Normalization.ipynb

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        "id": "6w6kfMRLQ1-W"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        "id": "S8Qd8Se7Q7Ws",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/100-days-of-machine-learning/day25-normalization/wine_data.csv',)"
      ],
      "metadata": {
        "id": "IJdSyjjgQ-2c"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df.head()"
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              "   1  14.23  1.71  2.43  15.6  127   2.8  3.06   .28  2.29  5.64  1.04  3.92  \\\n",
              "0  1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28  4.38  1.05  3.40   \n",
              "1  1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81  5.68  1.03  3.17   \n",
              "2  1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18  7.80  0.86  3.45   \n",
              "3  1  13.24  2.59  2.87  21.0  118  2.80  2.69  0.39  1.82  4.32  1.04  2.93   \n",
              "4  1  14.20  1.76  2.45  15.2  112  3.27  3.39  0.34  1.97  6.75  1.05  2.85   \n",
              "\n",
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              "      <td>735</td>\n",
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              "      <th>4</th>\n",
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        "# header=None,usecols=[0,1,2]\n",
        "df = pd.read_csv('/content/drive/My Drive/100-days-of-machine-learning/day25-normalization/wine_data.csv',header=None,usecols=[0,1,2])"
      ],
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              "   0      1     2\n",
              "0  1  14.23  1.71\n",
              "1  1  13.20  1.78\n",
              "2  1  13.16  2.36\n",
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              "   Class label1  Alcohol  Malic acid\n",
              "0             1    14.23        1.71\n",
              "1             1    13.20        1.78\n",
              "2             1    13.16        2.36\n",
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              "      <th>Malic acid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>14.23</td>\n",
              "      <td>1.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>13.20</td>\n",
              "      <td>1.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>13.16</td>\n",
              "      <td>2.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>14.37</td>\n",
              "      <td>1.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>13.24</td>\n",
              "      <td>2.59</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a98d6366-6a10-4bc5-a100-3518693b8015')\"\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-a98d6366-6a10-4bc5-a100-3518693b8015 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-a98d6366-6a10-4bc5-a100-3518693b8015');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sns.distplot(df[\"Alcohol\"],hist=False)\n",
