Dealing_with_imbalanced_data.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from imblearn.under_sampling import RandomUnderSampler, TomekLinks\n",
        "from imblearn.over_sampling import RandomOverSampler, SMOTE, ADASYN\n",
        "from imblearn.combine import SMOTETomek\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data= load_breast_cancer()\n",
        "df= pd.DataFrame(data.data, columns= data.feature_names)\n",
        "df['target']= data.target"
      ],
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        "id": "GRahpzbEDMlu"
      },
      "execution_count": 7,
      "outputs": []
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        "df.head()"
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              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
              "3                 0.09744  ...          26.50            98.87       567.7   \n",
              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
              "\n",
              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\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",
              "   worst symmetry  worst fractal dimension  target  \n",
              "0          0.4601                  0.11890       0  \n",
              "1          0.2750                  0.08902       0  \n",
              "2          0.3613                  0.08758       0  \n",
              "3          0.6638                  0.17300       0  \n",
              "4          0.2364                  0.07678       0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>target</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\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>0.2419</td>\n",
              "      <td>0.07871</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>0.1812</td>\n",
              "      <td>0.05667</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",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\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>0.2069</td>\n",
              "      <td>0.05999</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</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",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\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>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
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        "df.tail()"
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          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
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              "\n",
              "     worst smoothness  worst compactness  worst concavity  \\\n",
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              "\n",
              "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
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    {
      "cell_type": "code",
      "source": [
        "df['target']= df['target'].map({0:'M', 1:'B'})"
      ],
      "metadata": {
        "id": "szwSq4sNEMlE"
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      "execution_count": 10,
      "outputs": []
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          "height": 290
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        "id": "Rxj2bbKLE8FO",
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      "execution_count": 11,
      "outputs": [
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          "data": {
            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                 0.07871  ...          17.33           184.60      2019.0   \n",
              "1                 0.05667  ...          23.41           158.80      1956.0   \n",
              "2                 0.05999  ...          25.53           152.50      1709.0   \n",
              "3                 0.09744  ...          26.50            98.87       567.7   \n",
              "4                 0.05883  ...          16.67           152.20      1575.0   \n",
              "\n",
              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\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",
              "   worst symmetry  worst fractal dimension  target  \n",
              "0          0.4601                  0.11890       M  \n",
              "1          0.2750                  0.08902       M  \n",
              "2          0.3613                  0.08758       M  \n",
              "3          0.6638                  0.17300       M  \n",
              "4          0.2364                  0.07678       M  \n",
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              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>0.2419</td>\n",
              "      <td>0.07871</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>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>0.1812</td>\n",
              "      <td>0.05667</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",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>0.2069</td>\n",
              "      <td>0.05999</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>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</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",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
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          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "w6B1oYQ7E9ua",
        "outputId": "8fc22a04-e3ee-41f2-eaab-8e731e36d4e3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "564                 0.05623  ...          26.40           166.10      2027.0   \n",
              "565                 0.05533  ...          38.25           155.00      1731.0   \n",
              "566                 0.05648  ...          34.12           126.70      1124.0   \n",
              "567                 0.07016  ...          39.42           184.60      1821.0   \n",
              "568                 0.05884  ...          30.37            59.16       268.6   \n",
              "\n",
              "     worst smoothness  worst compactness  worst concavity  \\\n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
              "564                0.2216          0.2060                  0.07115       M  \n",
              "565                0.1628          0.2572                  0.06637       M  \n",
              "566                0.1418          0.2218                  0.07820       M  \n",
              "567                0.2650          0.4087                  0.12400       M  \n",
              "568                0.0000          0.2871                  0.07039       B  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>target</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>M</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b101593-b6da-400a-9a09-bb411684d367')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    .colab-df-convert {\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "\n",
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              "        document.querySelector('#df-2b101593-b6da-400a-9a09-bb411684d367 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-2b101593-b6da-400a-9a09-bb411684d367');\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",
              "\n",
              "\n",
              "<div id=\"df-319f0888-2d5b-4a5b-8d67-fbe09ffeca50\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-319f0888-2d5b-4a5b-8d67-fbe09ffeca50')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-319f0888-2d5b-4a5b-8d67-fbe09ffeca50 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "AMO7Jpf_E_8m",
        "outputId": "f235e2b9-4e62-41b3-e5ea-b730123f86dc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "B    357\n",
              "M    212\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>212</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes= plt.subplots(nrows=1, ncols=2, figsize=(10,4))\n",
        "\n",
