Feature_selection_techniques.ipynb

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        "id": "yqT8VRD-SgVN"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load dataset"
      ],
      "metadata": {
        "id": "YXy_Sy19jO02"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/lois_continuous.csv\")"
      ],
      "metadata": {
        "id": "G04obh-3jQt9"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Show details"
      ],
      "metadata": {
        "id": "kgfCPFQ3jW0F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
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        "id": "qBuilNVAjWAE",
        "outputId": "b69eab09-6d33-41d3-ebeb-0abe13e37bb1"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['FID', 'ID', 'SITE_NAME', 'DATE', 'PH', 'Conductivity ', 'OxygenCount',\n",
              "       'Temperature', 'Battery'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(10)"
      ],
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              "     FID  ID                  SITE_NAME              DATE    PH  \\\n",
              "0  29033  S1  Swale at Catterick Bridge  27/06/1995 11:00  8.27   \n",
              "1  29033  S1  Swale at Catterick Bridge  27/06/1995 11:30  8.31   \n",
              "2  29033  S1  Swale at Catterick Bridge  27/06/1995 12:00  8.34   \n",
              "3  29033  S1  Swale at Catterick Bridge  27/06/1995 12:30  8.36   \n",
              "4  29033  S1  Swale at Catterick Bridge  27/06/1995 13:00  8.39   \n",
              "5  29033  S1  Swale at Catterick Bridge  27/06/1995 13:30  8.41   \n",
              "6  29033  S1  Swale at Catterick Bridge  27/06/1995 14:00  8.44   \n",
              "7  29033  S1  Swale at Catterick Bridge  27/06/1995 14:30  8.46   \n",
              "8  29033  S1  Swale at Catterick Bridge  27/06/1995 15:00  8.48   \n",
              "9  29033  S1  Swale at Catterick Bridge  27/06/1995 15:30  8.50   \n",
              "\n",
              "   Conductivity   OxygenCount  Temperature  Battery  \n",
              "0          411.0        102.2         16.8     13.0  \n",
              "1          412.0        105.8         17.2     13.0  \n",
              "2          413.0        107.0         17.5     13.0  \n",
              "3          414.0        108.7         17.9     13.0  \n",
              "4          416.0        110.2         18.3     13.0  \n",
              "5          417.0        112.5         18.6     13.0  \n",
              "6          419.0        112.6         19.0     13.0  \n",
              "7          418.0        114.4         19.3     13.0  \n",
              "8          417.0        114.3         19.6     13.0  \n",
              "9          417.0        115.5         19.9     13.0  "
            ],
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              "      <td>29033</td>\n",
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              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 11:00</td>\n",
              "      <td>8.27</td>\n",
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              "      <th>1</th>\n",
              "      <td>29033</td>\n",
              "      <td>S1</td>\n",
              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 11:30</td>\n",
              "      <td>8.31</td>\n",
              "      <td>412.0</td>\n",
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              "      <td>17.2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29033</td>\n",
              "      <td>S1</td>\n",
              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 12:00</td>\n",
              "      <td>8.34</td>\n",
              "      <td>413.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>29033</td>\n",
              "      <td>S1</td>\n",
              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 12:30</td>\n",
              "      <td>8.36</td>\n",
              "      <td>414.0</td>\n",
              "      <td>108.7</td>\n",
              "      <td>17.9</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>29033</td>\n",
              "      <td>S1</td>\n",
              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 13:00</td>\n",
              "      <td>8.39</td>\n",
              "      <td>416.0</td>\n",
              "      <td>110.2</td>\n",
              "      <td>18.3</td>\n",
              "      <td>13.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>29033</td>\n",