        "sns.kdeplot(df[\"Alcohol\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "ZpjszXAnTZGw",
        "outputId": "ddbe0481-8118-4de6-c566-84e7fa0fa8b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6617231eb0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(df[\"Malic acid\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "LQ0FeBH9TmTz",
        "outputId": "343a8d5a-c572-4ef5-972f-26df8cda6fb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f66171c9160>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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t9WUhxn9Kaz2drMHYUTxobaH1ExjjL6MGgYjMEJGzgVgRWSUiq71fFwBxU1KhmbSSmjbCw4QFQbAHwUhmJseSnxZn8wmM8YOx+gjejaeDOJe3L//QDvy7n2oyPlZa08bczHhiIsOdLmVS1hSk8cKBE6gqItZPYIyvjBoEqno/cL+IvF9VH52imoyPlda2sc7btBLM1hWm8egbVRyq62B+dvDe3RgTaEYNAhG5QVV/DxSIyJdOfV1VbZG4ANfU2Udta09QdxQPWj83HYBXDjVYEBjjQ2N1Fg/OPkoAEof5MgHurT0Igmfp6ZHkpcVRmBHPPw7ZelPG+NJYTUN3ef/81tSUY3zt5IihaXBHAHDevAweKq6id8BFdERw93kYEyjGu+jcD0QkSUQiReR5EakXkRv8XZyZvNKaNmYmx5AWH+V0KT7xjnmZdPe72HHU9jE2xlfGO4/gXd69A96LZ62hs4B/8VdRxneCcQ+C0ZwzN52IMOHlNxucLsWYaWO8QTDYhPQe4GFVtWUgg0BPv4vD9Z1BufT0SBKiI1g9O9X6CYzxofEGweMicgA4G3heRDKBHv+VZXzh4PF2XG6dNv0Dg86bl0FJTRsNHb1Ol2LMtDDeZahvA84FilS1H+jklG0nTeApmUYjhoY6b75nc6ItZdY8ZIwvjGv1Ua+FeOYTDH3P73xcj/GhfTWtJMVEkJcW63QpPrVkVjKpcZG89GY9V6wM3hVVjQkU412G+gFgLrALGNwvULEgCGgl1a0szUmedssxhIcJG87K4B+HGmy5CWN8YLx3BEXAYlVVfxZjfKff5Wb/8XZuOrfA6VL84rx5mTy+p5YDx9tZNI1GRRnjhPF2Fu8DZvizEONbZXUd9A24p9WIoaHOX+DpJ3jhQJ3DlRgT/MYbBBlAqYg8LSKbB7/8WZiZnH3VnhG+S3OmV0fxoOykGFbkJvNM6QmnSzEm6I23aeib/izC+N6+6lbio8IpDMLN6sfr4sXZ/OiZNznR1kN2UozT5RgTtMY7fPQlPDOKI73fbwfe8GNdZpL21bSxeFYSYdN4f9+LF3taK5/bb3cFxkzGeNca+mfgEeAu71M5wF/8VZSZHJdbKa1pm7bNQoPmZyeQnxbHMyUWBMZMxnj7CD4DbADaAFT1EJDlr6LM5Bxp6KC738XSWdM7CESES5bOYEtZA61d/U6XY0zQGm8Q9Kpq3+AD76QyG0oaoPZVe2YUT/c7AoD3LJvJgFt5uuS406UYE7TGGwQvici/49nE/mLgYeBv/ivLTMa+6laiI8KYmzl9O4oHLc9NJi8tlsf31jpdijFBa7xBcBtQD+wFPgE8Cfw/fxVlJmdfTSuLZiYRET7ef97gJSK8Z9kstpQ10NTZN/YbjDGnGe+oITeezuFPq+oHVPXXNss4MLndSkl1G0tzpudEsuG8d/lMXG7lSbsrMOaMjBoE4vFNEWkADgIHvbuTfX1qyjMTVdncRXvvwLTvKB5qyawkFmQn8ugbVU6XYkxQGuuO4It4RgutUdU0VU0D1gEbROSLfq/OTNhe74ziJSEUBCLC+8/OYeexFsrqOpwux5igM1YQfBi4VlWPDD6hquXADcCN/izMnJk9Va1ERYSxYEai06VMqStX5hAeJnZXYMwZGCsIIlX1tN0/VLUeiPRPSWYydlW2sHhmElER07+jeKispBjOn5/Jozuq6He5nS7HmKAy1qfFaMMwbIhGgHG5lX3VrazMS3G6FEdcuzafuvZenrOF6IyZkLGCYIWItA3z1Q4sm4oCzfiV1XXQ1ediRV7o9A8MddHCLHJSYnlg21GnSzEmqIwaBKoarqpJw3wlqqo1DQWY3ZUtACzPDc07gvAw4bp1+bx6uJGyunanyzEmaPi1IVlELhGRgyJSJiK3DfP6l0SkVET2iMjzIjLbn/VMd7uqWkiMiZjWS0+P5UNr8oiKCOO3WyqcLsWYoOG3IBCRcOAO4FJgMXCtiCw+5bCdQJGqLsezuukP/FVPKNhT1cKK3JRpvfT0WDISonn/6lwe2VFFXXuP0+UYExT8eUewFihT1XLvgnUPAlcMPUBVX1TVLu/DbUCuH+uZ1nr6XRyobWd5bmj2Dwx1y3lz6He5uc/uCowZF38GQQ5QOeRxlfe5kXwM+L/hXhCRW0SkWESK6+vrfVji9FFS08aAW1kRoiOGhirMiOefls7kga1HaemywW3GjCUgBpuLyA1AEfDD4V5X1btVtUhVizIzM6e2uCCxp8rTURyqQ0dP9dlNZ9HRN8BdL5c7XYoxAc+fQVAN5A15nOt97m1E5J3A14DLVbXXj/VMa7srW8hOira9e70WzkjiihWzuHfLEesrMGYM/gyC7cA8ESkUkSjgGmDz0ANEZBWe7S8vV9U6P9Yy7e2uamVFiA4bHckXL57PgEv58dNvOl2KMQHNb0GgqgPArcDTwH7gIVUtEZFvi8jl3sN+CCQAD4vILhHZPMLpzChau/o50tBp/QOnmJ0ez80bCnhoRyW7vHMsjDGni/DnyVX1STyb2Ax97utDvn+nP68fKvZUez7k7I7gdJ/bNI+/7KrhG3/dx58/vYHwEB5aa8xIAqKz2EzOzmMtiMAyGzp6msSYSP7jvYvZXdXKr/9hHcfGDMeCYBooPtrM/KxEkmNt1Y/hXLZ8JpcsmcFPnnmTA8fbnC7HmIBjQRDkXG5l59Fmzi5IdbqUgCUi/Nf7lpIUG8Fn/vAGHb0DTpdkTECxIAhyh+raae8doGi2BcFo0hOi+fm1qzjS0MlXH9mDbbltzFssCIJccUUzAGdbEIzp3LkZ3HbpQp7YW8vtTx1wuhxjAoZfRw0Z/9txtJmMhGjy0+KcLiUo/PM75nCsqYu7XionNS6KT54/1+mSjHGcBUGQ23G0maLZqYjYsMjxEBG+dflSWrr6uf3/DtDd5+IL75xnf38mpFkQBLG69h6ONXVx43rbxmEiwsOEn12zipjIcH72/CEqm7r47lXLiIkMd7o0YxxhQRDEdnj7B1Zb/8CEhYcJP/zAcmanxfHjZ9+ktLaNn35oJYtmJjldmjFTzjqLg1jx0WaiI8JYOssmkp0JEeGzm+Zx781raOjo4/JfvMJ3n9xPW0+/06UZM6UsCILYjqPNrMhNISrC/hkn48IFWTzzxfN436ocfv2Pcjbe/gI/eeYgVc1dY7/ZmGnAPkGCVE+/i5KaVmsW8pG0+Ch+8IEV/O3WjZwzJ52fv1DGxu+/yNW/2sofXjtKRUOnzT0w05b1EQSp3ZUt9LvUJpL52NKcZO6+sYjKpi7+uquaP++s5muP7QM8+yGvzk/hrKwE8tPiyE+LIzU+iqTYSJJiIoiPigjp/aJN8LIgCFLFR62j2J/y0uK49aJ5fObCs3jzRAfFR5vYUdHMrsoWXjhQx4D79LsDEUiIiiA6MpyYyDBiBv+MCD/5fXRkOPFR4cxIimFGciw5qbEsmpFIlm0oZBxkQRCkth5uZOGMRNLio5wuZVoTERbMSGTBjESuX+cZpjvgclPb2kNlUxct3f209/TT1j1Ae08/7b0D9PS76e130TPgoqffTU+/i86+ARo7Pc+39w7Q0NHL0JamjIRoluUksXFeJhcvyiY/3SYImqljQRCEegdcFB9t4po1+U6XEpIiwsPIS4sjbxKzuftdburbe6ls6qK0to2SmjZ2HmvmO4+X8p3HS5mfncDVRXlcvSaPpBhbVdb4lwVBENp1rIWefjfnzk13uhRzhiLDw5iVEsuslFjWzXnr3/FoYyfP7a/jyb21/OcT+/nJs2/ygbNz+eT5c5mVEutgxWY6syAIQlvLGxGBdYUWBNPN7PR4PraxkI9tLGRfdSv3bqngwdcreai4kk+eP5dPnDeX2CibAW18y4aPBqFXDzeydFYyyXHWZDCdLc1J5sdXr+CFr5zPpkXZ/Pdzh9j047/zwoETTpdmphkLgiDT3edi57Fm1luzUMjITY3jjutW89An1pMUG8lH7yvmm5tL6Ol3OV2amSYsCILMjqPN9LvUgiAErS1M4y+f2cDNGwq479UKrrxjC2V1HU6XZaYBC4Ig8+rhBsLDhDUFaU6XYhwQExnONy5bwr03raG+vZer7tzCq4cbnC7LBDkLgiCztbyRFbnJJERbP38ou3BhFn/5zAayk2K48Tev83BxpdMlmSBmQRBEOnoH2FPVas1CBvDMfn7kU+eybk4a//LIHv7n+UNOl2SClAVBENl+pAmXWzl3bobTpZgAkRwbyX03r+WqVTn8+Nk3+fEzB21xPDNh1r4QRF4payAqPMw2qjdvExkexo8+uIKoiDD+54Uy+lxubrtkoW2/acbNgiCIvHigjnVz0mxLRXOasDDhu+9bRkS4cNdL5aBw26UWBmZ8LAiCREVDJ+UNnXzY9ic2IwgLE75zxVIE4a6Xy0mJi+JTF8x1uiwTBCwIgsSLB+sAz25axoxERPjW5Uto7e7n+08dICUukmvX2uKEZnQWBEHixYP1zMmIpyAj3ulSTIALCxN+9MEVtPX087XH9pISG8mly2Y6XZYJYDZqKAh09Q2wrbyRCxfa3YAZn6iIMH55/dmsyk/l8w/u4pVDNunMjMyCIAhsKWukb8DNRRYEZgJio8L57UfWMCcznlseKGZPVYvTJZkAZUEQBF48WEd8VLgtK2EmLDkukt99dC1p8VF89L7tHGvscrokE4D8GgQicomIHBSRMhG5bZjXzxORN0RkQEQ+4M9agpWq8uKBOjbOyyAqwnLbTFxWUgz33byWAbfykXtfp6mzz+mSTIDx2yeLiIQDdwCXAouBa0Vk8SmHHQNuAv7orzqC3cET7dS29lizkJmUs7ISuOfGImpauvnY/dvp7rMlrM1b/Pkr5lqgTFXLVbUPeBC4YugBqlqhqnsAtx/rCGovHPAMG73Aho2aSSoqSONn16xiV2ULn3twJy63LUVhPPwZBDnA0CURq7zPTZiI3CIixSJSXF9f75PigsXTJSdYlpNMdlKM06WYaeCSpTP45mVLeLb0BN/YvM/WJTJAkHQWq+rdqlqkqkWZmZlOlzNlqlu62V3ZwqXLZjhdiplGPnJuAZ84fw6/33aMO/9+2OlyTADw54SyaiBvyONc73NmnJ7adxyAS5faZCDjW19990JOtPbww6cPMjM5hqtW5zpdknGQP+8ItgPzRKRQRKKAa4DNfrzetPPUvloWzkik0GYTGx8LCxN+8IEVnDs3nX99ZA//OBRaTa7m7fwWBKo6ANwKPA3sBx5S1RIR+baIXA4gImtEpAr4IHCXiJT4q55gc6Kth+KjzXY3YPwmKiKMX334bM7KSuBTv3+DkppWp0syDvFrH4GqPqmq81V1rqr+l/e5r6vqZu/321U1V1XjVTVdVZf4s55g8rfdNajCe1dYEBj/SYrxbGyTFBPBTfdup6rZJpyFoqDoLA5Ff91Vw7KcZOZmJjhdipnmZiTHcN9H19Lb7+Ijv32dli6bcBZqLAgC0OH6DvZWt3LFyllOl2JCxPzsRO6+sYjKpm4+fn8xXX0DTpdkppAFQQD6664aROCyFRYEZuqcMyed/75mJW8ca+bj9xfT02+zj0OFBUGAcbuVR3dUsWFuhk0iM1Pun5bN5EcfXMHW8kY++fsd9A5YGIQCC4IAs7W8keqWbq5ekzf2wcb4wVWrc/nu+5bx94P13PrHnfQN2Aow050FQYD50/ZKkmMjedfibKdLMSHs2rX5fPsKz1IUtzxgzUTTnQVBAGnt6uepkuNcuXIWMZHhTpdjQtyN6wv43lXLeOnNem6693U6eq0DebqyIAggD++opG/AzYfW2GbjJjBcuzafn169ku0Vzdxwz2u2l8E0ZUEQINxu5YFtR1lTkMriWUlOl2PMSVeuyuHO61dTWtvGVXdu4UhDp9MlGR+zIAgQLx2q52hjFzeuL3C6FGNO8+4lM/jff15HW88A77tzC9srmpwuyfiQBUGAuHdLBZmJ0bx7iS05bQLT2bPTeOzT55IWF8X1v36NP7x21PYzmCYsCAJASU0rL79Zz03nFti+xCagzU6P59FPncu6OWl87bF9fPnh3bbt5TRgnzoB4K6XykmIjuCGc2Y7XYoxY0qNj+K+m9fy+U3zeGxnNe+7cwsHj/wBQ0QAAAykSURBVLc7XZaZBAsChx1r7OKJvbVcty6f5NhIp8sxZlzCw4QvXjyfe29aQ317L5f9zyv86qXDtg9ykLIgcNjPnj9ERJjwsY2FTpdizIRdsCCLZ754HhctzOL2/zvAB3/1KgeOtzldlpkgCwIHldV18NjOKj58zmxbV8gErfSEaH55w2p+ds1Kyhs6ec/PX+Gbm0to7ep3ujQzThYEDvrpc28SExnOJy+Y63QpxkyKiHDFyhxe/PIFXLs2j99treDCH/+d37xyxJanCAIWBA4prmjiiT21fHxjIRkJ0U6XY4xPpMZH8Z9XLuNvn93IwhmJfOfxUs7/4Yv8bmuFBUIAk2AbB1xUVKTFxcVOlzEpbrdyxR1bqG/v5YWvnE9cVITTJRnjF1sPN/KTZw+yvaKZtPgorlubz4fXW1OoE0Rkh6oWDfeafQI54MHtleytbuWnH1phIWCmtfVz03loznq2lTfx2y1HuOPvZfzqpcNsWpTFB87O44IFmUSGW8OE0+xTaIodb+3he0/uZ/2cdK5cmeN0Ocb4nYiwfm466+emc7Sxk99vO8pjO6t5uuQE6fFRvHvpDC5enM25c9OJjrBVd51gTUNTSFX5+P3FbDncwNNfOI/Z6fFOl2SMI/pdbl5+s54/76zm7wfq6OxzER8VzgULsti0KIsNZ9kOfb5mTUMB4oFtR3n+QB3/8d7FFgImpEWGh7FpUTabFmXT0+9i6+FGnik9wXP7T/DE3loA5mTEc87cdNbPSeecOelkJtqgCn+xO4Ipsq+6lat++Sob5qbz25vWICJOl2RMwHG7ldLaNraVN/Lq4UZeP9J0ckOceVkJnDMnnXVz0lhXaMEwUaPdEVgQTIG69h6u/MUWFHj8sxtJt+GixozLgMvNvpo2th5uZGt5Izsqmuj0LnI3NzOedXPSWVeYxjlz0q0paQwWBA7q7B3g+nte4+Dxdh7+5HqW5iQ7XZIxQWswGF4rb2RbeSPFFc20e+8YCtLjWFfouWM4d24GM5ItGIayIHBIT7+Lj963ndeONHHn9attrwFjfMzlVkpr2njtSCPbypt4/UgjbT2eYFg4I5ELF2Zx4YIsVuenEBHiw1QtCBzQ1tPPP99fzGtHmvjJ1Su4anWu0yUZM+253MqB4228cqiBFw/WUVzRzIBbSYqJ4B3zM7loQRYXLMgMyeZZC4Ipdqyxi1seKKasroMfX72CK2y+gDGOaOvpZ4s3FF48WE99ey8isDo/lU2LsnjnomzmZSWExOANC4Ip9NS+Wr766F5UlV9ct5rz5mc6XZIxBs+IpJKaNp7bf4LnD5xgX7Vnuey8tFg2Lcxm06Is1hWmT9tdAi0IpkBtazf/9cR+Ht9Ty9KcJO687mzy0+OcLssYM4LjrT28cKCO5/ef4JWyBnoH3CRER/COeRlsWpTNhdOsCcmCwI/q23u555Vy7n+1ArfCrReexacumGvrpxgTRLr7XLx6uIHn9tfxwoETnGjzNCGtykth06Jszp+fyaKZSYSHBW8TkgWBj/X0e/7T/GVnDU/tO86A281lK2bxlXctIC/N7gKMCWaqyr7qNp4/cILn99ext7oVgIToCFbPTmXN7FSKCtJYkpNEUkzwbC/rWBCIyCXAz4Bw4B5Vvf2U16OB3wFnA43Ah1S1YrRzOhEEnb0DlNa2saeqla2HG3mlrJ6efjdJMRFctTqXj5xbQGGGLRlhzHR0oq2HbeWNbK9ooriimYMn2hn82MxJiWXRzEQWzkhiXnYCeWlx5KXGkZEQFXAd0I4EgYiEA28CFwNVwHbgWlUtHXLMp4HlqvpJEbkGeJ+qfmi0855pELjdSp/LTe+Am94BF30Dnu/7Btx097to7e6npauPlq5+mrv6OdHaQ2VzF5XNXVQ1d7/tH37Toiw2LcrmnDlptlqiMSGmtaufNyqb2V/bxv7adg7UtlHe0InL/dZnaWxkOLmpsWQlRZMWH016fBRp8VGkxkUSHx1BXFQEcVHhxEeHExcVQWxkOJERYUSECeFhMuTPsJOPwybZLOXUonNrgTJVLfcW8SBwBVA65JgrgG96v38E+IWIiPohne56uZzvP3VgXMeKQHp8NHlpsazKS+WqVbksz01mWU4yWTaN3ZiQlhwXyYULPBPVBvX0uzja2EVlUxdVzV1UNndT1dxFfXsve5tbaOzso9070e1MhQl858qlXL9u9mR/hNP4MwhygMohj6uAdSMdo6oDItIKpAMNQw8SkVuAW7wPO0TkoF8qHqIC2DH6IRmcUmcAshonL9DrA6vRVwK+xhtuJ+OGM69xxAQJimWoVfVu4G6n6xhKRIpHus0KFFbj5AV6fWA1+koo1+jPMY7VQN6Qx7ne54Y9RkQigGQ8ncbGGGOmiD+DYDswT0QKRSQKuAbYfMoxm4GPeL//APCCP/oHjDHGjMxvTUPeNv9bgafxDB/9raqWiMi3gWJV3Qz8BnhARMqAJjxhESwCqqlqBFbj5AV6fWA1+krI1hh0E8qMMcb4lq2DYIwxIc6CwBhjQpwFwQSJyCUiclBEykTkNqfrGY6I/FZE6kRkn9O1DEdE8kTkRREpFZESEfm80zWdSkRiROR1EdntrfFbTtc0EhEJF5GdIvK407UMR0QqRGSviOwSkcBZMXIIEUkRkUdE5ICI7BeR9U7XNJSILPD+/Q1+tYnIF3x2fusjGL/xLJsRCETkPKAD+J2qLnW6nlOJyExgpqq+ISKJeObuXRlIf4/iWSgmXlU7RCQSeAX4vKpuc7i004jIl4AiIElV3+t0PacSkQqgSFUDdrKWiNwP/ENV7/GOcoxT1Ran6xqO93OoGlinqkd9cU67I5iYk8tmqGofMLhsRkBR1ZfxjMIKSKpaq6pveL9vB/bjmWUeMNSjw/sw0vsVcL81iUgu8B7gHqdrCVYikgych2cUI6raF6gh4LUJOOyrEAALgokabtmMgPoACzYiUgCsAl5ztpLTeZtcdgF1wLOqGnA1Av8N/CvgdrqQUSjwjIjs8C4XE2gKgXrgXm8T2z0iEsjLCV8D/K8vT2hBYBwjIgnAo8AXVLXN6XpOpaouVV2JZ1b8WhEJqGY2EXkvUKeqYyyL5biNqroauBT4jLfpMpBEAKuBX6rqKqATCNT+vyjgcuBhX57XgmBixrNshhkHb7v7o8AfVPXPTtczGm8zwYvAJU7XcooNwOXeNvgHgYtE5PfOlnQ6Va32/lkHPIaniTWQVAFVQ+74HsETDIHoUuANVT3hy5NaEEzMeJbNMGPwdsT+Btivqj9xup7hiEimiKR4v4/FM0BgfOuYTxFV/TdVzVXVAjz/F19Q1RscLuttRCTeOyAAb3PLu4CAGs2mqseBShFZ4H1qE29fLj+QXIuPm4UgSFYfDRQjLZvhcFmnEZH/BS4AMkSkCviGqv7G2areZgPwYWCvtw0e4N9V9UkHazrVTOB+7wiNMOAhVQ3I4ZkBLht4zLtbVwTwR1V9ytmShvVZ4A/eX/DKgZsdruc03iC9GPiEz89tw0eNMSa0WdOQMcaEOAsCY4wJcRYExhgT4iwIjDEmxFkQGGNMiLMgMCFDRHTohCsRiRCR+rFW7RSRCwaPEZHL/bXqrIgUicjPR3itQkQy/HFdY2wegQklncBSEYlV1W48Y7InNDPcu8WqXyYRqmoxEJDLNJvpze4ITKh5Es9qnXDKLE0RWSsiW70Lj706ZKYpQ465SUR+4f0+W0Qe8+5ZsFtEzh3m+F+KSPGpexqIyBrvNXZ79z1IPOXOI11EnvG+7x5AfPvXYMxbLAhMqHkQuEZEYoDlvH3V0wPAO7wLj30d+O4Y5/o58JKqrsCzNs1ws8y/pqpF3mudLyLLvbNX/4Rnf4MVwDuB7lPe9w3gFVVdgmd9nvyJ/JDGTIQ1DZmQoqp7vEtfX4vn7mCoZDzLSszDs3Ry5Binuwi40XteF9A6zDFXe5dejsCzbMVi77lrVXW7971tAN5lGAadB1zlff0JEWke309ozMTZHYEJRZuBH3H64l3fAV707up2GRAzmYuISCHwFWCTqi4HnpjsOY3xBwsCE4p+C3xLVfee8nwyb3Ue3zSO8zwPfApObmKTfMrrSXg6qFtFJBvPEsIAB4GZIrLG+95EETn17vxl4Drv65cCqeOox5gzYkFgQo6qVqnqcMM0fwB8T0R2Mr5m088DF4rIXjz7Li8+5Tq7gZ14+h7+CGzxPt8HfAj4HxHZDTzL6XcK3wLOE5ESPE1Ex8b54xkzYbb6qDHGhDi7IzDGmBBnQWCMMSHOgsAYY0KcBYExxoQ4CwJjjAlxFgTGGBPiLAiMMSbE/X+yxVHo4WQTPQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(df[\"Alcohol\"],df[\"Malic acid\"],hue=df[\"Class label1\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "pHrNfXCMT2O4",
