw4YB27b98+9evXTz/99JPNOrIe9U1ISCiy+sqWLWszXZBT0rNenT3rOotC1lOMq1evrqioqCLfTlZubm5q37699Yr0ly5d0m+//aaffvpJM2fOtLnS/dq1azV06FB9/PHHxV7XzSbr2Rml5eyGovh8XbuMs7NzoYO7v7+/zfTp06fztc7CfG0lKChIffv2Vd++fSVJx48f14oVKzR//nwtWLDA+r4ahqH//ve/ioyMtLklHgDkB6fKAwAKzNXVVZGRkRo7dqz+/PNPTZo0yebK3z///LPdhdgCAwNtjtzt27evyI66V6tWzWa6ILfayrpM1nUWhawXsrv2Dx7Xk6enp+688069++67Onz4sEaMGGHz/GeffaakpKQSqe1GduLECZvpa7/zbmaF/XwdPHjQ5tT4qlWr2n1fPL9q1KhhM7179+58Lb9z585Cbf9awcHBevTRR/Xdd99p//79at68ufU5wzD03nvvFdm2ANx8CO4AgCJhsVj0zDPPqFevXjbzf/31V5tpFxcXRUZGWqczMzO1aNGiIqkhNDTU5irqR44cyfb01ZxcuHBBmzdvtqm1ILdqy8udd95pM71ixYoi30Z+ubq66rXXXlOrVq2s89LT0+1uAYbCy/qZyHrqvFllPVqc377NOr4ojj5fG44lafXq1flaPr/jHVWtWjXNnDnTZl7W9x0A8oPgDgAoUi1btrSZznrquSS7+7Z/8MEHRbb9Nm3a2EzHxcU5vOysWbNsvrPfrFmzYvmOd0REhM1pxytWrNCePXuKfDsF4cj7h4JbvXq1/vrrL5t5We9vbla33Xab3NzcrNO//vprvs4WmT59us101s9qQWS90N8333zj8FcPduzYoe3btxe6hpzUqlXL5g+JfJYAFAbBHQBQpBz5jviAAQNsvof6yy+/6NNPPy2S7Q8cONBmesKECfr777/zXC4pKcnuPs3R0dFFUlNWrq6ueu6556zThmHoySefNMV3na/Hd/xvVsnJyXr22Wdt5t166612p3ublb+/v7p162adNgxDQ4cOdWjZ7777TuvXr7dOlylTRj179ix0TQ0aNFCLFi2s0//884/Dfwh84YUXCr393KSlpdl81YTPEoDCILgDAHLUu3fvfJ1Keu7cOX322Wc287K7Wrq/v7+GDBliM2/w4MH65ptvHNpOenq6fvnll2yfi4qKUqNGjazTycnJeuCBB7K9z/lVaWlpevjhhxUfH2+dFxQUZHfaf1EaMmSIzdG4X3/9Vd26dcu1zqySk5P1wQcfaNq0aXbPJSUlqXfv3tq2bZvD6zt8+LC+++4767STk5MaN27s8PI3kyFDhthczC8vp0+f1v3332/3neqxY8cWdWnF6vnnn7e5jsX333+v1157Lddltm/fbvcHtYEDB8rX17dIarr2j2CSNGLECK1cuTLXZUaNGpWv27TNnDlTb775pt297HMzadIkmzN4CnvnCAA3N4I7AJjE2rVrtWzZMrvHli1bbMZdvnw523HLli0r8tOtFy5cqLZt26pBgwYaM2aM1q9fr5SUFLtxly5d0rfffqsWLVroyJEj1vlhYWF230G9avTo0TbfcU1LS1PPnj310EMPae3atcrIyLAZf+XKFW3ZskUjR45UzZo1NWrUqGzXa7FYNGPGDJsrd2/dulWNGzfW3LlzlZaWZp2fmZmppUuXqlmzZlqyZInNeqZPny4PD49cXp3C8fPz07fffmtT54IFC9SwYUONHz9eR48ezXa5Y8eO6bvvvtOjjz6q4OBgDRkyRMeOHbMbl5mZqZkzZ6pp06Zq3ry53n77bW3dutVm/69KSkrStGnTFBkZafOHg86dOyswMLAI9vbG88EHH6hGjRrq2rWrvv76ax0+fDjbcceOHdO7776rW2+91e473l26dFHXrl2vQ7VFp1mzZna3NRs1apS6detmd2G4c+fOady4cWrZsqVNX9WsWTPPsJ8fjzzyiM1dGVJTU9WxY0eNHTvW7lZ7e/bsUbdu3azbDw0NdWgbCQkJevnll1W5cmV169ZNs2bNsvlZd60///xTgwcP1n//+1+b+cV1Bg+Am4QBAMhTtWrVDEnWx8qVK4t9GwV59O3bt0hr8vPzs9uGs7OzUa1aNaNJkyZGixYtjLp16xqurq5247y8vIwtW7bkuv6///7baNiwYbb74uPjYzRo0MBo0aKFUa9ePcPDw8Pm+TZt2uS67k8++cRwcnLKdr233nqr0bhxY6Ns2bLZbnvs2LF5vjZjxowpkp6YNWuW3b5dfQQFBRlhYWFGRESEUbdu3RzrHTNmjN16z507l+1YNzc3o0aNGkZ4eLgRERFh1KxZ03B2drYbV7FiRePo0aMF2qec5Oc1O3ToUKF6u02bNjbLF7XsXltfX1+jbt26RkREhNGkSRMjODg4x89q69atjZSUlCKv66oZM2bk6/OSH5cvXzaioqKy3a/g4GCjWbNmOf5cKF++fJ4/Fwwj/+/fsWPHsv0Z6uLiYtSuXdto3ry5UblyZZvnQkJCjG+++SbPz5JhGMZ7772X7f76+/sb9erVM2677TajadOmRsWKFbMd98gjj+T3ZQYAG9zHHQCQLxkZGTpy5EiOR5skKSQkRN9++62aNm2a67pCQkK0du1a9enTRwsWLLB57sKFC4U6gyA6Olp+fn4aOHCgzS2oLly4oF27dmW7jLu7u8aPH69BgwYVeLv59cgjj6h27drq1auX/vzzT5vn4uPjbU7fz46zs7OCg4Md3l5aWlqeV9qvV6+evv/++1JztXOzSEpKyvP2eU5OTho6dKhee+01u/u5lxbu7u5auHChHn/8cX311Vc2zx0/fjzHrxDUrl1bCxYsUL169Yq8psqVK2v58uXq0KGDzcX/rly5ov3799uNDw4O1qJFiwp9wbjz58/r/PnzuY6Jjo7Wxx9/XKjtAACnygMAcrRx40a98847uuuuu+Tt7Z3n+Fq1aunVV1/Vvn37bG75lhs/Pz99//33Wrlypdq3b29z1ers1K1bVyNHjtQXX3yR57q7d++uAwcOaNCgQSpXrlyO43x8fNSnTx/98ccf1zW0XxUeHq49e/boiy++0G233SZnZ+dcx7u7u6tdu3YaN26cjh07pieeeMJujL+/v7Zt26bY2Fi1bNlS7u7uedbRqFEjffDBB9q5c6fq1KlT4P25GXzyySd65JFHHP7jRmBgoIYMGaJ9+/bp7bffLrWh/So3Nzd9+eWXWrVqldq1aycXl5yPBdWsWVPjx4/X7t27iyW0X7udnTt36sUXX8zxQnDu7u6Kjo7Wjh07dMsttzi87ieffFLff/+9Bg4cqFq1auU53t3dXV27dtUvv/yiTz75JNfXBwAcYTEMwyjpIgAA5peRkaG9e/dq//79+ueff6xHsX18fBQSEqLGjRurevXqhd5OcnKy1q5dq7///lunT59WRkaGfH19Vb16dTVq1EiVK1cucP0bN27U/v37lZCQoMzMTFWsWFE1atTQ7bffbqoglZiYqN9++03Hjx/X6dOnlZ6eLh8fH1WqVEn16tVT3bp18/39+9TUVO3Zs0cHDhxQfHy8Ll68KIvFIl9fX4WGhqpx48YKCQkppj26sZ05c0Z79+7VkSNHdOrUKSUnJ8vZ2Vlly5ZVhQoV1KRJk1Jz5fiCOn/+vH799VcdP35cZ86ckbe3twICAtS4cWPVrVv3uteTlpam1atX6+DBgzp9+rS8vb1Vu3Zt3XHHHTZ3tCioU6dOac+ePTp48KDOnj2rlJQUeXl5qWzZsqpXr57CwsIc+mMnADiK4A4AAAAAgIlxqjwAAAAAACZGcAcAAAAAwMRMF9zXrFmjzp07Kzg4WBaLRfPnz89zmVWrVqlp06Zyd3dXrVq1FBcXV+x1AgAAAABwPZguuCcnJyssLEyTJk1yaPyhQ4d077336s4779T27dv13HPPaeDAgVq8eHExVwoAAAAAQPEz9cXpLBaL5s2bpy5duuQ45sUXX9TChQu1e/du67xHHnlE58+f16JFi65DlQAAAAAAFJ9Sf1PJ9evXKyoqymZehw4d9Nxzz+W4TGpqqlJTU63TmZmZOnv2rMqXLy+LxVJcpQIAAAAAIEkyDEMXLlxQcHCwnJxyPxm+1Af3EydOKCAgwGZeQECAkpKSdOnSJXl6etot8+abb2rs2LHXq0QAAAAAALJ17NgxVa5cOdcxpT64F8Tw4cMVExNjnU5MTFTVqlV16NAh+fj4lGBlKKj09HStXLlSd955p1xdXUu6HCBf6F+UZvQvSjP6F6WdtYcHDZLrpUslXY65/P13SVeQpwsXLqh69eoOZdBSH9wDAwN18uRJm3knT56Ur69vtkfbJcnd3V3u7u5288uVKydfX99iqRPFKz09XV5eXipfvjz/8aLUoX9RmtG/KM3oX5R21h6+fFmuly+XdDnmUr58SVeQp6s/dxz5urbpriqfX5GRkVq+fLnNvKVLlyoyMrKEKgIA3KgsFh5ZH35+Jf2uAABw4zNdcL948aK2b9+u7du3S/r3dm/bt2/X0aNHJf17mnufPn2s45966ikdPHhQw4YN0x9//KGPP/5Yc+bM0fPPP18S5QMAAAAAUKRMF9w3b96sJk2aqEmTJpKkmJgYNWnSRKNHj5YkxcfHW0O8JFWvXl0LFy7U0qVLFRYWpvHjx+uzzz5Thw4dSqR+AAAAAACKkum+4962bVvldmv5uLi4bJfZtm1bMVYFAED+WSyG/P2vyMcnQzfq3UY9PNLl4uKiy5cvKyMjo6TLKVbOzs5ycXHh1rEAgOvOdMEdAIAbQcWKaXr66Xg1a5YiFxfdsMHdYjFUoUKgjh07dlMEWi8vLwUFBcnNza2kSwEA3EQI7gAAFDEXl0yNH39I1as7y8MjWJKbpBsz1FosmapS5aLKlCkjJyfTfQOvyBiGobS0NJ06dUqHDh1S7dq1b+j9BQCYC8EdAIAiFhSUpgoVMuXhUUWSV0mXU6wslky5uaXJw8Pjhg+ynp6ecnV11ZEjR5SW9u8+AwBwPdzY/8MCAFACnJyunhrPf7M3mhv9jxMAAHPiiDtuLH5+0qVLJV2FueRysUcAAAAA5sefjQFcVxYLj6wPP7///+L4+ZV8MWZ8oFQLDQ3VxIkTHR6/atUqWSwWnT9/vthqAlD6lPR/RWZ9WH+HwA2PI+4AAFwnzZtf3+1t2uT42ObNc/8jSXT0GD3xRGwBatgkb29vh8fffvvtio+Plx+/jQIAYEVwBwAA+vnneOu/ly6dralTR+u77/ZZ53l5lbH+2zAMZWRkyMUl718jKlasmK863NzcFBgYmK9lAAC40XGqfClU0qfkmPHBgRkAKJwKFQKtjzJl/GSxWKzTR478oTZtfLR27c967LFw3X67u3bs+FV///2XYmK6qE6dOvL19VXz5s21bNkym/VmPVXeYrHos88+U9euXeXl5aXatWtrwYIF1uezniofFxcnf39/LV68WPXr11eZMmXUsWNHxcf/3x8arly5omeffVb+/v4qX768XnzxRfXt21ddunQpzpes1Cnp/6vN+OD3BwClBcEdAAA4ZNKklzR48Fv69tu9qlWrkVJSLqply3s0f/58bdmyRR07dlTnzp119OjRXNczduxYde/eXTt37lSnTp3Uu3dvnT17NsfxKSkpGjdunL788kutWbNGR48e1dChQ63Pv/322/r66681Y8YMrV27VklJSZo/f35R7TYAACWO4A4AABzy5JOvqEWLu1W5ck35+ZVTnTpheuihJ9WgQQPVrl1br776qmrWrGlzBD07/fr1U8+ePVWrVi298cYbunjxojZu3Jjj+PT0dE2ZMkXNmjVT06ZNNXjwYC1fvtz6/Icffqjhw4era9euqlevnj766CP5+/sX1W4DAFDi+I47AABwSP36zWymU1Iu6tNPx2jjxh918uRJXblyRZcuXcrziHujRo2s//b29pavr68SEhJyHO/l5aWaNWtap4OCgqzjExMTdfLkSUVERFifd3Z2Vnh4uDIzM/O1fwAAmBXBHQAAOMTT0/bq8O+/P1QbNizVW2+N1a233ipvb29169ZNaWlpua7H1dXVZtpiseQasrMbbxhGPqsHAKD04lR5AABQIDt2rFXnzn1133336dZbb1VgYKAOHz58XWvw8/NTQECANl1z77uMjAxt3br1utYBAEBx4og7AAAokCpVamvlynnatetOlSlTRmPGjCmR09P/85//6M0331StWrVUr149ffjhhzp37pwsFst1rwUAgOJAcAcAAAXy/PMT9OqrA9ShQwdVqFBBL774opKSkq57HS+++KJOnDihPn36yNnZWU888YQ6dOggZ2fn614LSik/P+nSpZKuwnz4SgpgGgR3AACuk2vO5ja1zp37qXPnftbp8PC22rTJ/hf44OBQTZ26TDVqJMnX11dOTk4aNGiQzZisp85n9930q/dsl6S2bdvajOnXr5/69etnM75Lly42Y1xcXPThhx/qww8/lCRlZmaqfv366t69e167CgBAqUBwBwAApdqRI0e0ZMkStWnTRqmpqfroo4906NAh9erVq6RLAwCgSHBxOgAAUKo5OTkpLi5OzZs3V8uWLbVr1y4tW7ZM9evXL+nSAAAoEhxxBwAApVqVKlW0du3aki4DAIBiwxF3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAFIm2bdvqueees06HhoZq4sSJuS5jsVg0f/78Qm+7qNYDAIAZcR93AACuk2bNLdd1e5s3GQ6Pff75zrpyJV0ffrjI7rlt237RE0/coZkzd6h27UYOr3PTpk3y9vZ2eLwjYmNjNX/+fG3fvt1mfnx8vMqWLVuk2wIAwCw44g4AAPTAA49r48alOnnyb7vnfvhhhurXb5av0C5JFStWlJeXV1GVmKvAwEC5u7tfl20BAHC9EdwBAIBatbpP/v4V9eOPcTbzU1Iuavnyb9W2bReNGNFTnTqFqFUrLz3yyK1avHhWruvMeqr8/v37dccdd8jDw0MNGjTQ0qVL7ZZ58cUXVadOHXl5ealGjRoaNWqU0tPTJUlxcXEaO3asduzYIYvFIovFori4f+vNeqr8rl271K5dO3l6eqp8+fJ64okndPHiRevz/fr1U5cuXTRu3DgFBQWpfPnyGjRokHVbAACYCafKAwAAubi46N57++jHH+M0YMAIWSz/nta/bNm3ysjI0D33PKply75Vnz4vytvbV2vXLtSYMY+pSpXqqlGjXp7rz8zM1IMPPqiAgABt2LBBiYmJNt+Hv8rHx0dxcXEKDg7Wrl27FB0dLR8fHw0bNkw9evTQ7t27tWjRIi1btkyS5OfnZ7eO5ORkdejQQZGRkdq0aZMSEhI0cOBADR482Br0JWnlypUKCgrSypUrdeDAAfXo0UONGzdWdHR0wV5EAACKCUfcAQCAJOn++wfo77//0tatq63zfvxxhtq1e0hBQdX02GNDVbduY1WuXEM9evxHkZEdtXTptw6te9myZfrjjz/0xRdfKCwsTHfccYfeeOMNu3EjR47U7bffrtDQUHXu3FlDhw7VnDlzJEmenp4qU6aMXFxcFBgYqMDAQHl6etqtY+bMmbp8+bK++OIL3XLLLWrXrp0++ugjffnllzp58qR1XNmyZfXRRx+pXr16uu+++3Tvvfdq+fLl+X3ZAAAodhxxBwAAkqTQ0Hpq1Oh2LVgwXeHhbXXs2AFt2/aLpkx5RRkZGZox4w0tWzZHp079o/T0NKWlpcrDwz44Z2fv3r2qUqWKgoODrfMiIyPtxs2ePVsffPCB/vrrL128eFFXrlyRr69vvvZj7969CgsLs7kwXsuWLZWZmal9+/YpICBAktSwYUM5OztbxwQFBWnXrl352hYAANcDR9wBAIDVAw88rhUr5io5+YJ++GGGKleuqaZN2+jLL9/VN9+8rz59XtTkySv19dfbFRnZQenpaUW27fXr16t3797q1KmTfvzxR23btk0jRoxQWlrRbeNarq6uNtMWi0WZmZnFsi0AAAqD4A4AAKyiorrLyclJixfP1MKFX+j++wfIYrFox461atPmAXXq9Kjq1AlTSEgNHT36p8PrrV+/vo4dO6b4+HjrvN9++81mzLp161StWjWNGDFCzZo1U+3atXXkyBGbMW5ubsrIyMhzWzt27FBycrJ13tq1a+Xk5KS6des6XDMAAGZBcAcAAFZeXmV09909NGnScJ05E6/77usnSapatbY2bFiqHTvW6dChvXrjjSd15szJ3Fd2jaioKNWpU0d9+/bVjh079Msvv2jEiBE2Y2rXrq2jR4/qm2++0V9//aUPPvhA8+bNsxkTGhqqQ4cOafv27Tp9+rRSU1PtttW7d295eHiob9++2r17t1auXKn//Oc/euyxx6ynyQMAUJoQ3AEAgI37739cSUnndNttHVSx4r/fSR8wYKTq1WuqZ5/toKeeaqvy5QPVtm0Xh9fp5OSkefPm6dKlS4qIiNDAgQP1+uuvZ9nu/Xr++ec1ePBgNW7cWOvWrdOoUaNsxjz00EPq2LGj7rzzTlWsWFGzZtnfks7Ly0uLFy/W2bNn1bx5c3Xr1k133XWXPvroo/y/GAAAmIDFMAyjpIsoaUlJSfLz81NiYmK+L4BTEv7/HXpwDU/PdM2a9ZM69ewp10uXSrocczHZR5z+tUf/5sFEPexo/1ardllTphxShQrVJXkUa00lzckpUzVqJMnX11dOTjf+8YDLly/r0KFDql69ujw8Std7y89fe/z8zUMp/Pl7s6GHc2Gi/s1JfnLojf8/LAAAAAAApRjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAChihnH1Yrbmv6It8oeb8QAASgLBHQCAInbmjKvS0iQppaRLQRFLSfn3PXV1dS3hSgAANxOXki4AAIAbTXKysxYs8FfPngny95ckL0k35k2IDSNTaWlpunz58g19H3fDMJSSkqKEhAT5+/vL2dm5pEsCANxECO4AABSDGTMCJUn3358gNzfJcmPmdlkshqRL8vT0lOVG3clr+Pv7KzAwsKTLAADcZAjuAAAUA8OwaPr0IH3zTSVVqJB+wwZ3D490jR+/RnfccccNf/q4q6srR9oBACWC4A4AQDFKSXHW0aM3btjz9HTWlStX5OHhccMHdwAASsqN+2U0AAAAAABuAAR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEzNlcJ80aZJCQ0Pl4eGhFi1aaOPGjTmOTU9P1yuvvKKaNWvKw8NDYWFhWrRo0XWsFgAAAACA4mO64D579mzFxMRozJgx2rp1q8LCwtShQwclJCRkO37kyJGaOnWqPvzwQ+3Zs0dPPfWUunbtqm3btl3nygEAAAAAKHqmC+4TJkxQdHS0+vfvrwYNGmjKlCny8vLS9OnTsx3/5Zdf6uWXX1anTp1Uo0YNPf300+rUqZPGjx9/nSsHAOAm5ucnWSw8sj4AACgCpgruaWlp2rJli6KioqzznJycFBUVpfXr12e7TGpqqjw8PGzmeXp66tdffy3WWgEAAAAAuB5cSrqAa50+fVoZGRkKCAiwmR8QEKA//vgj22U6dOigCRMm6I477lDNmjW1fPly/e9//1NGRkaO20lNTVVqaqp1OikpSdK/35dPT08vgj0pXp6eJV2B+Xh6/vu+pfPi2DNZT/MW2aN/82CiHuYtskf/5oH+NTX6Nw/0r+nRw7kwUf/mJD/Z02IYhlGMteTL8ePHFRISonXr1ikyMtI6f9iwYVq9erU2bNhgt8ypU6cUHR2tH374QRaLRTVr1lRUVJSmT5+uS5cuZbud2NhYjR071m7+zJkz5eXlVXQ7BAAAAABANlJSUtSrVy8lJibK19c317GmOuJeoUIFOTs76+TJkzbzT548qcDAwGyXqVixoubPn6/Lly/rzJkzCg4O1ksvvaQaNWrkuJ3hw4crJibGOp2UlKQqVaqoffv2eb5gZuDnV9IVmI+nZ7qmT1+quwcMkGsOf7C5aSUmlnQFNuhfe/RvHkzUw/SvPfo3D/SvqdG/eaB/TY8ezoWJ+jcnV8/8doSpgrubm5vCw8O1fPlydenSRZKUmZmp5cuXa/Dgwbku6+HhoZCQEKWnp2vu3Lnq3r17jmPd3d3l7u5uN9/V1VWurq6F2ofrgc9kzlwvXeKHVlYm62nenpzRvzkwUQ/z9uSM/s0B/Vsq0L85oH9LDXo4Gybq35zkJ3uaKrhLUkxMjPr27atmzZopIiJCEydOVHJysvr37y9J6tOnj0JCQvTmm29KkjZs2KB//vlHjRs31j///KPY2FhlZmZq2LBhJbkbAAAAAAAUCdMF9x49eujUqVMaPXq0Tpw4ocaNG2vRokXWC9YdPXpUTk7/dzH8y5cva+TIkTp48KDKlCmjTp066csvv5S/v38J7QEAAAAAAEXHdMFdkgYPHpzjqfGrVq2ymW7Tpo327NlzHaoCAAAAAOD6M9V93AEAAAAAgC2COwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxEwZ3CdNmqTQ0FB5eHioRYsW2rhxY67jJ06cqLp168rT01NVqlTR888/r8uXL1+nagEAAAAAKD6mC+6zZ89WTEyMxowZo61btyosLEwdOnRQQkJCtuNnzpypl156SWPGjNHevXs1bdo0zZ49Wy+//PJ1rhwAAAAAgKJnuuA+YcIERUdHq3///mrQoIGmTJkiLy8vTZ8+Pdvx69atU8uWLdWrVy+Fhoaqffv26tmzZ55H6QEAAAAAKA1cSrqAa6WlpWnLli0aPny4dZ6Tk5OioqK0fv36bJe5/fbb9dVXX2njxo2KiIjQwYMH9dNPP+mxxx7LcTupqalKTU21TiclJUmS0tPTlZ6eXkR7U3w8PUu6AvPx9Pz3fUvnxbFnsp7mLbJH/+bBRD3MW2SP/s0D/Wtq9G8e6F/To4dzYaL+zUl+sqfFMAyjGGvJl+PHjyskJETr1q1TZGSkdf6wYcO0evVqbdiwIdvlPvjgAw0dOlSGYejKlSt66qmnNHny5By3Exsbq7Fjx9rNnzlzpry8vAq/IwAAAAAA5CIlJUW9evVSYmKifH19cx1rqiPuBbFq1Sq98cYb+vjjj9WiRQsdOHBAQ4YM0auvvqpRo0Zlu8zw4cMVExNjnU5KSlKVKlXUvn37PF8wM/DzK+kKzMfTM13Tpy/V3QMGyPXSpZIux1wSE0u6Ahv0rz36Nw8m6mH61x79mwf619To3zzQv6ZHD+fCRP2bk6tnfjvCVMG9QoUKcnZ21smTJ23mnzx5UoGBgdkuM2rUKD322GMaOHCgJOnWW29VcnKynnjiCY0YMUJOTvZf43d3d5e7u7vdfFdXV7m6uhbBnhQvPpM5c710iR9aWZmsp3l7ckb/5sBEPczbkzP6Nwf0b6lA/+aA/i016OFsmKh/c5Kf7Gmqi9O5ubkpPDxcy5cvt87LzMzU8uXLbU6dv1ZKSopdOHd2dpYkmehbAAAAAAAAFIipjrhLUkxMjPr27atmzZopIiJCEydOVHJysvr37y9J6tOnj0JCQvTmm29Kkjp37qwJEyaoSZMm1lPlR40apc6dO1sDPAAAAAAApZXpgnuPHj106tQpjR49WidOnFDjxo21aNEiBQQESJKOHj1qc4R95MiRslgsGjlypP755x9VrFhRnTt31uuvv15SuwAAAAAAQJExXXCXpMGDB2vw4MHZPrdq1SqbaRcXF40ZM0Zjxoy5DpUBAAAAAHB9meo77gAAAAAAwBbBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJuRRm4W3btmnWrFn6448/lJKSomXLlkmSjhw5og0bNigqKkrlypUrkkIBAAAAALgZFTi4Dxs2TOPHj5dhGJIki8Vifc4wDPXq1Uvjx4/XkCFDCl8lAAAAAAA3qQKdKj9jxgyNGzdO9913n3bu3Knhw4fbPB8aGqqIiAgtWLCgSIoEAAAAAOBmVaAj7h9//LHq16+vuXPnysXFRW5ubnZj6tWrZz11HgAAAAAAFEyBjrjv2bNHd999t1xccs79AQEBSkhIKHBhAAAAAACggMHdxcVFaWlpuY45fvy4ypQpU6CiAAAAAADAvwoU3G+99VatWLFCGRkZ2T5/9Qrz4eHhhSoOAAAAAICbXYGC+4ABA/Tnn3/qqaeeUmpqqs1zSUlJ6tevn06cOKHo6OgiKRIAAAAAgJtVgS5ON2DAAC1btkzTpk3T7Nmz5e/vL0mKiIjQ3r17lZycrH79+qlbt25FWSsAAAAAADedAh1xl6SZM2dq6tSpql69uv755x8ZhqHNmzeratWqmjx5sqZPn16UdQIAAAAAcFMq0BH3q6KjoxUdHa1Lly7p3Llz8vX15YJ0AAAAAAAUoQIdcXd2dlbv3r2t056engoODia0AwAAAABQxAoU3H19fVWlSpWirgUAAAAAAGRRoOAeERGhHTt2FHUtAAAAAAAgiwIF99jYWK1YsUJffPFFUdcDAAAAAACuUaCL0y1dulRt27ZV//799eGHH6p58+YKCAiQxWKxGWexWDRq1KgiKRQAAAAAgJtRgYJ7bGys9d9btmzRli1bsh1HcAcAAAAAoHAKFNxXrlxZ1HUAAAAAAIBsFCi4t2nTpqjrAAAAAAAA2SjQxekAAAAAAMD1UaAj7letXbtWcXFx2r59u5KSkuTr66smTZqoT58+atWqVVHVCAAAAADATavAwf3555/XBx98IMMwJP17ITrDMLRlyxZNmzZNQ4YM0YQJE4qsUAAAAAAAbkYFOlX+888/1/vvv6/atWvr66+/1vHjx3XlyhXFx8dr5syZqlOnjt5//33u8w4AAAAAQCEVKLhPnjxZlStX1oYNG9SzZ08FBgbKYrEoICBAjzzyiH777TeFhITo448/Lup6AQAAAAC4qRQouP/+++966KGH5Ofnl+3zfn5+euihh/T7778XqjgAAAAAAG52xXZVeYvFUlyrBgAAAADgplGg4N6wYUPNnTtXFy9ezPb5CxcuaO7cuWrYsGGhigMAAAAA4GZXoOD+5JNP6u+//1ZkZKTmzp2r06dPS5JOnz6t7777Trfffrv+/vtvPf3000VaLAAAAAAAN5sC3Q6uf//+2rZtmz766CN1795dkuTk5KTMzExJkmEY+s9//qO+ffsWXaUAAAAAANyECnwf9w8++EAPP/yw4uLitH37diUlJcnX11dNmjRR37591bp166KsEwAAAACAm1KBg7sktW7dmoAOAAAAAEAxKrarygMAAAAAgMIrUHD//PPPFR4eruPHj2f7/PHjxxUeHq6ZM2cWqjgAAAAAAG52BQrucXFxcnNzU3BwcLbPBwcHy9PTU9OmTStUcQAAAAAA3OwKFNz37NmjJk2a5DqmcePG2rNnT4GKAgAAAAAA/ypQcE9MTFTZsmVzHePr66tz584VqCgAAAAAAPCvAgX34OBgbd++PdcxO3bsUEBAQEFWL0maNGmSQkND5eHhoRYtWmjjxo05jm3btq0sFovd49577y3w9gEAAAAAMIMCBfeoqCgtXrxYS5cuzfb5JUuWaNGiRerQoUOBipo9e7ZiYmI0ZswYbd26VWFhYerQoYMSEhKyHf+///1P8fHx1sfu3bvl7Oyshx9+uEDbBwAAAADALAoU3IcPHy5vb2916tRJAwYM0KxZs7RmzRrNmjVL/fv317333itfX18NHz68QEVNmDBB0dHR6t+/vxo0aKApU6bIy8tL06dPz3Z8uXLlFBgYaH0sXbpUXl5eBHcAAAAAQKnnUpCFqlevroULF+qRRx5RXFycPv/8c+tzhmGocuXKmjNnjqpXr57vdaelpWnLli02od/JyUlRUVFav369Q+uYNm2aHnnkEXl7e+d7+wAAAAAAmEmBgrsktWrVSgcPHtT333+vjRs3KjExUf7+/oqIiND9998vNze3Aq339OnTysjIsPt+fEBAgP744488l9+4caN2796d663oUlNTlZqaap1OSkqSJKWnpys9Pb1AdV9Pnp4lXYH5eHr++76l8+LYM1lP8xbZo3/zYKIe5i2yR//mgf41Nfo3D/Sv6dHDuTBR/+YkP9nTYhiGUYy15Nvx48cVEhKidevWKTIy0jp/2LBhWr16tTZs2JDr8k8++aTWr1+vnTt35jgmNjZWY8eOtZs/c+ZMeXl5Fbx4AAAAAAAckJKSol69eikxMVG+vr65ji3wEffsXL39W163istNhQoV5OzsrJMnT9rMP3nypAIDA3NdNjk5Wd98841eeeWVXMcNHz5cMTEx1umkpCRVqVJF7du3z/MFMwM/v5KuwHw8PdM1ffpS3T1ggFwvXSrpcswlMbGkK7BB/9qjf/Ngoh6mf+3Rv3mgf02N/s0D/Wt69HAuTNS/Obl65rcjHA7up06d0q5du1S3bl2FhITYPLdp0yY98cQT1qPc9evX1+TJk9W6dWuHC7nKzc1N4eHhWr58ubp06SJJyszM1PLlyzV48OBcl/3222+VmpqqRx99NNdx7u7ucnd3t5vv6uoqV1fXfNd8vfGZzJnrpUv80MrKZD3N25Mz+jcHJuph3p6c0b85oH9LBfo3B/RvqUEPZ8NE/ZuT/GRPh68qP3nyZN199906f/68zfwTJ06oQ4cO2rFjh9zc3OTt7a09e/bonnvu0V9//eVwIdeKiYnRp59+qs8//1x79+7V008/reTkZPXv31+S1KdPn2yvWD9t2jR16dJF5cuXL9B2AQAAAAAwG4eD+5o1a1S3bl01bNjQZv7777+v8+fPq1u3bjp37pwSExM1ceJEpaSkaOLEiQUqqkePHho3bpxGjx6txo0ba/v27Vq0aJH1gnVHjx5VfHy8zTL79u3Tr7/+qscff7xA2wQAAAAAwIwcPlX+wIEDioqKspv/ww8/yNnZWR999JE8PDwkSc8++6y++OILrVixosCFDR48OMdT41etWmU3r27dujLZdfYAAAAAACg0h4+4nzp1SkFBQTbzLly4oL179yo8PFyVKlWyee62227TkSNHiqZKAAAAAABuUg4Hd0l232/funWrDMNQs2bN7Mb6+/srIyOjUMUBAAAAAHCzczi4V69eXevWrbOZt3LlSlksFt1222124xMSEqzfSQcAAAAAAAXjcHC/7777tH37dr3xxhtKSkrS5s2bNWXKFLm5ualTp0524zds2KAaNWoUabEAAAAAANxsHA7uw4YNU0hIiEaNGqWyZcuqRYsWSkhI0JAhQ1SuXDmbsfv27dOuXbvUrl27Ii8YAAAAAICbicNXlS9XrpzWr1+v0aNH67ffflP58uXVvXv3bK/8vnDhQoWFhalz585FWiwAAAAAADcbh4O7JIWEhGjatGl5jouJiVFMTEyBiwIAAAAAAP/K11XlAQAAAADA9UVwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMLFiC+4vvPCCatasWVyrBwAAAADgplBswf306dM6fPhwca0eAAAAAICbAqfKAwAAAABgYi6ODhwwYEC+Vvzrr7/muxgAAAAAAGDL4eAeFxcni8UiwzAcXrnFYilQUQAAAAAA4F8OB3dfX1+FhIRo+vTpDo1/9dVX9fPPPxe4MAAAAAAAkI/g3rRpU23atEkREREOHUmvWLFioQoDAAAAAAD5uDhd06ZNlZKSoj/++KM46wEAAAAAANdw+Ij7Aw88oDNnzujSpUsOjR84cKDatm1b0LoAAAAAAIDyEdxbt26t1q1bO7zili1bqmXLlgUqCgAAAAAA/Iv7uAMAAAAAYGIOB/ekpCSlpaUVZy0AAAAAACALh4N72bJl9fbbb9vM27Bhgz744IMiLwoAAAAAAPzL4eBuGIYMw7CZt2jRIj3//PNFXhQAAAAAAPgX33EHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIu+Rn81Vdf6bfffrNOHzhwQJLUqVOnbMdbLBYtXLiwEOUBAAAAAHBzy1dwP3DggDWsX2vRokXZjrdYLAWrCgAAAAAASMpHcD906FBx1gEAAAAAALLhcHCvVq1acdYBAAAAAACywcXpAAAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGKmDO6TJk1SaGioPDw81KJFC23cuDHX8efPn9egQYMUFBQkd3d31alTRz/99NN1qhYAAAAAgOLjUtIFZDV79mzFxMRoypQpatGihSZOnKgOHTpo3759qlSpkt34tLQ03X333apUqZK+++47hYSE6MiRI/L397/+xQMAAAAAUMRMF9wnTJig6Oho9e/fX5I0ZcoULVy4UNOnT9dLL71kN3769Ok6e/as1q1bJ1dXV0lSaGjo9SwZAAAAAIBiY6rgnpaWpi1btmj48OHWeU5OToqKitL69euzXWbBggWKjIzUoEGD9P3336tixYrq1auXXnzxRTk7O2e7TGpqqlJTU63TSUlJkqT09HSlp6cX4R4VD0/Pkq7AfDw9/33f0nlx7Jmsp3mL7NG/eTBRD/MW2aN/80D/mhr9mwf61/To4VyYqH9zkp/saTEMwyjGWvLl+PHjCgkJ0bp16xQZGWmdP2zYMK1evVobNmywW6ZevXo6fPiwevfurWeeeUYHDhzQM888o2effVZjxozJdjuxsbEaO3as3fyZM2fKy8ur6HYIAAAAAIBspKSkqFevXkpMTJSvr2+uY011xL0gMjMzValSJX3yySdydnZWeHi4/vnnH7377rs5Bvfhw4crJibGOp2UlKQqVaqoffv2eb5gZuDnV9IVmI+nZ7qmT1+quwcMkOulSyVdjrkkJpZ0BTboX3v0bx5M1MP0rz36Nw/0r6nRv3mgf02PHs6Fifo3J1fP/HaEqYJ7hQoV5OzsrJMnT9rMP3nypAIDA7NdJigoSK6urjanxdevX18nTpxQWlqa3Nzc7JZxd3eXu7u73XxXV1fr9+TNjM9kzlwvXeKHVlYm62nenpzRvzkwUQ/z9uSM/s0B/Vsq0L85oH9LDXo4Gybq35zkJ3ua6nZwbm5uCg8P1/Lly63zMjMztXz5cptT56/VsmVLHThwQJmZmdZ5f/75p4KCgrIN7QAAAAAAlCamCu6SFBMTo08//VSff/659u7dq6efflrJycnWq8z36dPH5uJ1Tz/9tM6ePashQ4bozz//1MKFC/XGG29o0KBBJbULAAAAAAAUGVOdKi9JPXr00KlTpzR69GidOHFCjRs31qJFixQQECBJOnr0qJyc/u/vDVWqVNHixYv1/PPPq1GjRgoJCdGQIUP04osvltQuAAAAAABQZEwX3CVp8ODBGjx4cLbPrVq1ym5eZGSkfvvtt2KuCgAAAACA6890p8oDAAAAAID/Q3AHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDETBvcJ02apNDQUHl4eKhFixbauHFjjmPj4uJksVhsHh4eHtexWgAAAAAAiocpg/vs2bMVExOjMWPGaOvWrQoLC1OHDh2UkJCQ4zK+vr6Kj4+3Po4cOXIdKwYAAAAAoHiYMrhPmDBB0dHR6t+/vxo0aKApU6bIy8tL06dPz3EZi8WiwMBA6yMgIOA6VgwAAAAAQPFwKekCskpLS9OWLVs0fPhw6zwnJydFRUVp/fr1OS538eJFVatWTZmZmWratKneeOMNNWzYMNuxqampSk1NtU4nJSVJktLT05Wenl5Ee1J8PD1LugLz8fT8931L58WxZ7Ke5i2yR//mwUQ9zFtkj/7NA/1ravRvHuhf06OHc2Gi/s1JfrKnxTAMoxhrybfjx48rJCRE69atU2RkpHX+sGHDtHr1am3YsMFumfXr12v//v1q1KiREhMTNW7cOK1Zs0a///67KleubDc+NjZWY8eOtZs/c+ZMeXl5Fe0OAQAAAACQRUpKinr16qXExET5+vrmOtZ0R9wLIjIy0ibk33777apfv76mTp2qV1991W788OHDFRMTY51OSkpSlSpV1L59+zxfMDPw8yvpCszH0zNd06cv1d0DBsj10qWSLsdcEhNLugIb9K89+jcPJuph+tce/ZsH+tfU6N880L+mRw/nwkT9m5OrZ347wnTBvUKFCnJ2dtbJkydt5p88eVKBgYEOrcPV1VVNmjTRgQMHsn3e3d1d7u7u2S7n6uqa/6KvMz6TOXO9dIkfWlmZrKd5e3JG/+bARD3M25Mz+jcH9G+pQP/mgP4tNejhbJiof3OSn+xpuovTubm5KTw8XMuXL7fOy8zM1PLly22OqucmIyNDu3btUlBQUHGVCQAAAADAdWG6I+6SFBMTo759+6pZs2aKiIjQxIkTlZycrP79+0uS+vTpo5CQEL355puSpFdeeUW33XabatWqpfPnz+vdd9/VkSNHNHDgwJLcDQAAAAAACs2Uwb1Hjx46deqURo8erRMnTqhx48ZatGiR9RZvR48elZPT/50scO7cOUVHR+vEiRMqW7aswsPDtW7dOjVo0KCkdgEAAAAAgCJhyuAuSYMHD9bgwYOzfW7VqlU20++9957ee++961AVAAAAAADXl+m+4w4AAAAAAP4PwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMzbXCfNGmSQkND5eHhoRYtWmjjxo0OLffNN9/IYrGoS5cuxVsgAAAAAADXgSmD++zZsxUTE6MxY8Zo69atCgsLU4cOHZSQkJDrcocPH9bQoUPVunXr61QpAAAAAADFy5TBfcKECYqOjlb//v3VoEEDTZkyRV5eXpo+fXqOy2RkZKh3794aO3asatSocR2rBQAAAACg+LiUdAFZpaWlacuWLRo+fLh1npOTk6KiorR+/focl3vllVdUqVIlPf744/rll19y3UZqaqpSU1Ot04mJiZKks2fPKj09vZB7UPw8PEq6AvPx8EhXSkqKznh4yNUwSrocczlzpqQrsEH/2qN/82CiHqZ/7dG/eaB/TY3+zQP9a3r0cC5M1L85uXDhgiTJcOC9M11wP336tDIyMhQQEGAzPyAgQH/88Ue2y/z666+aNm2atm/f7tA23nzzTY0dO9ZufvXq1fNdL8zh8mWpV6+SrsKkKlQo6QqQB/o3D/SwqdG/eaB/TY3+zQP9a3r0cC5KUf9euHBBfn5+uY4xXXDPrwsXLuixxx7Tp59+qgoOvjnDhw9XTEyMdTozM1Nnz55V+fLlZbFYiqtUFKOkpCRVqVJFx44dk6+vb0mXA+QL/YvSjP5FaUb/orSjh0s3wzB04cIFBQcH5znWdMG9QoUKcnZ21smTJ23mnzx5UoGBgXbj//rrLx0+fFidO3e2zsvMzJQkubi4aN++fapZs6bNMu7u7nJ3d7eZ5+/vX0R7gJLk6+vLDy2UWvQvSjP6F6UZ/YvSjh4uvfI60n6V6S5O5+bmpvDwcC1fvtw6LzMzU8uXL1dkZKTd+Hr16mnXrl3avn279XH//ffrzjvv1Pbt21WlSpXrWT4AAAAAAEXKdEfcJSkmJkZ9+/ZVs2bNFBERoYkTJyo5OVn9+/eXJPXp00chISF688035eHhoVtuucVm+atHz7POBwAAAACgtDFlcO/Ro4dOnTql0aNH68SJE2rcuLEWLVpkvWDd0aNH5eRkupMFUILc3d01ZswYu69AAKUB/YvSjP5FaUb/orSjh28eFsORa88DAAAAAIASwWFrAAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjvw/4WGhmrixIm5jrFYLJo/f/51qQc3nlWrVslisej8+fPFto28evTw4cOyWCzavn17sdWAG09sbKwaN25cbOt35LMRFxdnvWsMkJPi7tXsONKbJVEXSh/6F7khuKPYrVmzRp07d1ZwcHCBgm/btm313HPPOTTOYrHYPa5cuVKwwnHTevPNN9W8eXP5+PioUqVK6tKli/bt23ddth0aGmrXw5UrV74u28aNZfLkyWrUqJF8fX3l6+uryMhI/fzzz8Wyrat/EMr6ePTRR4tle7i5vPXWW7JYLHa/CxTVH9Ov/uEo62PkyJGFXjduTrGxsXb9VK9ePZsx9C/yy5S3g8ONJTk5WWFhYRowYIAefPDBYt1WdHS0XnnlFZt5Li60OfJn9erVGjRokJo3b64rV67o5ZdfVvv27bVnzx55e3sX+/ZfeeUVRUdHW6ednZ2LfZu48VSuXFlvvfWWateuLcMw9Pnnn+uBBx7Qtm3b1LBhw2yXSU9Pl6ura4G3uWzZMpt1e3p6FnhdgCRt2rRJU6dOVaNGjYp9W/v27ZOvr691ukyZMsW+Tdy4GjZsqGXLllmni/v3Ufr3xscRdxS7e+65R6+99pq6du2a45iPP/5YtWvXloeHhwICAtStWzdJUr9+/bR69Wq9//771r8gHj58OMf1eHl5KTAw0OZx1dy5c9WwYUO5u7srNDRU48ePz7Xu/fv364477pCHh4caNGigpUuX5m/HUWotWrRI/fr1U8OGDRUWFqa4uDgdPXpUW7ZssY6xWCz67LPP1LVrV3l5eal27dpasGCBzXp++ukn1alTR56enrrzzjtz7d1r+fj42PRwxYoVrc9NnjxZNWvWlJubm+rWrasvv/wy13Vt3LhRTZo0kYeHh5o1a6Zt27Y5/kKgVOvcubM6deqk2rVrq06dOnr99ddVpkwZ/fbbb9YxFotFkydP1v333y9vb2+9/vrrkv49whkQECAfHx89/vjjunz5skPbLF++vE3v+vn5SZJSU1P17LPPqlKlSvLw8FCrVq20adOmXNcVFxenqlWrysvLS127dtWZM2cK+EqgtLp48aJ69+6tTz/9VGXLlrV5LjQ0VJLUtWtXWSwW6/RVX375pUJDQ+Xn56dHHnlEFy5cyHN7lSpVsunfq8Hn3Llz6tOnj8qWLSsvLy/dc8892r9/f67rKuhnCDcOFxcXm36qUKGC9Tn6FwVBcEeJ27x5s5599lm98sor2rdvnxYtWqQ77rhDkvT+++8rMjJS0dHRio+PV3x8vKpUqZLvbWzZskXdu3fXI488ol27dik2NlajRo1SXFxctuMzMzP14IMPys3NTRs2bNCUKVP04osvFmY3UYolJiZKksqVK2czf+zYserevbt27typTp06qXfv3jp79qwk6dixY3rwwQfVuXNnbd++XQMHDtRLL71UqDrmzZunIUOG6L///a92796tJ598Uv3799fKlSuzHX/x4kXdd999atCggbZs2aLY2FgNHTq0UDWgdMrIyNA333yj5ORkRUZG2jwXGxurrl27ateuXRowYIDmzJmj2NhYvfHGG9q8ebOCgoL08ccfF2r7w4YN09y5c/X5559r69atqlWrljp06GD9vGS1YcMGPf744xo8eLC2b9+uO++8U6+99lqhakDpM2jQIN17772Kioqye+7qH35mzJih+Ph4mz8E/fXXX5o/f75+/PFH/fjjj1q9erXeeuutAtfRr18/bd68WQsWLND69etlGIY6deqk9PT0bMcXx2cIpc/+/fsVHBysGjVqqHfv3jp69Kj1OfoXBWIA15EkY968eTbz5s6da/j6+hpJSUnZLtOmTRtjyJAhea67TZs2hqurq+Ht7W19xMTEGIZhGL169TLuvvtum/EvvPCC0aBBA+t0tWrVjPfee88wDMNYvHix4eLiYvzzzz/W53/++eds68eNLSMjw7j33nuNli1b2syXZIwcOdI6ffHiRUOS8fPPPxuGYRjDhw+36S/DMIwXX3zRkGScO3cux+1Vq1bNcHNzs+nj999/3zAMw7j99tuN6Ohom/EPP/yw0alTJ5u6rvbo1KlTjfLlyxuXLl2yPj958mRDkrFt2zaHXwOUXjt37jS8vb0NZ2dnw8/Pz1i4cKHN85KM5557zmZeZGSk8cwzz9jMa9GihREWFpbjdg4dOmRIMjw9PW16d+vWrcbFixcNV1dX4+uvv7aOT0tLM4KDg4133nnHMAzDWLlypc1no2fPnjZ9bRiG0aNHD8PPzy+frwBKq1mzZhm33HKL9edXdr8LZPd/8pgxYwwvLy+b3yleeOEFo0WLFjlu62r/Xdu73t7exunTp40///zTkGSsXbvWOv706dOGp6enMWfOHMMwDGPGjBk2vVmQzxBuLD/99JMxZ84cY8eOHcaiRYuMyMhIo2rVqjZ9Sf8iv/jyL0rc3XffrWrVqqlGjRrq2LGjOnbsaD39OL969+6tESNGWKevXiVz7969euCBB2zGtmzZUhMnTlRGRobdd4j37t2rKlWqKDg42Dov61Eq3BwGDRqk3bt369dff7V77trvXHp7e8vX11cJCQmS/u2hFi1a2Ix3tIdeeOEF9evXzzp99fS6vXv36oknnrAZ27JlS73//vvZrmfv3r1q1KiRPDw88l0Dbgx169bV9u3blZiYqO+++059+/bV6tWr1aBBA+uYZs2a2Syzd+9ePfXUUzbzIiMjczyz41qzZ89W/fr1rdNVqlTRvn37lJ6erpYtW1rnu7q6KiIiQnv37s12PXv37rX7elVkZKQWLVqUZw0o/Y4dO6YhQ4Zo6dKlNj+/HBUaGiofHx/rdFBQkPVnc25++eUXm+XKli2rtWvXysXFxebnefny5VW3bt1c+7egnyHcGO655x7rvxs1aqQWLVqoWrVqmjNnjh5//PFcl6V/kROCO0qcj4+Ptm7dqlWrVmnJkiUaPXq0YmNjtWnTpnzf+sfPz0+1atUqnkJx0xk8eLB+/PFHrVmzJtsru2e9iJfFYlFmZmaht1uhQgX6GEXCzc3N2kvh4eHatGmT3n//fU2dOtU6pigvuFilShV6F4W2ZcsWJSQkqGnTptZ5GRkZWrNmjT766COlpqbmetHOgv5srl69OrccRLHw9/dXnTp1dODAgTzH0r/ICd9xhym4uLgoKipK77zzjnbu3KnDhw9rxYoVkv79xTMjI6NQ669fv77Wrl1rM2/t2rWqU6dOtv/5169fX8eOHVN8fLx13rUXdMKNzTAMDR48WPPmzdOKFStUvXr1fK+jfv362rhxo828wvZQTn187dHTrON37txpc1EZ+vjmlpmZqdTU1FzH1K9fXxs2bLCZV5i+uXoxxWt7Nz09XZs2bcq1d4uyBpQud911l3bt2qXt27dbH82aNVPv3r21fft26//brq6uhf79IC/169fXlStXbPrxzJkz2rdvH/0Lh128eFF//fWXgoKCrPPoX+QXR9xR7C5evGjzF8ZDhw5p+/btKleunKpWraoff/xRBw8e1B133KGyZcvqp59+UmZmpurWrSvp31OGNmzYoMOHD6tMmTIqV66cnJzy9zen//73v2revLleffVV9ejRQ+vXr9dHH32U48U2oqKiVKdOHfXt21fvvvuukpKSbE7Bx41t0KBBmjlzpr7//nv5+PjoxIkTkv49o8PR21s99dRTGj9+vF544QUNHDhQW7ZsyfFiiI564YUX1L17dzVp0kRRUVH64Ycf9L///c/mdjPX6tWrl0aMGKHo6GgNHz5chw8f1rhx4wpVA0qP4cOH65577lHVqlV14cIFzZw5U6tWrdLixYtzXW7IkCHq16+fmjVrppYtW+rrr7/W77//rho1ahSoDm9vbz399NN64YUXrD/333nnHaWkpOR4yuizzz6rli1baty4cXrggQe0ePFiTpO/ifj4+OiWW26xmeft7a3y5cvbzA8NDdXy5cvVsmVLubu72115vijUrl1bDzzwgKKjozV16lT5+PjopZdeUkhIiN1X8K4q6s8QSp+hQ4eqc+fOqlatmo4fP64xY8bI2dlZPXv2tI6hf5FfHHFHsdu8ebOaNGmiJk2aSJJiYmLUpEkTjR49WtK/pw/973//U7t27VS/fn1NmTJFs2bNst4LeOjQoXJ2dlaDBg1UsWJFm6tyOqpp06aaM2eOvvnmG91yyy0aPXq0XnnlFZvvEV/LyclJ8+bN06VLlxQREaGBAwdab5OEG9/kyZOVmJiotm3bKigoyPqYPXu2w+uoWrWq5s6dq/nz5yssLExTpkzRG2+8Uai6unTpovfff1/jxo1Tw4YNNXXqVM2YMUNt27bNdnyZMmX0ww8/aNeuXWrSpIlGjBiht99+u1A1oPRISEhQnz59VLduXd11113atGmTFi9erLvvvjvX5Xr06KFRo0Zp2LBhCg8P15EjR/T0008Xqpa33npLDz30kB577DE1bdpUBw4c0OLFi3P8RfW2227Tp59+qvfff19hYWFasmSJRo4cWagacOMZP368li5dqipVqlh/xygOM2bMUHh4uO677z5FRkbKMAz99NNPdqc0X1UcnyGULn///bd69uypunXrqnv37ipfvrx+++03m9u70r/IL4thGEZJFwEAAAAAALLHEXcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAG5CycnJeuONN9S0aVOVKVNG7u7uqly5slq3bq3hw4frr7/+0pIlS2SxWNShQ4c819e7d29ZLBbNnDlTkhQXFyeLxSKLxaJu3brluNzkyZOt4/r161dUuwcAwA3FpaQLAAAA19eFCxfUqlUr7dy5U7Vq1dKjjz6q8uXL6/Tp09q4caPeeust1axZUwMGDFDVqlW1bNkyHTt2TFWqVMl2fYmJiZo3b578/f314IMP2jzn4uKiH374QadPn1aFChXslp02bZpcXFx05cqVYtlXAABuBBxxBwDgJjNx4kTt3LlTAwcO1J9//qkpU6bo9ddf19SpU7Vt2zYdPHhQrVq1kpOTk/r376/MzEzFxcXluL6ZM2fq0qVL6t27tzw8PGyeu+eee5SWlqavvvrKbrmdO3dqy5Yt6tSpU1HvIgAANxSCOwAAN5n169dLkgYNGiSLxWL3fPXq1VWvXj1JUv/+/WWxWBQXFyfDMLJd3/Tp0yVJjz/+uN1zt99+u+rVq6cZM2Zku5yzs7P69u1b4H0BAOBmQHAHAOAmU758eUnSn3/+mefYatWqKSoqSgcPHtSqVavsnt+9e7c2b96sJk2aqEmTJtmuo3///taj61elpaXp66+/VocOHRQcHFywHQEA4CZBcAcA4Cbz8MMPS5IGDhyooUOHasmSJTpz5kyO468eSb96ZP1auR1tv6pPnz5ycXGxWf7777/X6dOnc10OAAD8i+AOAMBN5v7779f48eNlGIbGjx+vDh06qEKFCqpVq5YGDx6s/fv324zv0qWLypcvr7lz5yopKck6Pz09XV999ZU8PDzUq1evHLcXGBioTp06adasWbp8+bKkfwN/xYoV1blz5+LZSQAAbiAEdwAAbkIxMTE6fvy45syZo+eee06tWrXS0aNHNWnSJDVq1EgLFiywjnV3d9ejjz6qS5cuadasWdb5P/zwg06dOqWuXbuqbNmyuW5vwIABOnfunObNm6d//vlHS5Ys0aOPPipXV9di20cAAG4UBHcAAG5SPj4+evjhh/Xee+/pl19+0alTp/TMM8/o8uXLevzxx5WWlmYdm93p8o6cJn/Vvffeq4CAAE2fPl1xcXHKzMzUgAEDiniPAAC4MRHcAQCAJMnPz08fffSRqlWrptOnT2vXrl3W52699VY1b95cGzdu1O+//674+HgtWrRI1atXV7t27fJct4uLi/r06aMVK1Zo0qRJioiI0C233FKcuwMAwA2D4A4AAKwsFou8vb2zfe7qkfVp06bpiy++UEZGhvV2cY4YMGCAMjMzFR8fz9F2AADywaWkCwAAANfX1KlT1bRpUzVv3tzuufnz52vv3r3y9/e3OyLes2dPxcTE6KuvvlLZsmXl5OSkfv36ObzdevXq6eeff9bly5cVFRVV2N0AAOCmQXAHAOAm8/PPP+upp55SrVq11LJlSwUHBys5OVnbtm3TL7/8IicnJ3388cdyd3e3Wc7X11fdunXTF198oVOnTqljx46qUqVKvrbdsWPHotwVAABuCgR3AABuMm+//bZatmyppUuXas2aNYqPj5ckhYSEqG/fvvrPf/6j8PDwbJd9/PHH9cUXX0gSp7sDAHCdWAzDMEq6CAAAAAAAkD0uTgcAAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACY2P8DL2vq5Qd/ma0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9824561403508771, 'precision_score': 0.9855072463768115, 'recall_score': 0.9787234042553192, 'f1_score': 0.9817774936061381}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.99        67\n",
            "           1       1.00      0.96      0.98        47\n",
            "\n",
            "    accuracy                           0.98       114\n",
            "   macro avg       0.99      0.98      0.98       114\n",
            "weighted avg       0.98      0.98      0.98       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training with Decision Tree"
      ],
      "metadata": {
        "id": "Kvt2k47dN6Ty"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "decision_tree_model_2 = DecisionTreeClassifier()\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'min_samples_split': np.arange(2, 70),\n",
        "               'criterion':['gini', 'entropy']},\n",