              "      <td>S1</td>\n",
              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 13:30</td>\n",
              "      <td>8.41</td>\n",
              "      <td>417.0</td>\n",
              "      <td>112.5</td>\n",
              "      <td>18.6</td>\n",
              "      <td>13.0</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>29033</td>\n",
              "      <td>S1</td>\n",
              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 14:00</td>\n",
              "      <td>8.44</td>\n",
              "      <td>419.0</td>\n",
              "      <td>112.6</td>\n",
              "      <td>19.0</td>\n",
              "      <td>13.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>29033</td>\n",
              "      <td>S1</td>\n",
              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 14:30</td>\n",
              "      <td>8.46</td>\n",
              "      <td>418.0</td>\n",
              "      <td>114.4</td>\n",
              "      <td>19.3</td>\n",
              "      <td>13.0</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>29033</td>\n",
              "      <td>S1</td>\n",
              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 15:00</td>\n",
              "      <td>8.48</td>\n",
              "      <td>417.0</td>\n",
              "      <td>114.3</td>\n",
              "      <td>19.6</td>\n",
              "      <td>13.0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>29033</td>\n",
              "      <td>S1</td>\n",
              "      <td>Swale at Catterick Bridge</td>\n",
              "      <td>27/06/1995 15:30</td>\n",
              "      <td>8.50</td>\n",
              "      <td>417.0</td>\n",
              "      <td>115.5</td>\n",
              "      <td>19.9</td>\n",
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            "text/plain": [
              "                FID             PH  Conductivity     OxygenCount  \\\n",
              "count  2.351200e+05  235119.000000  234538.000000  226434.000000   \n",
              "mean   5.217107e+05       7.759112     771.956162     104.046351   \n",
              "std    7.183234e+05       0.468810     368.871777    1408.652813   \n",
              "min    2.903300e+04       3.520000       0.600000       0.000000   \n",
              "25%    2.904100e+04       7.400000     475.000000      69.400000   \n",
              "50%    2.904300e+04       7.770000     685.000000      88.800000   \n",
              "75%    1.552046e+06       8.050000    1087.000000     100.000000   \n",
              "max    1.584377e+06       9.850000    2689.000000   99999.000000   \n",
              "\n",
              "         Temperature        Battery  \n",
              "count  235119.000000  210370.000000  \n",
              "mean       10.479797      14.398177  \n",
              "std         5.642680     435.994489  \n",
              "min        -0.500000      -0.100000  \n",
              "25%         5.600000      12.100000  \n",
              "50%        10.100000      12.800000  \n",
              "75%        15.000000      13.500000  \n",
              "max        30.400000   99999.000000  "
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              "      <th>FID</th>\n",
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              "      <th>count</th>\n",
              "      <td>2.351200e+05</td>\n",
              "      <td>235119.000000</td>\n",
              "      <td>234538.000000</td>\n",
              "      <td>226434.000000</td>\n",
              "      <td>235119.000000</td>\n",
              "      <td>210370.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.217107e+05</td>\n",
              "      <td>7.759112</td>\n",
              "      <td>771.956162</td>\n",
              "      <td>104.046351</td>\n",
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              "      <th>std</th>\n",
              "      <td>7.183234e+05</td>\n",
              "      <td>0.468810</td>\n",
              "      <td>368.871777</td>\n",
              "      <td>1408.652813</td>\n",
              "      <td>5.642680</td>\n",
              "      <td>435.994489</td>\n",
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              "      <th>min</th>\n",
              "      <td>2.903300e+04</td>\n",
              "      <td>3.520000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.500000</td>\n",