        "outputId": "defc51da-75a5-41d6-bf97-dded7c42b802"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6617111d30>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "color_dict={1:\"red\",3:\"green\",2:\"blue\"}\n",
        "sns.scatterplot(df[\"Alcohol\"],df[\"Malic acid\"],hue=df[\"Class label1\"],palette=color_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "VjeACiUGUC_g",
        "outputId": "6dcd363d-b8d6-4fad-86fe-534ff7e61302"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6617013790>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "Eo39iNkFUkvG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,1:]\n",
        "y=df.iloc[:,0:1]"
      ],
      "metadata": {
        "id": "7g64r5CGWLbR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# [\"Class label1\",\"Alcohol\",\"Malic acid\"]\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)"
      ],
      "metadata": {
        "id": "R6cWJ3AxU6xM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape,x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WHguTSUBV5Xl",
        "outputId": "b540abe3-c78f-4649-d9f5-8d8e6776beb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((124, 2), (54, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler=MinMaxScaler()\n",
        "\n",
        "# fit the scaler to the Tran set, it will learn the parameters\n",
        "scaler.fit(x_train)\n",
        "\n",
        "#  transform train and test sets\n",
        "\n",
        "x_train_scaled= scaler.transform(x_train)\n",
        "x_test_scaled = scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "mIfP24FMWCEG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_scaled= pd.DataFrame(x_train_scaled, columns = x_train.columns)\n",
        "x_test_scaled= pd.DataFrame(x_test_scaled, columns = x_test.columns)"
      ],
      "metadata": {
        "id": "uTiJmkZkW_Db"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.round(x_train.describe(),1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "PJdU0kSbXQQi",
        "outputId": "c581da53-546d-4e50-b38d-4ec04797888b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Alcohol  Malic acid\n",
              "count    124.0       124.0\n",
              "mean      13.0         2.4\n",
              "std        0.8         1.1\n",
              "min       11.0         0.9\n",