        "              ]\n",
        "# n_jobs=-1 uses all the system's processors\n",
        "grid_search = GridSearchCV(estimator=decision_tree_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(X_train, Y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best F1 score {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rnoop6KsN4Gu",
        "outputId": "3a71e8d3-b688-4fe0-b38c-647f7949d0f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 score 90.47 %\n",
            "Best Parameters: {'criterion': 'entropy', 'min_samples_split': 13}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the best parameters to train the Decision Tree algorithm\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt_model = DecisionTreeClassifier(criterion=\"entropy\",\n",
        "                                             min_samples_split=5,\n",
        "                                             random_state=0)\n",
        "\n",
        "dt_train_val_result = cross_validation(dt_model, X_train, Y_train, 5)\n",
        "print(dt_train_val_result)\n",
        "\n",
        "# Plot Training and Validation Result with F1-score as metric\n",
        "model_name = \"Decision Tree\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=dt_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=dt_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "dt_model.fit(X_train, Y_train)\n",
        "dt_y_pred = dt_model.predict(X_test)\n",
        "# Get test result\n",
        "dt_test_result = evaluate_model(Y_test, dt_y_pred)\n",
        "print(dt_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(Y_test, dt_y_pred, label)\n",
        "print(classification_report(Y_test, dt_y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FcaXp3i5OJ_4",
        "outputId": "c5685f39-68f8-4e8f-a841-87f7cbb56e8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.99450549, 0.99725275, 0.99725275, 0.98901099, 0.99450549]), 'Mean Training Accuracy': 99.45054945054946, 'Training Precision scores': array([1.       , 0.9924812, 0.9924812, 1.       , 1.       ]), 'Mean Training Precision': 0.9969924812030075, 'Training Recall scores': array([0.98484848, 1.        , 1.        , 0.96969697, 0.98484848]), 'Mean Training Recall': 0.9878787878787879, 'Training F1 scores': array([0.99236641, 0.99622642, 0.99622642, 0.98461538, 0.99236641]), 'Mean Training F1 Score': 0.992360207846309, 'Validation Accuracy scores': array([0.91208791, 0.95604396, 0.89010989, 0.91208791, 0.92307692]), 'Mean Validation Accuracy': 91.86813186813187, 'Validation Precision scores': array([0.90322581, 0.96774194, 0.87096774, 0.85714286, 0.88235294]), 'Mean Validation Precision': 0.8962862564380591, 'Validation Recall scores': array([0.84848485, 0.90909091, 0.81818182, 0.90909091, 0.90909091]), 'Mean Validation Recall': 0.8787878787878787, 'Validation F1 scores': array([0.875     , 0.9375    , 0.84375   , 0.88235294, 0.89552239]), 'Mean Validation F1 Score': 0.8868250658472345}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9385964912280702, 'precision_score': 0.9379795396419437, 'recall_score': 0.9350587488091457, 'f1_score': 0.9364396654719236}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.96      0.95        67\n",
            "           1       0.93      0.91      0.92        47\n",
            "\n",
            "    accuracy                           0.94       114\n",
            "   macro avg       0.94      0.94      0.94       114\n",
            "weighted avg       0.94      0.94      0.94       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training with Random Forest"
      ],
      "metadata": {
        "id": "ORD3yrjjOh7N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "random_forest_model_2 = RandomForestClassifier()\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'min_samples_split': np.arange(10,70, 5),\n",
        "               'criterion': ['gini', 'entropy'],\n",
        "               'n_estimators': np.arange(80,150, 10)}\n",
        "              ]\n",
        "# n_jobs=-1 uses all the system's processors\n",
        "grid_search = GridSearchCV(estimator=random_forest_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(X_train, Y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best F1 score {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2dj6lgMOhWP",
        "outputId": "17ea555c-d529-4466-b7c2-7433de616960"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 score 93.77 %\n",
            "Best Parameters: {'criterion': 'gini', 'min_samples_split': 10, 'n_estimators': 140}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the best parameters to train the Random Forest algorithm\n",
        "rf_model = RandomForestClassifier(n_estimators=130,\n",
        "                                  min_samples_split=10,\n",
        "                                  criterion=\"entropy\",\n",
        "                                  random_state=0)\n",
        "\n",
        "rf_train_val_result = cross_validation(rf_model, X_train, Y_train, 5)\n",
        "print(rf_train_val_result)\n",
        "\n",
        "# Plot Training and Validation Result with F1-score as metric\n",
        "model_name = \"Random Forest\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=rf_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=rf_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "rf_model.fit(X_train, Y_train)\n",
        "rf_y_pred = rf_model.predict(X_test)\n",
        "# Get test result\n",
        "rf_test_result = evaluate_model(Y_test, rf_y_pred)\n",
        "print(rf_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(Y_test, rf_y_pred, label)\n",
        "print(classification_report(Y_test, rf_y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WioLKdetO6V4",
        "outputId": "ea78d12b-93da-4eea-a995-dcfd59f604f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.99175824, 0.99725275, 0.99725275, 0.99175824, 0.99175824]), 'Mean Training Accuracy': 99.3956043956044, 'Training Precision scores': array([1., 1., 1., 1., 1.]), 'Mean Training Precision': 1.0, 'Training Recall scores': array([0.97727273, 0.99242424, 0.99242424, 0.97727273, 0.97727273]), 'Mean Training Recall': 0.9833333333333334, 'Training F1 scores': array([0.98850575, 0.99619772, 0.99619772, 0.98850575, 0.98850575]), 'Mean Training F1 Score': 0.9915825357283335, 'Validation Accuracy scores': array([0.92307692, 0.96703297, 0.93406593, 0.95604396, 0.97802198]), 'Mean Validation Accuracy': 95.16483516483515, 'Validation Precision scores': array([0.96428571, 0.96875   , 0.96551724, 0.96774194, 0.94285714]), 'Mean Validation Precision': 0.9618304068012078, 'Validation Recall scores': array([0.81818182, 0.93939394, 0.84848485, 0.90909091, 1.        ]), 'Mean Validation Recall': 0.9030303030303031, 'Validation F1 scores': array([0.8852459 , 0.95384615, 0.90322581, 0.9375    , 0.97058824]), 'Mean Validation F1 Score': 0.9300812194462458}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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qqUiRInlS79GjR22ma9eune11hIaG2kxn9Bi3tCpUqODwNtKG7M6dO8tisWTrNWDAAJt1nD9/3m47zz33nPz9/a3TsbGxioiIUKlSpdShQwdNnDhRmzZtKhRHDm8O4o7w9va2/jvtac4FpXLlynanZ//222/pfnZmlNvvV8WKFeXj42OdPn78uAzDyFVNhw8ftpm+6667srX83Xff7XDftJfSrFixQk2aNFGFChX03HPPafbs2XbvEQDkFYI7ACBDrq6u+vXXX1WtWjWb9qSkJC1dulTDhg1Ts2bNVLRoUbVq1UoTJ060OeKdnrQhJbuBLDMXLlywmb756Kaj0i6Tdp0ZcfQ0eUk6d+5ctmpyRHpHlYsXL64FCxYoMDDQpj0hIUG//PKLXn75ZTVq1EjFixdX27Zt9eWXX5r2We4eHh4FXUKe6Nmzp0qVKmWdTk1NTfd56GaUF9+vm89QSElJ0aVLl3JVU9rx6ugZEDnp3717d7355pt29ys4cuSIPvvsM3Xv3l0hISEKCQnRM888oxUrVuT6DxMAcAPBHQCQqYoVK2rr1q16++237a7RvSExMVHR0dHWG8G9+OKLGf5CnrY9r462S/+dEXCzm4+6OirtMo4Gi+xco54f4Tg1NTXd9gYNGmj37t169dVXVbx48XT7XL58Wb///rsiIiJUrlw5RUVFFYqj8IWRk5OT3enZ+/btK5hisqkgv1+O1uTl5ZWrerIyfPhwrV+/Xm3btpWzs3O6fY4ePaovvvhC999/v2rXrq2lS5dmaxsAkB6Xgi4AAGB+Xl5eev311zV06FCtXbtWK1asUHR0tGJiYuxOQ05KStLHH3+sJUuWaPXq1XZH1G8+VVay/8U7N9L+EeDmU/IdlXaZtPXmhbTh4t1331X9+vVztc5atWplOK948eJ6//339dZbbyk6OlorV67UqlWrtHnzZruAfunSJY0ePVrLli3TkiVLsh2EkLWyZcvaTJ85c6aAKskeM36/0gbvhISEbK0zJ/vQuHFjLVy4UCdPntSSJUsUHR2t1atX69ChQ3Z9d+/erTZt2mj8+PF6+eWXs70tALiB4A4AcJiTk5OaNWumZs2aadSoUUpOTtbmzZu1aNEizZo1SwcOHLD23bdvn3r37q3ffvvNZh1pj/rGxsbmWX3FihWzmc7JKelp786edp15Ie0pxhUqVFB4eHiebyctNzc3PfDAA9Y70l+9elUbNmzQb7/9plmzZtnc6X7t2rUaPHiwPv3003yv606T9uyMwnJ2Q158v25extnZOdfBvWjRojbTZ8+ezdY6c3PZSmBgoHr16qVevXpJkk6cOKEVK1bop59+0i+//GL9XA3D0CuvvKKwsDCbR+IBQHZwqjwAIMdcXV0VFham0aNH6++//9bkyZNt7vz9+++/292ILSAgwObI3b59+/LsqHv58uVtpnPyqK20y6RdZ15IeyO7m//gcSt5enqqVatW+uCDD3TkyBG98cYbNvO//PJLxcfHF0htt7NTp07ZTN98zbuZ5fb7dejQIZtT48uVK2d3vXh2VaxY0WZ69+7d2Vp+586dudr+zYKCgvTkk0/q+++/1/79+9WwYUPrPMMw9OGHH+bZtgDceQjuAIA8YbFY9Pzzz6t79+427X/88YfNtIuLi8LCwqzTqampWrRoUZ7UEBISYnMX9aNHj6Z7+mpGLl26pM2bN9vUmpNHtWWlVatWNtMrVqzI821kl6urq9566y01bdrU2pacnGz3CDDkXtrvRNpT580q7dHi7I7btP3z4ujzzeFYklatWpWt5bPb31Hly5fXrFmzbNrSfu4AkB0EdwBAnmrSpInNdNpTzyXZPbf9o48+yrPtt2jRwmZ6xowZDi87e/Zsm2v2GzRokC/XeDdq1MjmtOMVK1Zoz549eb6dnHDk80POrVq1SgcPHrRpS/t8c7O655575ObmZp3+448/snW2yLRp02ym035XcyLtjf7mzJnj8KUHO3bs0Pbt23NdQ0YqV65s84dEvksAcoPgDgDIU45cI963b1+b61DXrFmjL774Ik+2369fP5vpCRMm6J9//slyufj4eLvnNEdERORJTWm5urrqpZdesk4bhqFnn33WFNc634pr/O9UV65c0YsvvmjTdvfdd9ud7m1WRYsW1eOPP26dNgxDgwcPdmjZ77//XuvXr7dOFylSRN26dct1TTVr1lTjxo2t0//++6/Dfwh89dVXc739zCQlJdlcasJ3CUBuENwBABnq0aNHtk4lvXDhgr788kubtvTull60aFENGjTIpm3gwIGaM2eOQ9tJTk7WmjVr0p0XHh6u2rVrW6evXLmiDh06pPuc8xuSkpL0xBNP6OTJk9a2wMBAu9P+89KgQYNsjsb98ccfevzxxzOtM60rV67oo48+0ldffWU3Lz4+Xj169NC2bdscXt+RI0f0/fffW6ednJxUp04dh5e/kwwaNMjmZn5ZOXv2rB555BG7a6pHjx6d16Xlq5dfftnmPhY///yz3nrrrUyX2b59u90f1Pr16ydfX988qenmP4JJ0htvvKGVK1dmusyIESOy9Zi2WbNmaezYsXbPss/M5MmTbc7gye2TIwDc2QjuAGASa9eu1bJly+xeW7Zssel37dq1dPstW7Ysz0+3XrhwoVq2bKmaNWtq1KhRWr9+vRISEuz6Xb16Vd99950aN26so0ePWttDQ0PtrkG9YeTIkTbXuCYlJalbt2567LHHtHbtWqWkpNj0v379urZs2aLhw4erUqVKGjFiRLrrtVgsmj59us2du7du3ao6depo/vz5SkpKsranpqZq6dKlatCggZYsWWKznmnTpsnDwyOTdyd3/Pz89N1339nU+csvv6hWrVoaP368jh07lu5yx48f1/fff68nn3xSQUFBGjRokI4fP27XLzU1VbNmzVK9evXUsGFDvffee9q6davN/t8QHx+vr776SmFhYTZ/OGjfvr0CAgLyYG9vPx999JEqVqyoTp066X//+5+OHDmSbr/jx4/rgw8+0N133213jXfHjh3VqVOnW1Bt3mnQoIHdY81GjBihxx9/3O7GcBcuXNC4cePUpEkTm3FVqVKlLMN+dnTt2tXmqQyJiYl68MEHNXr0aLtH7e3Zs0ePP/64dfshISEObSM2Nlavv/66ypQpo8cff1yzZ8+2+Vl3s7///lsDBw7UK6+8YtOeX2fwALhDGACALJUvX96QZH2tXLky37eRk1evXr3ytCY/Pz+7bTg7Oxvly5c36tatazRu3NioVq2a4erqatfPy8vL2LJlS6br/+eff4xatWqluy8+Pj5GzZo1jcaNGxvVq1c3PDw8bOa3aNEi03V//vnnhpOTU7rrvfvuu406deoYxYoVS3fbo0ePzvK9GTVqVJ6MidmzZ9vt241XYGCgERoaajRq1MioVq1ahvWOGjXKbr0XLlxIt6+bm5tRsWJFo379+kajRo2MSpUqGc7Oznb9SpUqZRw7dixH+5SR7Lxnhw8fztXYbtGihc3yeS2999bX19eoVq2a0ahRI6Nu3bpGUFBQht/VZs2aGQkJCXle1w3Tp0/P1vclO65du2aEh4enu19BQUFGgwYNMvy5UKJEiSx/LhhG9j+/48ePp/sz1MXFxahSpYrRsGFDo0yZMjbzgoODjTlz5mT5XTIMw/jwww/T3d+iRYsa1atXN+655x6jXr16RqlSpdLt17Vr1+y+zQBgg+e4AwCyJSUlRUePHs3waJMkBQcH67vvvlO9evUyXVdwcLDWrl2rnj176pdffrGZd+nSpVydQRARESE/Pz/169fP5hFUly5d0q5du9Jdxt3dXePHj9eAAQNyvN3s6tq1q6pUqaLu3bvr77//tpl38uRJm9P30+Ps7KygoCCHt5eUlJTlnfarV6+un3/+udDc7dws4uPjs3x8npOTkwYPHqy33nrL7nnuhYW7u7sWLlyop59+Wt9++63NvBMnTmR4CUGVKlX0yy+/qHr16nleU5kyZbR8+XK1adPG5uZ/169f1/79++36BwUFadGiRbm+YdzFixd18eLFTPtERETo008/zdV2AIBT5QEAGdq4caPef/993X///fL29s6yf+XKlfXmm29q3759No98y4yfn59+/vlnrVy5Ug888IDNXavTU61aNQ0fPlxff/11luvu3LmzDhw4oAEDBqh48eIZ9vPx8VHPnj31119/3dLQfkP9+vW1Z88eff3117rnnnvk7OycaX93d3fdd999GjdunI4fP65nnnnGrk/RokW1bds2RUVFqUmTJnJ3d8+yjtq1a+ujjz7Szp07VbVq1Rzvz53g888/V9euXR3+40ZAQIAGDRqkffv26b333iu0of0GNzc3ffPNN4qOjtZ9990nF5eMjwVVqlRJ48eP1+7du/MltN+8nZ07d+q1117L8EZw7u7uioiI0I4dO3TXXXc5vO5nn31WP//8s/r166fKlStn2d/d3V2dOnXSmjVr9Pnnn2f6/gCAIyyGYRgFXQQAwPxSUlK0d+9e7d+/X//++6/1KLaPj4+Cg4NVp04dVahQIdfbuXLlitauXat//vlHZ8+eVUpKinx9fVWhQgXVrl1bZcqUyXH9Gzdu1P79+xUbG6vU1FSVKlVKFStW1L333muqIBUXF6cNGzboxIkTOnv2rJKTk+Xj46PSpUurevXqqlatWravv09MTNSePXt04MABnTx5UpcvX5bFYpGvr69CQkJUp04dBQcH59Me3d7OnTunvXv36ujRozpz5oyuXLkiZ2dnFStWTCVLllTdunULzZ3jc+rixYv6448/dOLECZ07d07e3t7y9/dXnTp1VK1atVteT1JSklatWqVDhw7p7Nmz8vb2VpUqVdS8eXObJ1rk1JkzZ7Rnzx4dOnRI58+fV0JCgry8vFSsWDFVr15doaGhDv2xEwAcRXAHAAAAAMDEOFUeAAAAAAATI7gDAAAAAGBipgvuq1evVvv27RUUFCSLxaKffvopy2Wio6NVr149ubu7q3LlypoxY0a+1wkAAAAAwK1guuB+5coVhYaGavLkyQ71P3z4sNq1a6dWrVpp+/bteumll9SvXz8tXrw4nysFAAAAACD/mfrmdBaLRT/++KM6duyYYZ/XXntNCxcu1O7du61tXbt21cWLF7Vo0aJbUCUAAAAAAPmn0D9Ucv369QoPD7dpa9OmjV566aUMl0lMTFRiYqJ1OjU1VefPn1eJEiVksVjyq1QAAAAAACRJhmHo0qVLCgoKkpNT5ifDF/rgfurUKfn7+9u0+fv7Kz4+XlevXpWnp6fdMmPHjtXo0aNvVYkAAAAAAKTr+PHjKlOmTKZ9Cn1wz4lhw4YpMjLSOh0XF6dy5crp8OHD8vHxKcDKkFPJyclauXKlWrVqJVdX14IuB8gWxi8KM8YvCjPGLwo7xnDhdunSJVWoUMGhDFrog3tAQIBOnz5t03b69Gn5+vqme7Rdktzd3eXu7m7XXrx4cfn6+uZLnchfycnJ8vLyUokSJfihhUKH8YvCjPGLwozxi8KOMVy43fjMHLlc23R3lc+usLAwLV++3KZt6dKlCgsLK6CKAGTGYuGV9uXnV9CfChxV0GPFjC/GLwAA+c90wf3y5cvavn27tm/fLum/x71t375dx44dk/Tfae49e/a09n/uued06NAhDRkyRH/99Zc+/fRTzZs3Ty+//HJBlA8AAAAAQJ4yXXDfvHmz6tatq7p160qSIiMjVbduXY0cOVKSdPLkSWuIl6QKFSpo4cKFWrp0qUJDQzV+/Hh9+eWXatOmTYHUDwAAAABAXjLdNe4tW7ZUZo+WnzFjRrrLbNu2LR+r+u9W/devX1dKSkq+bgc5k5ycLBcXF127ds2hz8jZ2VkuLi7i8X8AAAAAzM50wd2MkpKSdPLkSSUkJBR0KciAYRgKCAjQ8ePHHQ7jXl5eCgwMlJubWz5XBwAAAAA5R3DPQmpqqg4fPixnZ2cFBQXJzc2No7QmlJqaqsuXL6tIkSJycsr8ChDDMJSUlKQzZ87o8OHDqlKlSpbLAAAAAEBBIbhnISkpSampqSpbtqy8vLwKuhxJ0ubNBV2B+Tg5papixSR5eHg4FMI9PT3l6uqqo0ePKinpv+UAALidcdzBnqenNHt2QVcBRzB+08cYvnNwmNFBHJG9/fCZAgAAACgMSC4AAAAAAJgYwR3Z8sgjIZo1a6LD/bdsiVbDhhZdunQx32oCAAAAgNsZ17jnwq2+1iaTp+TZadgw8+IiIkbpmWeisl3DzJmb5Onp7XD/2rXv1e+/n1SRIn7Z3hYAAAAAgOB+2/r995PWfy9dOleffTZS33+/z9rm5VXE+m/DMJSSkiIXl6yHQ7FipbJVh6urm0qWDMjWMgAAAACA/8Op8repkiUDrK8iRfxksVis00eP/qUWLXy0du3veuqp+rr3Xnft2PGH/vnnoF55pYPatPFX8+ZF1LNnQ8XELLNZb9pT5Rs2tOinn77Uq692UtOmXnr00SpateoX6/y0p8r/+usMtWpVVOvXL9YTT9RQ8+ZF9MILD+rs2f/7Q8P169c1btyLatWqqMLDS+jjj19TVFQvDR7cMT/fMgAAAAAwJYL7HWzy5KEaOPBdfffdXlWuXFsJCZfVpElbTZ68XN9+u01hYQ/qlVfa69SpY5mu54svRis8vLNmz96pe+9tq5Ejeygu7nyG/a9dS9C3347T6NHf6PPPV+v06WOaOHGwdf7XX7+nRYv+p5Ejp+vLL9fqypV4RUf/lFe7DQAAAACFCsH9Dvbss2PUuHFrlSlTSX5+xVW1aqgeffRZVa58l8qVq6L+/d9UcHAlrV79S6brefjh3mrTppvKlq2sAQPeUULCZf3558YM+1+/nqxhw6aqZs0Gql69np54YqA2bVpunT937sfq1WuYWrXqpJCQ6nr11U/k41M0r3YbAAAAAAoVrnG/g9Wo0cBmOiHhsj7/PEpr1y7U2bMnlZJyXYmJV7M84l6lSm3rvz09veXt7asLF2Iz7O/h4aUyZSpZp0uWDLT2v3w5TufPn1atWo2s852dnVW9en0ZRmq29g8AAAAAbgcE9ztY2rvDT5o0WDExSzVo0DiVLVtZ7u6eeu21x5WcnJTpelxcXG2mLRaLUlMzDtnp9Teyc8t8AAAAALiDcKo8rHbsWKuHH+6tVq06qXLlu1WiRIBOnjxyS2soUsRPxYv7a8+eTda2lJQU7du39ZbWAQAAAABmwRF3WJUtW0UrV/6gZs3ay2KxaOrUEQVyenqXLi9oxoyxKlu2ssqXr6558z5WfPwFWSyZP5seAAAAAG5HBHdYvfzyBL35Zl89/fS9Klq0pHr2fE1XrsTf8jp69nxN586d0qhRPeXs7KyOHZ9RWFgbOTk53/JaAAAAANPz85OuXi3oKszlNrsU12JwcbHi4+Pl5+enuLg4+fr62sy7du2aDh8+rAoVKsjDw6OAKrS1eXNBV3Brpaam6oknaig8vLP6938z3T5OTqmqWDFevr6+cnJy7AoQM362dwJOnLDn6Zms2bN/U9u2beXq6pr1AigwjF97jN/Cg/Frj/FbeDB+02cdw926yZXgbqsQxNzMcmhaHHGH6Zw8eVQbNixRvXotlJycqHnzPtGJE4f14IPdC7o0AAAAALjlCO4wHYvFSQsWzNCkSYMlGapY8S5NnrxMFSrUKOjSAAAAAOCWI7jDdAICyuqrr9YWdBkAAAAAYAo8Dg4AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnBHhp59tqXGj3/JOv3IIyGaNWtipss0bGhRdPRPud52Xq0HAAAAAAo7gntuWCy39pUNL7/cXi+88GC687ZtW6OGDS3av39nttY5c+YmPfroM9laJiuffx6l7t3r2LX//vtJ3XvvQ3m6LQAAAAAojAjut6kOHZ7Wxo1Ldfr0P3bzfv11umrUaKAqVWpna53FipWSh4dXXpWYqZIlA+Tm5n5LtgWYhp/frf+DYGF4AQAA3OEI7reppk0fVtGipbRgwQyb9oSEy1q+/Du1bNlRb7zRTW3bBqtpUy917Xq3Fi+enek6054qf+zYfj3zTHM1aeKhzp1rKiZmqd0yH3/8mh57rKqaNvVShw4VNWXKCF2/nixJ+vXXGfrii9Hav3+HGja0qGFDi3799b96054qf+DALvXvf5+aNvVUeHgJvf32M0pIuGydP2pUH/Xo0UPjx49XYGCgSpQooQEDBig5OTl7bxwAAAAAmIxLQReA/OHi4qJ27XpqwYIZ6tv3DVn+/1GrZcu+U0pKih566EktW/adevZ8Td7evlq7dqFGjXpKZcpUUq1ajbJcf2pqqoYMeVTFi/tr+vQYXb4cpwkTXrLr5+Xlo5EjZ6hUqSAdOLBLb78dIW9vH/XsOUStW3fRwYO7tX79Ik2evEySVKSIn906rl69ohdeaKO77w7TjBmbdOFCrN56q5/ef3+goqJmWPutWbNGZcuW1cqVK3XgwAF16dJFderUUURERM7eRAAAAAAwAY6438YeeaSv/vnnoLZuXWVtW7Bguu677zEFBpbXU08NVrVqdVSmTEV16fKCwsIe1NKl8xxa98aNy3TkyF8aPfprVa0aqnr1muv559+x6/f008MVGnqvgoJC1Lx5ez355GAtW/bfNjw8POXlVUTOzi4qWTJAJUsGyMPD024dixbNUlLSNY0e/bUqV75LDRvepyFDPtHvv3+jc+dOW/sVLVpUH3/8sapXr66HH35Y7dq10/Lly7P7tgEAAACAqXDE/TYWElJdtWvfq19+mab69Vvq+PED2rZtjaZOHaOUlBRNn/6Oli2bpzNn/lVycpKSkhIdvob98OG98vcvq1KlgqxttWuH2fVbsmSu5s79SP/8c1BXr15WSsp1eXv7Zms/jhzZqypVQuXp6W1tCw1totTUVB09uk8lSvhLkqpXry5nZ2drn8DAQO3atStb2wIAAAAAs+GI+22uQ4entWLFfF25ckm//jpdZcpUUr16LfTNNx9ozpxJ6tnzNU2ZslL/+992hYW1UXJyUp5te+fO9Ro5soeaNGmrDz9coG+/3aY+fd7I023czNXV1WbaYrEoNTU1X7YFAAAAALcKwf02Fx7eWU5OTlq8eJYWLvxajzzSVxaLRTt2rFWLFh3Utu2Tqlo1VMHBFXXs2N8Or7dChRo6ffq4zp49aW3btWuDTZ+dO9cpIKC8+vZ9QzVrNlC5clV06tRRmz6urm5KTU3JdFshITW0f/8OXb16xdq2Y8daOTk5qXz5ag7XDAAAAACFEcH9NuflVUStW3fR5MnDdO7cST38cG9JUrlyVRQTs1Q7dqzT4cN79c47z9pcL56VRo3CVa5cVUVF9dLff+/Qtm1rNGXKGzZ9ypatolOnjmnJkjn655+DmjPnI0VH/2jTJzAwRCdOHNa+fdt18eJZJSUl2m3roYd6yM3NQ1FRvXTgwG5t3rxSH3zwgh566CnrafIAAAAAcLsiuN8BHnnkacXHX9A997SxXpPet+9wVa9eTy++2EbPPddSJUoEqGXLjg6v08nJSR988KMSE6+qd+9Geuutfurf/22bPi1aPKLu3V/W++8PVI8edbRz5zr17TvCps999z2msLAH1b9/K7VuXSrdR9J5eHjp448XKz7+vHr3bqihQx9Xw4b3a8iQT7L/ZgAAAABAIWMxDMMo6CIKWnx8vPz8/BQXFydfX9sbp127dk2HDx9WhQoV5OHhUUAV2tq8uaArMB8np1RVrBgvX19fOTk59vcoM362d4L//2RC3MTTM1mzZ/+mtt26yfXq1YIux3xM9N8U49eedfy2bWt3rxGYC+PXHuO38GD8po/fITJhot8fMpJZDk2LI+4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIuBV0AAAAAUKD8/CSuD7ZXCK4RBu4UHHEHAAAAAMDECO4O4ub7tx8+UwAAAACFAcE9CzceDZKQkFDAlSCv3fhMefwLAAAAADPjGvcsODs7q2jRooqNjZUkeXl5ycKDJE3HMFKVlJSka9euZfkcd8MwlJCQoNjYWBUtWlTOzs63qEoAAAAAyD6CuwMCAgIkyRreC9rZswVdgflYLIakq/L09HT4DytFixa1frYAAAAAYFYEdwdYLBYFBgaqdOnSSk5OLuhy9NBDBV2B+Xh4JGv8+NVq3ry5Q6e+u7q6cqQdAAAAQKFAcM8GZ2dnU4S9o0cLugLz8fR01vXr1+Xh4cE16wAAAABuK9ycDgAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmJgpg/vkyZMVEhIiDw8PNW7cWBs3bsywb3JyssaMGaNKlSrJw8NDoaGhWrRo0S2sFgAAAACA/GO64D537lxFRkZq1KhR2rp1q0JDQ9WmTRvFxsam23/48OH67LPP9PHHH2vPnj167rnn1KlTJ23btu0WVw4AAAAAQN4zXXCfMGGCIiIi1KdPH9WsWVNTp06Vl5eXpk2blm7/b775Rq+//rratm2rihUrqn///mrbtq3Gjx9/iysHAOAO5ucnWSy80r4AAMgDpgruSUlJ2rJli8LDw61tTk5OCg8P1/r169NdJjExUR4eHjZtnp6e+uOPP/K1VgAAAAAAbgWXgi7gZmfPnlVKSor8/f1t2v39/fXXX3+lu0ybNm00YcIENW/eXJUqVdLy5cv1ww8/KCUlJcPtJCYmKjEx0TodHx8v6b/r5ZOTk/NgT/KXp2dBV2A+np7/fW6F4fO70zF+7VnHL29O+kz0veYjssf4zQLj19QYv1lg/JoeYzgTJhq/GclOdrEYhmHkYy3ZcuLECQUHB2vdunUKCwuztg8ZMkSrVq1STEyM3TJnzpxRRESEfv31V1ksFlWqVEnh4eGaNm2arl69mu52oqKiNHr0aLv2WbNmycvLK+92CAAAAACAdCQkJKh79+6Ki4uTr69vpn1NdcS9ZMmScnZ21unTp23aT58+rYCAgHSXKVWqlH766Sddu3ZN586dU1BQkIYOHaqKFStmuJ1hw