              "      <td>-0.100000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>2.904100e+04</td>\n",
              "      <td>7.400000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>69.400000</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>12.100000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.904300e+04</td>\n",
              "      <td>7.770000</td>\n",
              "      <td>685.000000</td>\n",
              "      <td>88.800000</td>\n",
              "      <td>10.100000</td>\n",
              "      <td>12.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.552046e+06</td>\n",
              "      <td>8.050000</td>\n",
              "      <td>1087.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>13.500000</td>\n",
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              "      <th>max</th>\n",
              "      <td>1.584377e+06</td>\n",
              "      <td>9.850000</td>\n",
              "      <td>2689.000000</td>\n",
              "      <td>99999.000000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>99999.000000</td>\n",
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              "    <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-5289de86-4f6e-444d-b623-60a759ec60a4 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-5289de86-4f6e-444d-b623-60a759ec60a4');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kq8xA-Thusiu",
        "outputId": "978f3f8a-38b8-409b-fa61-9e7e2c4ee89b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "235120"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove missing values"
      ],
      "metadata": {
        "id": "dLOaaF0vlgBy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.dropna()\n",
        "len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDYGxBMHlV5y",
        "outputId": "500136c4-4701-4a91-c9fc-557f47c513d4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "202613"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns.tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nYvoUbgl7_6",
        "outputId": "c3d38865-cdae-4bfe-df94-08d5b6431eda"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['FID',\n",
              " 'ID',\n",
              " 'SITE_NAME',\n",
              " 'DATE',\n",
              " 'PH',\n",
              " 'Conductivity ',\n",
              " 'OxygenCount',\n",
              " 'Temperature',\n",
              " 'Battery']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Consider only relevant critical features"
      ],
      "metadata": {
        "id": "W1CVt0ninPRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[['PH','Conductivity ','Temperature','Battery','OxygenCount']]"
      ],
      "metadata": {
        "id": "_-AElwhcltb7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Detect Outliers"
      ],
      "metadata": {
        "id": "QWY0cWcatIPY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Method 1. Visualization"
      ],
      "metadata": {
        "id": "MKoL6pZatPCC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.boxplot(data['PH'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "OiCZEtfnsuDA",
        "outputId": "222b29fb-a23a-4e5a-bb69-9a0302d5d8fd"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6037e5aed0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "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": [
        "data = data[(data[\"PH\"] < 9) & (data[\"PH\"] > 6.5)]"
      ],
      "metadata": {
        "id": "G5PZXADbt9r5"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lEqnCNmfu5Lu",
        "outputId": "be82077f-1317-4cc2-fa33-38f7c3f31c64"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "200526"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data['Conductivity '])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "6Nj0XkZNszIv",
        "outputId": "e66630a2-1e28-403f-f3ae-a460537036b8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6037dc2890>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "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": [
        "data = data[(data[\"Conductivity \"] < 2000) & (data[\"Conductivity \"] > 0)]"
      ],
      "metadata": {
        "id": "tFL0-Y44vR0v"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNEiHZBzvcl-",
        "outputId": "55b017f6-e3f5-48df-9deb-9d682cd22045"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "200078"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data['Temperature'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "EKM4uWdEs6bz",
        "outputId": "7ed0c765-6c0c-4d05-a902-6810f9879ddf"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f60378ee310>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALG0lEQVR4nO3dXYxc513H8d8/dgNOKVVCIlO5LW5xC4gKtY1TEVTAN1RQCUqlqKLcpBKiBYox3ADihlyAxFtfopUoBIgookB5KRBQRSiIlwBSiR0CSeMEVsFVu0oTtxZNgkOiJA8Xc1Ysltf2bnb375n9fG52Znb3zPPsWX995szMszXGCAA774ruAQDsVgIM0ESAAZoIMEATAQZosncjX3zttdeOgwcPbtNQABbTiRMnPj/GuO7c2zcU4IMHD+b48eNbNyqAXaCqPn2+252CAGgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoMmG/iYcL8zS0lKWl5e7h/GCrKysJEkOHDjQPJKtcejQoRw9erR7GOxSAryDlpeXc+/9J/PcVdd0D2XT9pz9YpLkc0/P/6/OnrNnuofALjf//4rmzHNXXZOnvvat3cPYtH0PfjxJ5noOq1bnAl2cAwZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaLIjAV5aWsrS0tJO3BXAltrOfu3dlq2eY3l5eSfuBmDLbWe/nIIAaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmiydyfuZGVlJU899VSOHTu2E3d32VpeXs4Vz4zuYTC54n8ez/LyE7v+95ILW15ezr59+7Zl2xc9Aq6qd1fV8ao6fvr06W0ZBMBudNEj4DHGbUluS5LDhw9v6vDtwIEDSZJbb711M9++MI4dO5YTDz/aPQwmz3/pl+fQq/fv+t9LLmw7HyE5BwzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZrs3Yk7OXTo0E7cDcCW285+7UiAjx49uhN3A7DltrNfTkEANBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoMne7gHsNnvOnsm+Bz/ePYxN23P2C0ky13NYtefsmST7u4fBLibAO+jQoUPdQ3jBVlaeTZIcOLAI4dq/EPuE+SXAO+jo0aPdQwAuI84BAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZrUGOPSv7jqdJJPb+H9X5vk81u4vcvBIs4pWcx5mdN8WIQ5fdUY47pzb9xQgLdaVR0fYxxuG8A2WMQ5JYs5L3OaD4s4p1VOQQA0EWCAJt0Bvq35/rfDIs4pWcx5mdN8WMQ5JWk+Bwywm3UfAQPsWgIM0KQtwFX17VX1UFUtV9VPdo1jK1XVqaq6r6rurarj3ePZjKq6vaoeq6r719x2TVV9oqr+Y/p4decYN2Oded1SVSvT/rq3qt7aOcaNqqpXVNXfVNUDVfWpqjo23T63++sCc5rrfbWelnPAVbUnyb8n+bYkn01yd5J3jjEe2PHBbKGqOpXk8Bhjbl80XlXfkuTJJL81xnjddNsvJDkzxvi56T/Lq8cYP9E5zo1aZ163JHlyjPFLnWPbrKp6WZKXjTHuqaqXJDmR5LuTvCtzur8uMKd3ZI731Xq6joDflGR5jPHwGOOZJL+X5G1NY2GNMcbfJzlzzs1vS/Lh6fKHM/sHMVfWmddcG2M8Msa4Z7r8RJKTSQ5kjvfXBea0kLoCfCDJZ9Zc/2wW44c8kvxlVZ2oqnd3D2YL7R9jPDJd/lyS/Z2D2WI/XFX/Np2imJuH6ueqqoNJ3pDkk1mQ/XXOnJIF2VdreRJua715jPHGJN+R5L3Tw96FMmbnrBbltYsfSvLVSV6f5JEk7+sdzuZU1Zcl+aMkPzrGeHzt5+Z1f51nTguxr87VFeCVJK9Yc/3l021zbYyxMn18LMkfZ3aqZRE8Op2bWz1H91jzeLbEGOPRMcZzY4znk/xa5nB/VdWLMgvVR8YYH5tunuv9db45LcK+Op+uAN+d5DVV9aqqujLJ9yS5o2ksW6KqXjw9aZCqenGStyS5/8LfNTfuSHLzdPnmJH/aOJYtsxqpydszZ/urqirJbyQ5OcZ4/5pPze3+Wm9O876v1tP2TrjpZSQfTLInye1jjJ9tGcgWqapXZ3bUmyR7k/zOPM6pqn43yZHMlgB8NMlPJ/mTJL+f5JWZLUf6jjHGXD2htc68jmT2kHYkOZXkPWvOnV72qurNSe5Kcl+S56ebfyqzc6Zzub8uMKd3Zo731Xq8FRmgiSfhAJoIMEATAQZoIsAATQQYoMne7gGwOKrqK5L89XT1K5M8l+T0dP1N07ofl4WqOpLkmTHGP3WPhd1LgNkyY4wvZPZazctipbGq2jvGeHadTx/JbHW0Sw7wRbYHG+YUBNuqqq6vqr+bFii6c81bZP+2qj5QVcer6mRV3VBVH5vWsP2Z6WsOVtWDVfWR6Wv+sKquuoTtfnBaj/lYVX1nVX2yqv6lqv6qqvZPi7z8QJIfm9aW/eaq+s2qumnNuJ+cPh6pqruq6o4kD1TVnqr6xaq6e1oY5j07+fNksQgw26mSLCW5aYxxfZLbk6x9d+AzY4zDSX4ls7fLvjfJ65K8azqdkSRfk+SXxxhfl+TxJD80rRVwoe1eOcY4PMZ4X5J/SPKNY4w3ZLbs6Y+PMU5N9/mBMcbrxxh3XWQeb0xybIzx2iTfl+SLY4wbktyQ5Pur6lUb/9GAUxBsry/JLKifmL3FP3syW8lq1er6H/cl+dTqW0ur6uHMFmv6rySfGWP84/R1v53kR5L8xUW2+9E1l1+e5KPTEfKVSf5zE/P45zHG6ve9Jck3rDlafmmS12xyu+xyAsx2qszCeuM6n396+vj8msur11d/N899r/y4hO3+95rLS0neP8a4Y3ri7ZZ1vufZTI8Iq+qKzGJ9vu1VkqNjjDvX2Q5cMqcg2E5PJ7muqm5MZssMVtXXb3Abr1z9/iTfm9kphYc2sN2X5v+WOr15ze1PJHnJmuunklw/Xf6uJC9aZ3t3JvnB6TRIquq10+p3sGECzHZ6PslNSX6+qv41yb1JvmmD23gos8XtTya5OsmHppezXep2b0nyB1V1Isnav9X3Z0nevvokXGZrzH7rtL0b8/+Petf69SQPJLmnZn/g81fjkSSbZDU0LlvTqxX+fPWPaMKicQQM0MQRMEATR8AATQQYoIkAAzQRYIAmAgzQ5H8BwLyyELneivAAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[(data[\"Temperature\"] < 30) & (data[\"Temperature\"] > 0)]"
      ],
      "metadata": {
        "id": "2_ZPmjCFvjXF"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3eK0L9N3v4vq",
        "outputId": "fe9b51a7-e751-4095-d246-99a602f42dae"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "199378"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data['Battery'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "zlHRwFIEs9jz",
        "outputId": "cd98bec9-1d67-4e7a-fe50-f6a330c668c4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. 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",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f603786e190>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "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": "markdown",
      "source": [
        "No Categorical features to encode So skipping that phase\n",
        "\n"
      ],
      "metadata": {
        "id": "W_t2JmzRwuGf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Univariate analysis. \n",