              "25%       12.4         1.6\n",
              "50%       13.0         1.9\n",
              "75%       13.6         3.2\n",
              "max       14.8         5.6"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c7cfb5c9-4b74-4533-8d91-beda48a13904\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic acid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>124.0</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.0</td>\n",
              "      <td>2.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.8</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.0</td>\n",
              "      <td>0.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.4</td>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.6</td>\n",
              "      <td>3.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.8</td>\n",
              "      <td>5.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c7cfb5c9-4b74-4533-8d91-beda48a13904')\"\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",
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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",
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              "      height: 32px;\n",
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              "    }\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c7cfb5c9-4b74-4533-8d91-beda48a13904 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-c7cfb5c9-4b74-4533-8d91-beda48a13904');\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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.round(x_train_scaled.describe(),1)\n",
        "#  After MinMaxScaling min=0 and max=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Er3xpOppXVom",
        "outputId": "d78b263c-8ab3-4ada-c29b-20eff93a9f66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Alcohol  Malic acid\n",
              "count    124.0       124.0\n",
              "mean       0.5         0.3\n",
              "std        0.2         0.2\n",
              "min        0.0         0.0\n",
              "25%        0.4         0.2\n",
              "50%        0.5         0.2\n",
              "75%        0.7         0.5\n",
              "max        1.0         1.0"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic acid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>124.0</td>\n",
              "      <td>124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-697a5c18-c2fe-42b9-a865-b28009e3610a')\"\n",
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              "          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",
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              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "x_train_scaled.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "9laKH5mOXagG",