        "pie_colors= ['skyblue','lightcoral']\n",
        "axes[0].pie(df['target'].value_counts(),labels= df['target'].value_counts().index, autopct='%1.1f%%', startangle=90, colors= pie_colors)\n",
        "axes[0].set_title('Class distribution before resampling (Pie Chart)')\n",
        "\n",
        "countplot_colors= sns.color_palette(pie_colors)\n",
        "sns.countplot(x='target', data=df, palette=countplot_colors, ax= axes[1])\n",
        "axes[1].set_title('Class distribution before resampling (Count Plot)')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "iixBeGn7FOot",
        "outputId": "993f3b4b-4efb-40cd-bf67-575aea552009"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-a6dbdb95bbb7>:8: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x='target', data=df, palette=countplot_colors, ax= axes[1])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X= df.drop('target', axis=1)\n",
        "y= df['target']"
      ],
      "metadata": {
        "id": "bkIm9iUuJiHd"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test= train_test_split(X,y, test_size= 0.2, random_state= 11)"
      ],
      "metadata": {
        "id": "hufYJ1p2KI0Z"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_resampling_results(y_resampled, title):\n",
        "  plt.figure(figsize=(4,4))\n",
        "  pd.Series(y_resampled).value_counts().plot.pie(autopct='%1.1f%%', startangle= 90, colors=['skyblue','lightcoral'])\n",
        "  plt.title(title)\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "OvAv-ATzKs_a"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Undersampling\n",
        "rus= RandomUnderSampler(random_state= 101, sampling_strategy= 1) # sampling_strategy is how many minority instances must be there for 100 majority instances (default = 1)\n",
        "X_rus, y_rus= rus.fit_resample(X_train,y_train)\n",
        "plot_resampling_results(y_rus,'Class distribution after Random Undersampling')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "St418rmDLsbi",
        "outputId": "6c3e165b-107e-4543-fdb8-c9dd560b7b99"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "dahrZDeSMZEQ",
        "outputId": "4789b7a0-ae2c-4145-8886-4e4cc68c08fb"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "B    281\n",
              "M    174\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_rus.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "S27jgvofM8Ey",
        "outputId": "ed6a26cd-34d9-4b7a-964c-f47b92fdd4ad"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "B    174\n",
              "M    174\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('No. of records discarded:',y_train.shape[0]-y_rus.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDo5rac1M_6H",
        "outputId": "5036b4af-9691-4b3f-c653-f6821005dc15"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of records discarded: 107\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Oversampler\n",
        "ros= RandomOverSampler(random_state= 111)\n",
        "X_ros, y_ros= ros.fit_resample(X_train,y_train)\n",
        "plot_resampling_results(y_ros,'CLass distribution after Random Oversampling')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "o46-9NMxOGpq",
        "outputId": "f214c895-5e61-4a43-9924-c26435f24a09"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_ros.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "FGi_zNhKOQFl",
        "outputId": "9ce8bfc6-86db-492f-ca5d-e641a4da693e"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "M    281\n",
              "B    281\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"No. of records added:\",y_ros.shape[0]-y_train.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aG5aKXefOvBW",
        "outputId": "6f3c363e-bcf3-40bc-8e60-af86d7ea1d2f"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of records added: 107\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tomek Undersampling\n",
        "tomek= TomekLinks()\n",
        "X_tl, y_tl= tomek.fit_resample(X_train, y_train)\n",
        "plot_resampling_results(y_tl,'Class distribution after Tomek Undersampling')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "wOU3WPILO74Y",
        "outputId": "127d9cbe-57f4-42b9-ef42-1fb3d053c41b"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_tl.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "NPgcWtTqPuR1",
        "outputId": "c6317b25-1320-402c-8f18-89cfb849bb31"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "B    271\n",
              "M    174\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>174</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('No. of records discarded: ',y_train.shape[0]-y_tl.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdCYs__dP-CH",
        "outputId": "e92fedee-abdf-4364-b52a-c32fcb424e42"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of records discarded:  10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SMOTE\n",
        "smote= SMOTE(random_state= 123)\n",
        "X_smote, y_smote= smote.fit_resample(X_train,y_train)\n",
        "plot_resampling_results(y_smote, 'Class distribution after SMOTE')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "r7biarRUQXrt",
        "outputId": "b591fd25-1ab2-4cf9-b08c-13edbefd0006"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_smote.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "yLgo28s4Q7z_",
        "outputId": "de15e6ef-55d9-40d2-f330-8b747d345f23"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "M    281\n",
              "B    281\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('No. of records added: ',y_smote.shape[0]-y_train.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVAxVEV0RACK",
        "outputId": "e406b64c-d201-4cb2-9dd4-d1ca8f101e24"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of records added:  107\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SMOTE + TomekLinks\n",
        "smote_tomek= SMOTETomek(random_state= 20)\n",
        "X_st, y_st= smote_tomek.fit_resample(X_train,y_train)\n",
        "plot_resampling_results(y_st, 'Class distribution after SMOTE + TomekLinks')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "jlNOmFIXRRxs",
        "outputId": "efe9a972-33ad-462e-d778-762d3e82dc7e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_st.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "90kcO8Q3SC-1",
        "outputId": "31309562-550d-4ed8-cc3c-7875a8ed1738"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "M    272\n",
              "B    272\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('No. of records added : ',y_train.shape[0]-y_st.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rq7VX-MDSLDq",
        "outputId": "dabb5588-0243-4086-c5c5-2d7c2a7f59fa"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of records added :  -89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ADASYN\n",
        "adasyn= ADASYN(random_state= 211)\n",
        "X_adasyn, y_adasyn= adasyn.fit_resample(X_train,y_train)\n",
        "plot_resampling_results(y_adasyn, 'Class distribution after ADASYN')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "iK0SItRKSeu2",
        "outputId": "4a9cd475-2b36-4c4a-f9d8-efbaf96cd216"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_adasyn.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "G1-6MmlDTKNk",
        "outputId": "c17ac336-1bbe-454c-f2db-8b2eace75eb0"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "M    283\n",
              "B    281\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>283</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>281</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"No. of records added: \",y_adasyn.shape[0]-y_train.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FL81120vTN7T",
        "outputId": "03f07c0c-5a7a-4464-d98f-a8771bcc93f9"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of records added:  109\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rKMtgcU8TWXg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}