        "This is when you analyze a **single** **feature**, and the most common two techniques for this are\n",
        "\n",
        " 1) Remove features with low variance \n",
        "\n",
        " \n",
        " 2) Remove features that have a high amount of missing values."
      ],
      "metadata": {
        "id": "4c1acVZl0JPz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Remove features with low variance"
      ],
      "metadata": {
        "id": "8tzNl9pv0vi6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_selection import VarianceThreshold\n",
        "\n",
        "variance = VarianceThreshold(threshold = (.9 * (1 - .9)))\n",
        "variance.fit(data)\n",
        "\n",
        "variance.get_support()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3ncP3MutW6y",
        "outputId": "edc3a9ce-a198-4c1c-af9b-77877fd156ad"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is no feature with low variance"
      ],
      "metadata": {
        "id": "lriHr2sG01Wk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Remove features with missing values"
      ],
      "metadata": {
        "id": "tbuOumIw1CYI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.dropna()\n",
        "len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9TpD1UP1GjK",
        "outputId": "42a49602-1ee2-450b-b603-1bb73f76fe20"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "199378"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Bi-Variate analysis"
      ],
      "metadata": {
        "id": "smvMeEVk1s6c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Filter based methods"
      ],
      "metadata": {
        "id": "5W3XuUkz-Kyo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Identify highly correlated features."
      ],
      "metadata": {
        "id": "NLQ9_lvp1i7E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_cor=data.corr()\n",
        "data_cor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mOjWaKyg6243",
        "outputId": "d0aa687f-8484-4148-ccd9-7077d9fe91ef"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     PH  Conductivity   Temperature   Battery  OxygenCount\n",
              "PH             1.000000      -0.345254    -0.082669 -0.002428     0.002847\n",
              "Conductivity  -0.345254       1.000000     0.292360 -0.003717     0.003416\n",
              "Temperature   -0.082669       0.292360     1.000000 -0.000749     0.007515\n",
              "Battery       -0.002428      -0.003717    -0.000749  1.000000    -0.000011\n",
              "OxygenCount    0.002847       0.003416     0.007515 -0.000011     1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40c97840-2d3a-4c7d-b390-658a9afce5db\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PH</th>\n",
              "      <th>Conductivity</th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Battery</th>\n",
              "      <th>OxygenCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PH</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.345254</td>\n",
              "      <td>-0.082669</td>\n",
              "      <td>-0.002428</td>\n",
              "      <td>0.002847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Conductivity</th>\n",
              "      <td>-0.345254</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.292360</td>\n",
              "      <td>-0.003717</td>\n",
              "      <td>0.003416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Temperature</th>\n",
              "      <td>-0.082669</td>\n",
              "      <td>0.292360</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000749</td>\n",
              "      <td>0.007515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Battery</th>\n",
              "      <td>-0.002428</td>\n",
              "      <td>-0.003717</td>\n",
              "      <td>-0.000749</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OxygenCount</th>\n",
              "      <td>0.002847</td>\n",
              "      <td>0.003416</td>\n",
              "      <td>0.007515</td>\n",
              "      <td>-0.000011</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40c97840-2d3a-4c7d-b390-658a9afce5db')\"\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",
              "      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-40c97840-2d3a-4c7d-b390-658a9afce5db 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-40c97840-2d3a-4c7d-b390-658a9afce5db');\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": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10,6))\n",