        "outputId": "c13af56d-b090-45bd-f278-06970d198819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Alcohol  Malic acid\n",
              "count  124.000000  124.000000\n",
              "mean     0.525017    0.313805\n",
              "std      0.215414    0.238802\n",
              "min      0.000000    0.000000\n",
              "25%      0.358199    0.150735\n",
              "50%      0.540323    0.209034\n",
              "75%      0.701613    0.495273\n",
              "max      1.000000    1.000000"
            ],
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              "\n",
              "  <div id=\"df-5bc6e368-d71f-4142-8f48-17b20c9f5e66\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic acid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>124.000000</td>\n",
              "      <td>124.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.525017</td>\n",
              "      <td>0.313805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.215414</td>\n",
              "      <td>0.238802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.358199</td>\n",
              "      <td>0.150735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.540323</td>\n",
              "      <td>0.209034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.701613</td>\n",
              "      <td>0.495273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5bc6e368-d71f-4142-8f48-17b20c9f5e66')\"\n",
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5bc6e368-d71f-4142-8f48-17b20c9f5e66');\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",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n",
        "\n",
        "ax1.scatter(x_train['Alcohol'], x_train['Malic acid'], c=\"red\")\n",
        "ax1.set_title(\"Before Scaling\")\n",
        "ax2.scatter(x_train_scaled['Alcohol'], x_train_scaled['Malic acid'])\n",
        "ax2.set_title(\"After Scaling\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "BzmtSco2YkKz",
        "outputId": "e5da7182-46fe-4bbc-b8e3-ae811003af4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))\n",
        "\n",
        "# before scaling\n",
        "ax1.set_title('Malic acid Distribution Before Scaling')\n",
        "sns.kdeplot(x_train['Malic acid'], ax=ax1)\n",
        "\n",
        "# after scaling\n",
        "ax2.set_title('Malic acid Distribution After Standard Scaling')\n",
        "sns.kdeplot(x_train_scaled['Malic acid'], ax=ax2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "iXhBmLjbZO2M",
        "outputId": "fd9b172e-76a7-43f1-fdea-2b4f3669926b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zutEotKqa2FZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}