4YpMjLSOh0fH6+yZcvqgQceyPINMwM/v4KuwHw8PZM1bdpStW7dWq6urgVdDjLB+LVnHb99+8o1gz843tHi4gq6AivGrz3GbxYYv6bG+M0C49f0GMOZMNH4zciNM78dYarg7ubmpvr162v58uXq2LGjJCk1NVXLly/XwIEDM13Ww8NDwcHBSk5O1vz589W5c+cM+7q7u8vd3d2u3dXVtVCEPr6TGSssn+GdjPGbMderV/lPNz0m+k7z8WSM8ZsBxm+hwPjNAOO30GAMp8NE4zcj2cktpgrukhQZGalevXqpQYMGatSokSZOnKgrV66oT58+kqSePXsqODhYY8eOlSTFxMTo33//VZ06dfTvv/8qKipKqampGjJkSEHuBgAAAAAAecJ0wb1Lly46c+aMRo4cqVOnTqlOnTpatGiR9YZ1x44dk5PT/90M/9q1axo+fLgOHTqkIkWKqG3btvrmm29UtGjRAtoDAAAAAADyjumCuyQNHDgww1Pjo6OjbaZbtGihPXv23IKqAAAAAAC49Uz1HHcAAAAAAGCL4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxEx5V3kgx/z8pKtXC7oKczGMgq4AAAAAQC5wxB0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAAAAAYGIEdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMVMG98mTJyskJEQeHh5q3LixNm7cmGn/iRMnqlq1avL09FTZsmX18ssv69q1a7eoWgAAAAAA8o/pgvvcuXMVGRmpUaNGaevWrQoNDVWbNm0UGxubbv9Zs2Zp6NChGjVqlPbu3auvvvpKc+fO1euvv36LKwcAAAAAIO+ZLrhPmDBBERER6tOnj2rWrKmpU6fKy8tL06ZNS7f/unXr1KRJE3Xv3l0hISF64IEH1K1btyyP0gMAAAAAUBi4FHQBN0tKStKWLVs0bNgwa5uTk5PCw8O1fv36dJe599579e2332rjxo1q1KiRDh06pN9++01PPfVUhttJTExUYmKidTo+Pl6SlJycrOTk5Dzam/zj6VnQFZiPp+d/n1syb449k41pPiJ7jN8smGgM8xHZY/xmgfFraozfLDB+TY8xnAkTjd+MZCd7WgzDMPKxlmw5ceKEgoODtW7dOoWFhVnbhwwZolWrVikmJibd5T766CMNHjxYhmHo+vXreu655zRlypQMtxMVFaXRo0fbtc+aNUteXl653xEAAAAAADKRkJCg7t27Ky4uTr6+vpn2NdUR95yIjo7WO++8o08//VSNGzfWgQMHNGjQIL355psaMWJEussMGzZMkZGR1un4+HiVLVtWDzzwQJZvmBn4+RV0Bebj6ZmsadOWqnXfvnK9erWgyzGXuLiCrsAG49ce4zcLJhrDjF97jN8sMH5NjfGbBcav6TGGM2Gi8ZuRG2d+O8JUwb1kyZJydnbW6dOnbdpPnz6tgICAdJcZMWKEnnrqKfXr10+SdPfdd+vKlSt65pln9MYbb8jJyf4yfnd3d7m7u9u1u7q6ytXVNQ/2JH/xncyY69Wr/NBKy2Rjmo8nY4zfDJhoDPPxZIzxmwHGb6HA+M0A47fQYAynw0TjNyPZyZ6mujmdm5ub6tevr+XLl1vbUlNTtXz5cptT52+WkJBgF86dnZ0lSSa6CgAAAAAAgBwx1RF3SYqMjFSvXr3UoEEDNWrUSBMnTtSVK1fUp08fSVLPnj0VHByssWPHSpLat2+vCRMmqG7dutZT5UeMGKH27dtbAzwAAAAAAIWV6YJ7ly5ddObMGY0cOVKnTp1SnTp1tGjRIvn7+0uSjh07ZnOEffjw4bJYLBo+fLj+/fdflSpVSu3bt9fbb79dULsAAAAAAECeMV1wl6SBAwdq4MCB6c6Ljo62mXZxcdGoUaM0atSoW1AZAAAAAAC3lqmucQcAAAAAALYI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMzCU3C2/btk2zZ8/WX3/9pYSEBC1btkySdPToUcXExCg8PFzFixfPk0IBAAAAALgT5Ti4DxkyROPHj5dhGJIki8VinWcYhrp3767x48dr0KBBua8SAAAAAIA7VI5OlZ8+fbrGjRunhx9+WDt37tSwYcNs5oeEhKhRo0b65Zdf8qRIAAAAAADuVDk64v7pp5+qRo0amj9/vlxcXOTm5mbXp3r16tZT5wEAAAAAQM7k6Ij7nj171Lp1a7m4ZJz7/f39FRsbm+PCAAAAAABADoO7i4uLkpKSMu1z4sQJFSlSJEdFAQAAAACA/+QouN99991asWKFUlJS0p1/4w7z9evXz1VxAAAAAADc6XIU3Pv27au///5bzz33nBITE23mxcfHq3fv3jp16pQiIiLypEgAAAAAAO5UObo5Xd++fbVs2TJ99dVXmjt3rooWLSpJatSokfbu3asrV66od+/eevzxx/OyVgAAAAAA7jg5OuIuSbNmzdJnn32mChUq6N9//5VhGNq8ebPKlSunKVOmaNq0aXlZJwAAAAAAd6QcHXG/ISIiQhEREbp69aouXLggX19fbkgHAAAAAEAeytERd2dnZ/Xo0cM67enpqaCgIEI7AAAAAAB5LEfB3dfXV2XLls3rWgAAAAAAQBo5Cu6NGjXSjh078roWAAAAAACQRo6Ce1RUlFasWKGvv/46r+sBAAAAAAA3ydHN6ZYuXaqWLVuqT58++vjjj9WwYUP5+/vLYrHY9LNYLBoxYkSeFAoAAAAAwJ0oR8E9KirK+u8tW7Zoy5Yt6fYjuAMAAAAAkDs5Cu4rV67M6zoAAAAAAEA6chTcW7Rokdd1AAAAAACAdOTo5nQAAAAAAODWyNER9xvWrl2rGTNmaPv27YqPj5evr6/q1q2rnj17qmnTpnlVIwAAAAAAd6wcB/eXX35ZH330kQzDkPTfjegMw9CWLVv01VdfadCgQZowYUKeFQoAAAAAwJ0oR6fKz5w5U5MmTVKVKlX0v//9TydOnND169d18uRJzZo1S1WrVtWkSZN4zjsAAAAAALmUo+A+ZcoUlSlTRjExMerWrZsCAgJksVjk7++vrl27asOGDQoODtann36a1/UCAAAAAHBHyVFw//PPP/XYY4/Jz88v3fl+fn567LHH9Oeff+aqOAAAAAAA7nT5dld5i8WSX6sGAAAAAOCOkaPgXqtWLc2fP1+XL19Od/6lS5c0f/581apVK1fFAQAAAABwp8tRcH/22Wf1zz//KCwsTPPnz9fZs2clSWfPntX333+ve++9V//884/69++fp8UCAAAAAHCnydHj4Pr06aNt27bpk08+UefOnSVJTk5OSk1NlSQZhqEXXnhBvXr1yrtKAQAAAAC4A+X4Oe4fffSRnnjiCc2YMUPbt29XfHy8fH19VbduXfXq1UvNmjXLyzoBAAAAALgj5Ti4S1KzZs0I6AAAAAAA5KN8u6s8AAAAAADIvRwF95kzZ6p+/fo6ceJEuvNPnDih+vXra9asWbkqDgAAAACAO12OgvuMGTPk5uamoKCgdOcHBQXJ09NTX331Va6KAwAAAADgTpej4L5nzx7VrVs30z516tTRnj17clQUAAAAAAD4T46Ce1xcnIoVK5ZpH19fX124cCFHRQEAAAAAgP/kKLgHBQVp+/btmfbZsWOH/P39c7J6SdLkyZMVEhIiDw8PNW7cWBs3bsywb8uWLWWxWOxe7dq1y/H2AQAAAAAwgxwF9/DwcC1evFhLly5Nd/6SJUu0aNEitWnTJkdFzZ07V5GRkRo1apS2bt2q0NBQtWnTRrGxsen2/+GHH3Ty5Enra/fu3XJ2dtYTTzyRo+0DAAAAAGAWOQruw4YNk7e3t9q2bau+fftq9uzZWr16tWbPnq0+ffqoXbt28vX11bBhw3JU1IQJExQREaE+ffqoZs2amjp1qry8vDRt2rR0+xcvXlwBAQHW19KlS+Xl5UVwBwAAAAAUei45WahChQpauHChunbtqhkzZmjmzJnWeYZhqEyZMpo3b54qVKiQ7XUnJSVpy5YtNqHfyclJ4eHhWr9+vUPr+Oqrr9S1a1d5e3tne/sAAAAAAJhJjoK7JDVt2lSHDh3Szz//rI0bNyouLk5FixZVo0aN9Mgjj8jNzS1H6z179qxSUlLsro/39/fXX3/9leXyGzdu1O7duzN9FF1iYqISExOt0/Hx8ZKk5ORkJScn56juW8nTs6ArMB9Pz/8+t2TeHHsmG9N8RPYYv1kw0RjmI7LH+M0C49fUGL9ZYPyaHmM4EyYavxnJTva0GIZh5GMt2XbixAkFBwdr3bp1CgsLs7YPGTJEq1atUkxMTKbLP/vss1q/fr127tyZYZ+oqCiNHj3arn3WrFny8vLKefEAAAAAADggISFB3bt3V1xcnHx9fTPtm+Mj7um58fi3rB4Vl5mSJUvK2dlZp0+ftmk/ffq0AgICMl32ypUrmjNnjsaMGZNpv2HDhikyMtI6HR8fr7Jly+qBBx7I8g0zAz+/gq7AfDw9kzVt2lK17ttXrlevFnQ55hIXV9AV2GD82mP8ZsFEY5jxa4/xmwXGr6kxfrPA+DU9xnAmTDR+M3LjzG9HOBzcz5w5o127dqlatWoKDg62mbdp0yY988wz1qPcNWrU0JQpU9SsWTOHC7nBzc1N9evX1/Lly9WxY0dJUmpqqpYvX66BAwdmuux3332nxMREPfnkk5n2c3d3l7u7u127q6urXF1ds13zrcZ3MmOuV6/yQystk41pPp6MMX4zYKIxzMeTMcZvBhi/hQLjNwOM30KDMZwOE43fjGQnezp8V/kpU6aodevWunjxok37qVOn1KZNG+3YsUNubm7y9vbWnj179NBDD+ngwYMOF3KzyMhIffHFF5o5c6b27t2r/v3768qVK+rTp48kqWfPnunesf6rr75Sx44dVaJEiRxtFwAAAAAAs3E4uK9evVrVqlVTrVq1bNonTZqkixcv6vHHH9eFCxcUFxeniRMnKiEhQRMnTsxRUV26dNG4ceM0cuRI1alTR9u3b9eiRYusN6w7duyYTp48abPMvn379Mcff+jpp5/O0TYBAAAAADAjh0+VP3DggMLDw+3af/31Vzk7O+uTTz6Rh4eHJOnFF1/U119/rRUrVuS4sIEDB2Z4anx0dLRdW7Vq1WSy++wBAAAAAJBrDh9xP3PmjAIDA23aLl26pL1796p+/foqXbq0zbx77rlHR48ezZsqAQAAAAC4Qzkc3CXZXd++detWGYahBg0a2PUtWrSoUlJSclUcAAAAAAB3OoeDe4UKFbRu3TqbtpUrV8piseiee+6x6x8bG2u9Jh0AAAAAAOSMw8H94Ycf1vbt2/XOO+8oPj5emzdv1tSpU+Xm5qa2bdva9Y+JiVHFihXztFgAAAAAAO40Dgf3IUOGKDg4WCNGjFCxYsXUuHFjxcbGatCgQSpevLhN33379mnXrl2677778rxgAAAAAADuJA7fVb548eJav369Ro4cqQ0bNqhEiRLq3Llzund+X7hwoUJDQ9W+ffs8LRYAAAAAgDuNw8FdkoKDg/XVV19l2S8yMlKRkZE5LgoAAAAAAPwnW3eVBwAAAAAAtxbBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMTyLbi/+uqrqlSpUn6tHgAAAACAO0K+BfezZ8/qyJEj+bV6AAAAAADuCJwqDwAAAACAibk42rFv377ZWvEff/yR7WIAAAAAAIAth4P7jBkzZLFYZBiGwyu3WCw5KgoAAAAAAPzH4eDu6+ur4OBgTZs2zaH+b775pn7//fccFwYAAAAAALIR3OvVq6dNmzapUaNGDh1JL1WqVK4KAwAAAAAA2bg5Xb169ZSQkKC//vorP+sBAAAAAAA3cfiIe4cOHXTu3DldvXrVof79+vVTy5Ytc1oXAAAAAABQNoJ7s2bN1KxZM4dX3KRJEzVp0iRHRQEAAAAAgP/wHHcAAAAAAEzM4eAeHx+vpKSk/KwFAAAAAACk4XBwL1asmN577z2btpiYGH300Ud5XhQAAAAAAPiPw8HdMAwZhmHTtmjRIr388st5XhQAAAAAAPgP17gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGX7HT+9ttvtWHDBuv0gQMHJElt27ZNt7/FYtHChQtzUR4AAAAAAHe2bAX3AwcOWMP6zRYtWpRuf4vFkrOqAAAAAACApGwE98OHD+dnHQAAAAAAIB0OB/fy5cvnZx0AAAAAACAd3JwOAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJmbK4D558mSFhITIw8NDjRs31saNGzPtf/HiRQ0YMECBgYFyd3dX1apV9dtvv92iagEAAAAAyD8uBV1AWnPnzlVkZKSmTp2qxo0ba+LEiWrTpo327dun0qVL2/VPSkpS69atVbp0aX3//fcKDg7W0aNHVbRo0VtfPAAAAAAAecx0wX3ChAmKiIhQnz59JElTp07VwoULNW3aNA0dOtSu/7Rp03T+/HmtW7dOrq6ukqSQkJBbWTIAAAAAAPnGVME9KSlJW7Zs0bBhw6xtTk5OCg8P1/r169Nd5pdfflFYWJgGDBign3/+WaVKlVL37t312muvydnZOd1lEhMTlZiYaJ2Oj4+XJCUnJys5OTkP9yh/eHoWdAXm4+n53+eWzJtjz2Rjmo/IHuM3CyYaw3xE9hi/WWD8mhrjNwuMX9NjDGfCROM3I9nJnhbDMIx8rCVbTpw4oeDgYK1bt05hYWHW9iFDhmjVqlWKiYmxW6Z69eo6cuSIevTooeeff14HDhzQ888/rxdffFGjRo1KdztRUVEaPXq0XfusWbPk5eWVdzsEAAAAAEA6EhIS1L17d8XFxcnX1zfTvqY64p4TqampKl26tD7//HM5Ozurfv36+vfff/XBBx9kGNyHDRumyMhI63R8fLzKli2rBx54IMs3zAz8/Aq6AvPx9EzWtGlL1bpvX7levVrQ5ZhLXFxBV2CD8WuP8ZsFE41hxq89xm8WGL+mxvjNAuPX9BjDmTDR+M3IjTO/HWGq4F6yZEk5Ozvr9OnTNu2nT59WQEBAussEBgbK1dXV5rT4GjVq6NSpU0pKSpKbm5vdMu7u7nJ3d7drd3V1tV4nb2Z8JzPmevUqP7TSMtmY5uPJGOM3AyYaw3w8GWP8ZoDxWygwfjPA+C00GMPpMNH4zUh2sqepHgfn5uam+vXra/ny5da21NRULV++3ObU+Zs1adJEBw4cUGpqqrXt77//VmBgYLqhHQAAAACAwsRUwV2SIiMj9cUXX2jmzJnau3ev+vfvrytXrljvMt+zZ0+bm9f1799f58+f16BBg/T3339r4cKFeueddzRgwICC2gUAAAAAAPKMqU6Vl6QuXbrozJkzGjlypE6dOqU6depo0aJF8vf3lyQdO3ZMTk7/9/eGsmXLavHixXr55ZdVu3ZtBQcHa9CgQXrttdcKahcAAAAAAMgzpgvukjRw4EANHDgw3XnR0dF2bWFhYdqwYUM+VwUAAAAAwK1nulPlAQAAAADA/yG4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYgR3AAAAAABMjOAOAAAAAICJEdwBAAAAADAxgjsAAAAAACZGcAcAAAAAwMQI7gAAAAAAmBjBHQAAAAAAEyO4AwAAAABgYqYN7pMnT1ZISIg8PDzUuHFjbdy4McO+M2bMkMVisXl5eHjcwmoBAAAAAMgfpgzuc+fOVWRkpEaNGqWtW7cqNDRUbdq0UWxsbIbL+Pr66uTJk9bX0aNHb2HFAAAAAADkD1MG9wkTJigiIkJ9+vRRzZo1NXXqVHl5eWnatGkZLmOxWBQQEGB9+fv738KKAQAAAADIHy4FXUBaSUlJ2rJli4YNG2Ztc3JyUnh4uNavX5/hcpcvX1b58uWVmpqqevXq6Z133lGtWrXS7ZuYmKjExETrdHx8vCQpOTlZycnJebQn+cfTs6ArMB9Pz/8+t2TeHHsmG9N8RPYYv1kw0RjmI7LH+M0C49fUGL9ZYPyaHmM4EyYavxnJTva0GIZh5GMt2XbixAkFBwdr3bp1CgsLs7YPGTJEq1atUkxMjN0y69ev1/79+1W7dm3FxcVp3LhxWr16tf7880+VKVPGrn9UVJRGjx5t1z5r1ix5eXnl7Q4BAAAAAJBGQkKCunfvrri4OPn6+mba13RH3HMiLCzMJuTfe++9qlGjhj777DO9+eabdv2HDRumyMhI63R8fLzKli2rBx54IMs3zAz8/Aq6AvPx9EzWtGlL1bpvX7levVrQ5ZhLXFxBV2CD8WuP8ZsFE41hxq89xm8WGL+mxvjNAuPX9BjDmTDR+M3IjTO/HWG64F6yZEk5Ozvr9OnTNu2nT59WQECAQ+twdXVV3bp1deDAgXTnu7u7y93dPd3lXF1ds1/0LcZ3MmOuV6/yQystk41pPp6MMX4zYKIxzMeTMcZvBhi/hQLjNwOM30KDMZwOE43fjGQne5ru5nRubm6qX7++li9fbm1LTU3V8uXLbY6qZyYlJUW7du1SYGBgfpUJAAAAAMAtYboj7pIUGRmpXr16qUGDBmrUqJEmTpyoK1euqE+fPpKknj17Kjg4WGPHjpUkjRkzRvfcc48qV66sixcv6oMPPtDRo0fVr1+/gtwNAAAAAAByzZTBvUuXLjpz5oxGjhypU6dOqU6dOlq0aJH1EW/Hjh2Tk9P/nSxw4cIFRURE6NSpUypWrJjq16+vdevWqWbNmgW1CwAAAAAA5AlTBndJGjhwoAYOHJjuvOjoaJvpDz/8UB9++OEtqAoAAAAAgFvLdNe4AwAAAACA/0NwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxEwb3CdPnqyQkBB5eHiocePG2rhxo0PLzZkzRxaLRR07dszfAgEAAAAAuAVMGdznzp2ryMhIjRo1Slu3blVoaKjatGmj2NjYTJc7cuSIBg8erGbNmt2iSgEAAAAAyF+mDO4TJkxQRESE+vTpo5o1a2rq1Kny8vLStGnTMlwmJSVFPXr00OjRo1WxYsVbWC0AAAAAAPnHpaALSCspKUlbtmzRsGHDrG1OTk4KDw/X+vXrM1xuzJgxKl26tJ5++mmtWbMm020kJiYqMTHROh0XFydJOn/+vJKTk3O5B/nPw6OgKzAfD49kJSQk6JyHh1wNo6DLMZdz5wq6AhuMX3uM3yyYaAwzfu0xfrPA+DU1xm8WGL+mxxjOhInGb0YuXbokSTIc+OxMF9zPnj2rlJQU+fv727T7+/vrr7/+SneZP/74Q1999ZW2b9/u0DbGjh2r0aNH27VXqFAh2/XCHK5dk7p3L+gqTKpkyYKuAFlg/GaBMWxqjN8sMH5NjfGbBcav6TGGM1GIxu+lS5fk5+eXaR/TBffsunTpkp566il98cUXKunghzNs2DBFRkZap1NTU3X+/HmVKFFCFoslv0pFPoqPj1fZsmV1/Phx+fr6FnQ5QLYwflGYMX5RmDF+Udgxhgs3wzB06dIlBQUFZdnXdMG9ZMmScnZ21unTp23aT58+rYCAALv+Bw8e1JEjR9S+fXtrW2pqqiTJxcVF+/btU6VKlWyWcXd3l7u7u01b0aJF82gPUJB8fX35oYVCi/GLwozxi8KM8YvCjjFceGV1pP0G092czs3NTfXr19fy5cutbampqVq+fLnCwsLs+levXl27du3S9u3bra9HHnlErVq10vbt21W2bNlbWT4AAAAAAHnKdEfcJSkyMlK9evVSgwYN1KhRI02cOFFXrlxRnz59JEk9e/ZUcHCwxo4dKw8PD9111102y984ep62HQAAAACAwsaUwb1Lly46c+aMRo4cqVOnTqlOnTpatGiR9YZ1x44dk5OT6U4WQAFyd3fXqFGj7C6BAAoDxi8KM8YvCjPGLwo7xvCdw2I4cu95AAAAAABQIDhsDQAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAH/r+QkBBNnDgx0z4Wi0U//fTTLakHt5/o6GhZLBZdvHgx37aR1Rg9cuSILBaLtm/fnm814PYTFRWlOnXq5Nv6HfluzJgxw/rUGCAj+T1W0+PI2CyIulD4MH6RGYI78t3q1avVvn17BQUF5Sj4tmzZUi+99JJD/SwWi93r+vXrOSscd6yxY8eqYcOG8vHxUenSpdWxY0ft27fvlmw7JCTEbgyXKVPmlmwbt5cpU6aodu3a8vX1la+vr8LCwvT777/ny7Zu/EEo7evJJ5/Ml+3hzvLuu+/KYrHY/S6QV39Mv/GHo7Sv4cOH53rduDNFRUXZjafq1avb9GH8IrtM+Tg43F6uXLmi0NBQ9e3bV48++mi+bisiIkJjxoyxaXNxYZgje1atWqUBAwaoYcOGun79ul5//XU98MAD2rNnj7y9vfN9+2PGjFFERIR12tnZOd+3idtPmTJl9O6776pKlSoyDEMzZ85Uhw4dtG3bNtWqVSvdZZKTk+Xq6prjbS5btsxm3Z6enjleFyBJmzZt0meffabatWvn+7b27dsnX19f63SRIkXyfZu4fdWqVUvLli2zTuf376OM39sfR9yR7x566CG99dZb6tSpU4Z9Pv30U1WpUkUeHh7y9/fX448/Lknq3bu3Vq1apUmTJln/gnjkyJEM1+Pl5aWAgACb1w3z589XrVq15O7urpCQEI0fPz7Tuvfv36/mzZvLw8NDNWvW1NKlS7O34yi0Fi1apN69e6tWrVoKDQ3VjBkzdOzYMW3ZssXax2Kx6Msvv1SnTp3k5eWlKlWq6JdffrFZz2+//aaqVavK09NTrVq1ynTs3szHx8dmDJcqVco6b8qUKapUqZLc3NxUrVo1ffPNN5mua+PGjapbt648PDzUoEEDbdu2zfE3AoVa+/bt1bZtW1WpUkVVq1bV22+/rSJFimjDhg3WPhaLRVOmTNEjjzwib29vvf3225L+O8Lp7+8vHx8fPf3007p27ZpD2yxRooTN2PXz85MkJSYm6sUXX1Tp0qXl4eGhpk2batOmTZmua8aMGSpXrpy8vLzUqVMnnTt3LofvBAqry5cvq0ePHvriiy9UrFgxm3khISGSpE6dOslisVinb/jmm28UEhIiPz8/de3aVZcuXcpye6VLl7YZvzeCz4ULF9SzZ08VK1ZMXl5eeuihh7R///5M15XT7xBuHy4uLjbjqWTJktZ5jF/kBMEdBW7z5s168cUXNWbMGO3bt0+LFi1S8+bNJUmTJk1SWFiYIiIidPLkSZ08eVJly5bN9ja2bNmizp07q2vXrtq1a5eioqI0YsQIzZgxI93+qampevTRR+Xm5qaYmBhNnTpVr732Wm52E4VYXFycJKl48eI27aNHj1bnzp21c+dOtW3bVj169ND58+clScePH9ejjz6q9u3ba/v27erXr5+GDh2aqzp+/PFHDRo0SK+88op2796tZ599Vn369NHKlSvT7X/58mU9/PDDqlmzprZs2aKoqCgNHjw4VzWgcEpJSdGcOXN05coVhYWF2cyLiopSp06dtGvXLvXt21fz5s1TVFSU3nnnHW3evFmBgYH69NNPc7X9IUOGaP78+Zo5c6a2bt2qypUrq02bNtbvS1oxMTF6+umnNXDgQG3fvl2tWrXSW2+9lasaUPgMGDBA7dq1U3h4uN28G3/4mT59uk6ePGnzh6CDBw/qp59+0oIFC7RgwQKtWrVK7777bo7r6N27tzZv3qxffvlF69evl2EYatu2rZKTk9Ptnx/fIRQ++/fvV1BQkCpWrKgePXro2LFj1nmMX+SIAdxCkowff/zRpm3+/PmGr6+vER8fn+4yLVq0MAYNGpTlulu0aGG4uroa3t7e1ldkZKRhGIbRvXt3o3Xr1jb9X331VaNmzZrW6fLlyxsffvihYRiGsXjxYsPFxcX4999/rfN///33dOvH7S0lJcVo166d0aRJE5t2Scbw4cOt05cvXzYkGb///rthGIYxbNgwm/FlGIbx2muvGZKMCxcuZLi98uXLG25ubjbjeNKkSYZhGMa9995rRERE2PR/4oknjLZt29rUdWOMfvbZZ0aJEiWMq1evWudPmTLFkGRs27bN4fcAhdfOnTsNb29vw9nZ2fDz8zMWLlxoM1+S8dJLL9m0hYWFGc8//7xNW+PGjY3Q0NAMt3P48GFDkuHp6Wkzdrdu3WpcvnzZcHV1Nf73v/9Z+yclJRlBQUHG+++/bxiGYaxcudLmu9GtWzebcW0YhtGlSxfDz88vm+8ACqvZs2cbd911l/XnV3q/C6T3f/KoUaMMLy8vm98pXn31VaNx48YZbuvG+Lt57Hp7extnz541/v77b0OSsXbtWmv/s2fPGp6ensa8efMMwzCM6dOn24zNnHyHcHv57bffjHnz5hk7duwwFi1aZISFhRnlypWzGZeMX2QXF/+iwLVu3Vrly5dXxYoV9eCDD+rBBx+0nn6cXT169NAbb7xhnb5xl8y9e/eqQ4cONn2bNGmiiRMnKiUlxe4a4r1796ps2bIKCgqytqU9SoU7w4ABA7R792798ccfdvNuvubS29tbvr6+io2NlfTfGGrcuLFNf0fH0KuvvqrevXtbp2+cXrd3714988wzNn2bNGmiSZMmpbuevXv3qnbt2vLw8Mh2Dbg9VKtWTdu3b1dcXJy+//579erVS6tWrVLNmjWtfRo0aGCzzN69e/Xcc8/ZtIWFhWV4ZsfN5s6dqxo1aliny5Ytq3379ik5OVlNmjSxtru6uqpRo0bau3dvuuvZu3ev3eVVYWFhWrRoUZY1oPA7fvy4Bg0apKVLl9r8/HJUSEiIfHx8rNOBgYHWn82ZWbNmjc1yxYoV09q1a+Xi4mLz87xEiRKqVq1apuM3p98h3B4eeugh679r166txo0bq3z58po3b56efvrpTJdl/CIjBHcUOB8fH23dulXR0dFasmSJRo4cqaioKG3atCnbj/7x8/NT5cqV86dQ3HEGDhyoBQsWaPXq1ene2T3tTbwsFotSU1Nzvd2SJUsyjpEn3NzcrGOpfv362rRpkyZNmqTPPvvM2icvb7hYtmxZxi5ybcuWLYqNjVW9evWsbSkpKVq9erU++eQTJSYmZnrTzpz+bK5QoQKPHES+KFq0qKpWraoDBw5k2Zfxi4xwjTtMwcXFReHh4Xr//fe1c+dOHTlyRCtWrJD03y+eKSkpuVp/jRo1tHbtWpu2tWvXqmrVqun+51+jRg0dP35cJ0+etLbdfEMn3N4Mw9DAgQP1448/asWKFapQoUK211GjRg1t3LjRpi23YyijcXzz0dO0/Xfu3GlzUxnG8Z0tNTVViYmJmfapUaOGYmJibNpyM25u3Ezx5rGbnJysTZs2ZTp287IGFC7333+/du3ape3bt1tfDRo0UI8ePbR9+3br/9uurq65/v0gKzVq1ND169dtxuO5c+e0b98+xi8cdvnyZR08eFCBgYHWNsYvsosj7sh3ly9ftvkL4+HDh7V9+3YVL15c5cqV04IFC3To0CE1b95cxYoV02+//abU1FRVq1ZN0n+nDMXExOjIkSMqUqSIihcvLien7P3N6ZVXXlHDhg315ptvqkuXLlq/fr0++eSTDG+2ER4erqpVq6pXr1764IMPFB8fb3MKPm5vAwYM0KxZs/Tzzz/Lx8dHp06dkvTfGR2OPt7queee0/jx4/Xqq6+qX79+2rJlS4Y3Q3TUq6++qs6dO6tu3boKDw/Xr7/+qh9++MHmcTM36969u9544w1FRERo2LBhOnLkiMaNG5erGlB4DBs2TA899JDKlSunS5cuadasWYqOjtbixYszXW7QoEHq3bu3GjRooCZNmuh///uf/vzzT1WsWDFHdXh7e6t///569dVXrT/333//fSUkJGR4yuiLL76oJk2aaNy4cerQoYMWL17MafJ3EB8fH9111102bd7e3ipRooRNe0hIiJYvX64mTZrI3d3d7s7zeaFKlSrq0KGDIiIi9Nlnn8nHx0dDhw5VcHCw3SV4N+T1dwiFz+DBg9W+fXuVL19eJ06c0KhRo+Ts7Kxu3bpZ+zB+kV0ccUe+27x5s+rWrau6detKkiIjI1W3bl2NHDlS0n+nD/3www+67777VKNGDU2dOlWzZ8+2Pgt48ODBcnZ2Vs2aNVWqVCmbu3I6ql69epo3b57mzJmju+66SyNHjtSYMWNsriO+mZOTk3788UddvXpVjRo1Ur9+/ayPScLtb8qUKYqLi1PLli0VGBhofc2dO9fhdZQrV07z58/XTz/9pNDQUE2dOlXvvPNOrurq2LGjJk2apHHjxqlWrVr67LPPNH36dLVs2TLd/kWKFNGvv/6qXbt2qW7dunrjjTf03nvv5aoGFB6xsbHq2bOnqlWrpvvvv1+bNm3S4sWL1bp160yX69Kli0aMGKEhQ4aofv36Onr0qPr375+rWt5991099thjeuqpp1SvXj0dOHBAixcvzvAX1XvuuUdffPGFJk2apNDQUC1ZskTDhw/PVQ24/YwfP15Lly5V2bJlrb9j5Ifp06erfv36evjhhxUWFibDMPTbb7/ZndJ8Q358h1C4/PPPP+rWrZuqVaumzp07q0SJEtqwYYPN410Zv8gui2EYRkEXAQAAAAAA0scRdwAAAAAATIzgDgAAAACAiRHcAQAAAAAwMYI7AAAAAAAmRnAHAAAAAMDECO4AAAAAAJgYwR0AAAAAABMjuAMAcBsKCQlRSEhIQZcBAADyAMEdAIAsHDlyRBaLxebl6uqq4OBgde7cWZs3by7oEgutGTNm2L23N7/q1KlT0CVmy42x0rt374IuBQBwG3Ep6AIAACgsKlWqpCeffFKSdOXKFW3ZskXfffedfvrpJy1btkzNmzcv4AoLr/vvv19Nmza1aw8ICCiAagAAMBeCOwAADqpcubKioqJs2t59910NGzZMI0aM0KpVqwqmsNtAeHi4hg4dWtBlAABgSpwqDwBALjz99NOSpC1bttjNmzZtmjp06KCQkBB5eHioePHiatOmjVauXGnXNzo6WhaLRVFRUdq8ebNat24tHx8f+fn5qVOnTjpy5Ei62//555/VsGFDeXp6yt/fXxEREbpw4UKG9Z49e1YvvfSSKlSoIHd3d5UuXVqdO3fW7t277fr27t1bFotFhw4d0rhx41S1alV5enqqZs2amjNnjiQpKSlJb7zxhnUfa9eurd9//92Rty5H1q5dq3bt2ql48eLy8PBQ9erVNWrUKCUkJNj1tVgsatmypf7991/17NlTAQEBcnJyUnR0tLXP6tWr1b59e5UsWVLu7u6qUqWKhg8fnu765s+frxYtWqh06dLy8PBQUFCQwsPDNX/+fEn/nfZfoUIFSdLMmTNtTvm/eZsAAGQXR9wBAMgDLi72/6UOGDBAoaGhCg8PV6lSpfTvv//qp59+Unh4uH744Qd16NDBbplNmzbp/fffV6tWrfTss89q27Zt+umnn7Rr1y7t3r1bHh4e1r5ff/21evXqJV9fXz311FMqWrSoFixYoPDwcCUlJcnNzc1m3WfOnFFYWJgOHjyoli1bqmvXrjp8+LC+//57LVy4UIsXL073dPXIyEjFxMSoffv2cnZ21pw5c9S9e3cVK1ZMH3/8sfbs2aN27drp2rVrmjVrljp06KC9e/eqUqVKefDO/p/vvvtO3bp1k7u7u7p06aLSpUtryZIlGjNmjBYvXqzo6Gib90eSzp07p7CwMBUvXlxdu3bVtWvX5OvrK0maMmWKBgwYoKJFi6p9+/YqXbq0Nm/erLffflsrV67UypUrre/hlClT9PzzzyswMFCdOnVSiRIldOrUKW3cuFE//vijHnvsMdWpU0eDBg3SpEmTFBoaqo4dO1rr4EaBAIBcMQAAQKYOHz5sSDLatGljN++dd94xJBnt2rWzm3fo0CG7thMnThhBQUFGlSpVbNpXrlxpSDIkGXPmzLGZ99RTTxmSjNmzZ1vb4uLiDF9fX8Pb29vYt2+ftT0pKclo3ry5IckoX768zXr69OljSDKGDRtm075w4UJDklG5cmUjJSXF2t6rVy9DklG1alUjNjbW2h4TE2NIMooWLWo0bdrUuHz5snXe3LlzDUnGCy+8YLfv6Zk+fbohybj//vuNUaNG2b1Onjxp3V8/Pz/D3d3d2LFjh3X5lJQUo0uXLoYkY8yYMTbrvvF+9unTx7h+/brNvD///NNwcXExQkNDjbNnz9rMGzt2rCHJGDdunLWtXr16hpubm3H69Gm7fbh5+RtjpVevXg7tPwAAjiC4AwCQhRthrFKlStZAOXjwYKNVq1aGJMPf39/Ys2ePw+t74YUXDEnGkSNHrG03gnvz5s3t+t+YFxkZaW2bOXNmhgF5zZo1dsE9MTHR8PDwMEqUKGFcuXLFbpnWrVsbkozVq1db224E95kzZ9r1r1ixoiHJWLVqlU379evXDVdX13T3Iz03gntGr23bthmGYRhff/21Icno37+/3TqOHj1quLi4GBUrVrRpl2S4ubkZZ86csVvmxRdftNvfG1JSUoxSpUoZ9evXt7bVq1fP8Pb2Ns6fP5/p/hDcAQD5gVPlAQBw0MGDBzV69GibtoCAAK1Zs0aVK1e263/o0CGNHTtWK1as0L///qvExESb+SdOnFD58uVt2urXr2+3njJlykiSLl68aG3bsWOHJKlZs2Z2/cPCwuxO3f/rr7907do1tWrVSl5eXnbLtGrVSkuXLtX27dvt1pneI9kCAwN16NAhu3nOzs4qXbq0Tpw4YbdMZsaOHZvpzem2bdsmSWrZsqXdvHLlyqlixYr6+++/denSJfn4+FjnVahQQSVLlrRbZsOGDZKkxYsXa/ny5XbzXV1d9ddff1mnu3btqiFDhuiuu+5S9+7d1apVKzVt2tR62j0AAPmJ4A4AgIPatGmjRYsWSfrvevGZM2fqtdde0yOPPKKNGzeqSJEi1r4HDhxQo0aNFB8fr1atWql9+/by9fW13hxt1apVdkFeUrpB8EYIT0lJsbbFxcVJkkqXLm3X39nZWSVKlLBpi4+PlyT5+/unu2+BgYE2/RytKaN5ycnJ6W4npxyp/++//1Z8fLxNcM+o//nz5yVJb7/9tkPbHzx4sEqUKKEpU6Zo/PjxGjdunFxcXNSuXTt9+OGH1pvSAQCQHwjuAADkQKlSpTR48GDFxcXprbfe0vDhwzVx4kTr/A8//FAXLlzQN998Y332+w3PPfdcrh8d5+fnJ0mKjY21m5eSkqJz584pODjY2nYjYJ8+fTrd9Z06dcqmn9nktH6LxZLp+tIG/YxYLBb17dtXffv21blz57RmzRrNnj1b8+bN0/79+7Vz5045Ozs7vD8AAGQHj4MDACAXXn/9dQUFBenTTz+1eWTbwYMHJcnuzvGGYWjt2rW53m5oaKgkac2aNXbz1q9fr+vXr9u0Va9eXR4eHtq0aVO6jzq78biy9E6LN4O6detKUrqPVTt+/LgOHjyoihUrOhTCJalx48aS/u+U+ewoUaKEOnbsqLlz5+q+++7Tnj17dODAAUmyhvebz44AACC3CO4AAOSCp6enXnvtNSUnJ+vNN9+0tt+4dv2PP/6w6f/uu++m+8z07OrQoYN8fX01bdo0/f3339b25ORkDR8+3K6/m5ubunXrprNnz2rs2LE28xYtWqTFixercuXKatKkSa5ryw8dOnSQn5+fpk+frj///NPabhiGXnvtNV2/fl29e/d2eH3PP/+8XFxc9MILL+jYsWN28y9evGi9rl767w8GhmHY9ElOTraecn/jMXTFihWTxWLR8ePHs7N7AABkilPlAQDIpWeeeUbvvfeevv76a73++uuqVKmSnnvuOU2fPl2PPfaYOnfurBIlSmjDhg3aunWr2rVrp4ULF+Zqm35+fvroo4/Uu3dvNWzYUF27dpWfn58WLFggT09P6zXrN3vvvfe0atUqvfXWW1q3bp0aN26sI0eO6LvvvpOXl5emT58uJydz/k3f19dXX3zxhbp166bGjRurS5cuKlWqlJYtW6YtW7aoUaNGevXVVx1e31133aVPP/1U/fv3V7Vq1dS2bVtVqlRJly5d0qFDh7Rq1Sr17t1bU6dOlSR17NhRvr6+uueee1S+fHklJydr6dKl2rNnjx5//HHrH2qKFCmihg0bavXq1XrqqadUpUoVOTk56amnnrK7ESEAAI4y5//OAAAUIh4eHho2bJiuX79uvet83bp1tWTJEtWrV08//PCDpk2bpqJFi2rt2rVq0KBBnmy3V69e+vHHH1WlShXNnDlTM2fOVJMmTbRs2TK5ubnZ9S9VqpRiYmL04osv6uDBgxo3bpyWLl2qjh07KiYmRk2bNs2TuvLLE088oZUrV6p58+b64Ycf9OGHH+rSpUsaMWKEVqxYYT3q7aiIiAitX79eHTt21IYNGzRx4kR9//33Onv2rF5++WW99NJL1r5jx45V3bp1tXHjRn3yySf69ttvVaRIEU2ZMkWzZs2yWe8333yjhx56SAsWLFBUVJRGjBihw4cP58VbAAC4Q1mMtOd9AQAAAAAA0+CIOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBiBHcAAAAAAEyM4A4AAAAAgIkR3AEAAAAAMDGCOwAAAAAAJkZwBwAAAADAxAjuAAAAAACYGMEdAAAAAAATI7gDAAAAAGBi/w9ky8eWLZqQJwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9473684210526315, 'precision_score': 0.9487922705314009, 'recall_score': 0.94252143537631, 'f1_score': 0.9453324808184143}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.97      0.96        67\n",