        "sns.heatmap(data_cor, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "vjfduPnU6775",
        "outputId": "ba0a0379-2c1f-4ffb-ed12-a7c51133e5e7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f603782d710>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above correlation metrix, we could identify that there is no correlated features to remove"
      ],
      "metadata": {
        "id": "PbAY6eGM901R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Wrapper based methods"
      ],
      "metadata": {
        "id": "GW-9oVXn-SW9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1-SequentialFeatureSelector"
      ],
      "metadata": {
        "id": "J4nkSTPJ-fYs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "X = data.drop('OxygenCount', axis=1)\n",
        "y = data[['OxygenCount']]\n"
      ],
      "metadata": {
        "id": "tpwXNhqN-kHc"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = y.values.ravel()"
      ],
      "metadata": {
        "id": "ymfpFRHw_Gax"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Llfu5DdM_atV",
        "outputId": "d6fbe061-7a35-4db5-febf-0046f3631b26"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(199378,)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKkwRjA7AUUv",
        "outputId": "b5e5e813-60f1-4418-f2e2-106ae685df14"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(199378, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scaling the features"
      ],
      "metadata": {
        "id": "MwELpgZ3_qer"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "iVl2ZdSQ_oVF"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhPh1O7bCyI2",
        "outputId": "5a53cf28-2bba-4536-b5d3-fb27e8aae8fd"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.27419355e-01, 3.04304304e-01, 2.07142857e-01, 1.31001179e-04],\n",
              "       [6.16935484e-01, 3.45845846e-01, 4.28571429e-01, 1.28001152e-04],\n",
              "       [2.62096774e-01, 5.98098098e-01, 4.78571429e-01, 1.23001107e-04],\n",
              "       ...,\n",
              "       [3.26612903e-01, 6.34134134e-01, 2.60714286e-01, 1.36001224e-04],\n",
              "       [3.95161290e-01, 6.20620621e-02, 3.07142857e-01, 1.35001215e-04],\n",
              "       [7.41935484e-01, 4.92492492e-01, 3.85714286e-01, 1.29001161e-04]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GdiurYcrCPQX",
        "outputId": "e2d74b6d-21bd-4f18-f3c6-a85f4e41f5ba"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.04838710e-01, 6.62662663e-01, 6.85714286e-01, 1.38001242e-04],\n",
              "       [9.71774194e-01, 2.00200200e-01, 6.42857143e-01, 1.34001206e-04],\n",
              "       [6.25000000e-01, 2.90290290e-01, 4.57142857e-01, 1.39001251e-04],\n",
              "       ...,\n",
              "       [2.70161290e-01, 3.76376376e-01, 1.82142857e-01, 1.13001017e-04],\n",
              "       [5.96774194e-01, 3.20820821e-01, 4.78571429e-01, 9.80008820e-05],\n",
              "       [2.78225806e-01, 4.73473473e-01, 3.71428571e-01, 1.19001071e-04]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mlxtend"
      ],
      "metadata": {
        "id": "ibNseRlKDB20"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "import sys\n",
        "sys.modules['sklearn.externals.joblib'] = joblib\n",
        "from mlxtend.feature_selection import SequentialFeatureSelector as SFS"
      ],
      "metadata": {
        "id": "mBZi_LngDG9k"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sfs = SFS(LinearRegression(),\n",
        "          k_features='best',\n",
        "          verbose=1,\n",
        "          forward=True,\n",
        "          floating=False,\n",
        "          scoring='r2',\n",
        "          cv=5)"
      ],
      "metadata": {
        "id": "6dujfznnDYzT"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sfs.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cUldIQVuJPw4",
        "outputId": "8ab62ba5-7ab7-4b23-b598-6c2bd3cfb9df"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.5s finished\n",
            "Features: 1/4[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    1.0s finished\n",
            "Features: 2/4[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    1.0s finished\n",
            "Features: 3/4[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.7s finished\n",
            "Features: 4/4"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SequentialFeatureSelector(estimator=LinearRegression(), k_features='best',\n",
              "                          scoring='r2', verbose=1)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sfs.k_feature_names_ "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYWUVFwEJ5_Q",
        "outputId": "00ef03ee-5b5c-478d-9cee-1333193bdf04"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Battery',)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs\n",
        "import matplotlib.pyplot as plt\n",
        "fig1 = plot_sfs(sfs.get_metric_dict(), kind='std_dev')\n",
        "plt.title('Sequential Forward Selection')\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "gPMwxbBKKM9v",
        "outputId": "67206eb3-3303-4b56-84a0-d2727ddac804"
      },
      "execution_count": 39,
      "outputs": [
        {
          "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": [
        "pd.DataFrame.from_dict(sfs.get_metric_dict()).T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "Dc0GfrOrQ0zM",
        "outputId": "f9921b70-cde6-44a5-cd5f-159d122a91ac"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    feature_idx                                          cv_scores avg_score  \\\n",
              "1          (3,)  [-0.4295356449369081, -0.004995442073238543, -... -0.826537   \n",
              "2        (0, 3)  [-0.6680652580312587, -0.004788444773241807, -... -0.871259   \n",
              "3     (0, 1, 3)  [-0.26015804597624803, -0.011192652805912306, ... -1.130841   \n",
              "4  (0, 1, 2, 3)  [-1.6135625446095978, -0.013916863628949283, -... -1.462484   \n",
              "\n",
              "                               feature_names  ci_bound   std_dev   std_err  \n",
              "1                                 (Battery,)  0.990849  0.770914  0.385457  \n",
              "2                              (PH, Battery)  1.055786  0.821437  0.410719  \n",
              "3               (PH, Conductivity , Battery)  1.765003  1.373233  0.686616  \n",
              "4  (PH, Conductivity , Temperature, Battery)  1.709364  1.329943  0.664972  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-02b9aedf-2937-4ad9-8e76-9abd6e852001\">\n",
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              "      <th></th>\n",
              "      <th>feature_idx</th>\n",
              "      <th>cv_scores</th>\n",
              "      <th>avg_score</th>\n",
              "      <th>feature_names</th>\n",
              "      <th>ci_bound</th>\n",
              "      <th>std_dev</th>\n",
              "      <th>std_err</th>\n",
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              "      <th>1</th>\n",
              "      <td>(3,)</td>\n",
              "      <td>[-0.4295356449369081, -0.004995442073238543, -...</td>\n",
              "      <td>-0.826537</td>\n",
              "      <td>(Battery,)</td>\n",
              "      <td>0.990849</td>\n",
              "      <td>0.770914</td>\n",
              "      <td>0.385457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(0, 3)</td>\n",
              "      <td>[-0.6680652580312587, -0.004788444773241807, -...</td>\n",
              "      <td>-0.871259</td>\n",
              "      <td>(PH, Battery)</td>\n",
              "      <td>1.055786</td>\n",
              "      <td>0.821437</td>\n",
              "      <td>0.410719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(0, 1, 3)</td>\n",
              "      <td>[-0.26015804597624803, -0.011192652805912306, ...</td>\n",
              "      <td>-1.130841</td>\n",
              "      <td>(PH, Conductivity , Battery)</td>\n",
              "      <td>1.765003</td>\n",
              "      <td>1.373233</td>\n",
              "      <td>0.686616</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(0, 1, 2, 3)</td>\n",
              "      <td>[-1.6135625446095978, -0.013916863628949283, -...</td>\n",
              "      <td>-1.462484</td>\n",
              "      <td>(PH, Conductivity , Temperature, Battery)</td>\n",
              "      <td>1.709364</td>\n",
              "      <td>1.329943</td>\n",
              "      <td>0.664972</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "          const element = document.querySelector('#df-02b9aedf-2937-4ad9-8e76-9abd6e852001');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    }
  ]
}