            "           1       0.96      0.91      0.93        47\n",
            "\n",
            "    accuracy                           0.95       114\n",
            "   macro avg       0.95      0.94      0.95       114\n",
            "weighted avg       0.95      0.95      0.95       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualise The Final Results**"
      ],
      "metadata": {
        "id": "fmoEHICeQU9o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# *Bar Plot for a multi - model analysis*"
      ],
      "metadata": {
        "id": "7IKPLBf-mqBf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create empty list for accuracy, precision, recall and f1-score of each ml algorithm\n",
        "\n",
        "accuracy_score_tf = [lr_test_result['accuracy_score'], knn_test_result['accuracy_score'],\n",
        "                     dt_test_result['accuracy_score'], rf_test_result['accuracy_score'],\n",
        "                     svm_test_result['accuracy_score']]\n",
        "\n",
        "precision_score_tf = [lr_test_result['precision_score'], knn_test_result['precision_score'],\n",
        "                     dt_test_result['precision_score'], rf_test_result['precision_score'],\n",
        "                     svm_test_result['precision_score']]\n",
        "\n",
        "recall_score_tf = [lr_test_result['recall_score'], knn_test_result['recall_score'],\n",
        "                   dt_test_result['recall_score'], rf_test_result['recall_score'],\n",
        "                   svm_test_result['recall_score']]\n",
        "\n",
        "f1_score_tf = [lr_test_result['f1_score'], knn_test_result['f1_score'],\n",
        "               dt_test_result['f1_score'], rf_test_result['f1_score'],\n",
        "               svm_test_result['f1_score']]\n",
        "\n",
        "models = [\"Logistic Regression\", \"KNN\", \"Decision Tree\", \"Random Forest\", \"SVM\"]\n",
        "\n",
        "# 5 is the number of models used\n",
        "X = np.arange(5)\n",
        "fig = plt.figure(figsize=(12,6))\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.bar(X + 0.00, accuracy_score_tf, color = 'g', width = 0.25)\n",
        "ax.bar(X + 0.20, precision_score_tf, color = 'pink', width = 0.25)\n",
        "ax.bar(X + 0.40, recall_score_tf, color = 'purple', width = 0.25)\n",
        "ax.bar(X + 0.60, f1_score_tf, color = 'b', width = 0.25)\n",
        "plt.xticks(X+0.30, models)\n",
        "plt.ylim(0.8, 1)\n",
        "plt.xlabel(\"ML Algorithms\")\n",
        "plt.ylabel(\"Scores\")\n",
        "plt.legend([\"Accuracy\", \"Precision\", \"Recall\", \"F1\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "tpxoFnbWQZ8I",
        "outputId": "18349162-a359-4302-e331-309aa4d22dcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7efeac4ae680>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Logistic Regression Accuracy:\", lr_test_result['accuracy_score'] * 100,\"%\" )\n",
        "print(\"Decision Tree Accuracy:\", dt_test_result['accuracy_score'] * 100,\"%\" )\n",
        "print(\"Random Forest Accuracy:\", rf_test_result['accuracy_score'] * 100,\"%\" )\n",
        "print(\"KNN Accuracy:\", knn_test_result['accuracy_score'] * 100,\"%\" )\n",
        "print(\"SVM Accuracy:\", svm_test_result['accuracy_score'] * 100,\"%\" )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06c8a405-5ac3-4c6e-c894-f6afd190142f",
        "id": "UKmG4gFkS75y"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Accuracy: 96.49122807017544 %\n",
            "Decision Tree Accuracy: 93.85964912280701 %\n",
            "Random Forest Accuracy: 94.73684210526315 %\n",
            "KNN Accuracy: 95.6140350877193 %\n",
            "SVM Accuracy: 98.24561403508771 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#*Final Multi - Model Accuracy Results after Data Validation*"
      ],
      "metadata": {
        "id": "X3Rfku7pm-ze"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Model              | Accuracy (Before Validation)| Accuracy (After Validation)\n",
        "|-----------         | : --------------------------|:------------------------\n",
        "|Logistic Regression |        96.49%               |   96.49%\n",
        "|Decision Tree       |        93.85%               |   93.85%\n",
        "|Random Forest       |        97.36%               |   94.73%\n",
        "|KNN                 |        96.49%               |   95.61%  \n",
        "|SVM                 |        98.24%               |   98.24%"
      ],
      "metadata": {
        "id": "4BT8n12NUR3Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Build a Final Trained Model**\n",
        "\n",
        "##Take the cell nucleus features as input from user and predict if the breast cancer tumor is Malignant or Benign."
      ],
      "metadata": {
        "id": "T1mNPAcFnPRd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Function to get user input\n",
        "def get_user_input():\n",
        "    input_data = []\n",
        "    print(\"Enter the numerical values for the breast cancer features:\")\n",
        "    for i in range(29):\n",
        "        value = float(input(f\"Enter value {i+1}: \"))\n",
        "        input_data.append(value)\n",
        "    return input_data\n",
        "\n",
        "# Load your trained model\n",
        "# model = load_model(\"your_model_path\")\n",
        "\n",
        "# Get user input\n",
        "user_input = get_user_input()\n",
        "\n",
        "# Convert user input to a NumPy array\n",
        "input_data_as_numpy_array = np.asarray(user_input)\n",
        "\n",
        "# Reshape the numpy array as we are predicting for one datapoint\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)\n",
        "\n",
        "# Predict using the model\n",
        "prediction = svm_model.predict(input_data_reshaped)\n",
        "\n",
        "# Placeholder prediction (remove/comment the above line once the model is loaded)\n",
        "prediction = np.array([np.random.randint(0, 2)])\n",
        "\n",
        "# Print the prediction\n",
        "if prediction[0] == 0:\n",
        "    print('The breast cancer is malignant.')\n",
        "else:\n",
        "    print('The breast cancer is benign.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkd3UGuqZUeJ",
        "outputId": "5092474e-420d-43be-d2f3-cc78ed2e2eff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the numerical values for the breast cancer features:\n",
            "Enter value 1: 13.54\n",
            "Enter value 2: 14.36\n",
            "Enter value 3: 87.46\n",
            "Enter value 4: 566.3\n",
            "Enter value 5: 0.09779\n",
            "Enter value 6: 0.08129\n",
            "Enter value 7: 0.06664\n",
            "Enter value 8: 0.04781\n",
            "Enter value 9: 0.1885\n",
            "Enter value 10: 0.05766\n",
            "Enter value 11: 0.2699\n",
            "Enter value 12: 0.7886\n",
            "Enter value 13: 2.058\n",
            "Enter value 14: 23.56\n",
            "Enter value 15: 0.008462\n",
            "Enter value 16: 0.0146\n",
            "Enter value 17: 0.02387\n",
            "Enter value 18: 0.01315\n",
            "Enter value 19: 0.0198\n",
            "Enter value 20: 0.0023\n",
            "Enter value 21: 15.11\n",
            "Enter value 22: 19.26\n",
            "Enter value 23: 99.7\n",
            "Enter value 24: 711.2\n",
            "Enter value 25: 0.144\n",
            "Enter value 26: 0.1773\n",
            "Enter value 27: 0.239\n",
            "Enter value 28: 0.1288\n",
            "Enter value 29: 0.2977\n",
            "The breast cancer is benign.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "\n",
        "#  Save the Trained model to a file\n",
        "\n",
        "joblib.dump(svm_model, 'svm_model.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E14ClNUGjacA",
        "outputId": "939abd04-407e-4457-a8e0-48856301f24b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['svm_model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    }
  ]
}