Spine.ipynb

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    "colab": {
      "name": "Spine.ipynb",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd /content/drive/My Drive/Dava with ML"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Dava with ML\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYtLzjD_UU_7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "213e4cd8-0f3d-4c90-c4c3-dbbd1d845bcf"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import sklearn.metrics as m"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-1qCI73gCjI",
        "colab_type": "code",
        "outputId": "b85e5ca6-a741-43c8-d8c4-23cca7a940da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "breast-cancer-wisconsin-data.zip  heart.ipynb\n",
            "Cancer.ipynb                      indian_liver_patient.csv\n",
            "chronic-kidney-disease.zip        indian-liver-patient-records.zip\n",
            "data.csv                          kidney.ipynb\n",
            "Dataset_spine.csv                 liver.ipynb\n",
            "diabetes.csv                      new_model.csv\n",
            "Diabeties.ipynb                   pima-indians-diabetes-database.zip\n",
            "heart.csv                         Spine.ipynb\n",
            "heart-disease-uci.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GGYSBvphgYu",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "dataset=pd.read_csv('Dataset_spine.csv')\n",
        "dataset"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>Class_att</th>\n",
              "      <th>Unnamed: 13</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.027818</td>\n",
              "      <td>22.552586</td>\n",
              "      <td>39.609117</td>\n",
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              "      <td>15.30468</td>\n",
              "      <td>-28.658501</td>\n",
              "      <td>43.5123</td>\n",
              "      <td>Abnormal</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39.056951</td>\n",
              "      <td>10.060991</td>\n",
              "      <td>25.015378</td>\n",
              "      <td>28.995960</td>\n",
              "      <td>114.405425</td>\n",
              "      <td>4.564259</td>\n",
              "      <td>0.415186</td>\n",
              "      <td>12.8874</td>\n",
              "      <td>17.5323</td>\n",
              "      <td>16.78486</td>\n",
              "      <td>-25.530607</td>\n",
              "      <td>16.1102</td>\n",
              "      <td>Abnormal</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68.832021</td>\n",
              "      <td>22.218482</td>\n",
              "      <td>50.092194</td>\n",
              "      <td>46.613539</td>\n",
              "      <td>105.985135</td>\n",
              "      <td>-3.530317</td>\n",
              "      <td>0.474889</td>\n",
              "      <td>26.8343</td>\n",
              "      <td>17.4861</td>\n",
              "      <td>16.65897</td>\n",
              "      <td>-29.031888</td>\n",
              "      <td>19.2221</td>\n",
              "      <td>Abnormal</td>\n",
              "      <td>Prediction is done by using binary classificat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>69.297008</td>\n",
              "      <td>24.652878</td>\n",
              "      <td>44.311238</td>\n",
              "      <td>44.644130</td>\n",
              "      <td>101.868495</td>\n",
              "      <td>11.211523</td>\n",
              "      <td>0.369345</td>\n",
              "      <td>23.5603</td>\n",
              "      <td>12.7074</td>\n",
              "      <td>11.42447</td>\n",
              "      <td>-30.470246</td>\n",
              "      <td>18.8329</td>\n",
              "      <td>Abnormal</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49.712859</td>\n",
              "      <td>9.652075</td>\n",
              "      <td>28.317406</td>\n",
              "      <td>40.060784</td>\n",
              "      <td>108.168725</td>\n",
              "      <td>7.918501</td>\n",
              "      <td>0.543360</td>\n",
              "      <td>35.4940</td>\n",
              "      <td>15.9546</td>\n",
              "      <td>8.87237</td>\n",
              "      <td>-16.378376</td>\n",
              "      <td>24.9171</td>\n",
              "      <td>Abnormal</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>47.903565</td>\n",
              "      <td>13.616688</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>34.286877</td>\n",
              "      <td>117.449062</td>\n",
              "      <td>-4.245395</td>\n",
              "      <td>0.129744</td>\n",
              "      <td>7.8433</td>\n",
              "      <td>14.7484</td>\n",
              "      <td>8.51707</td>\n",
              "      <td>-15.728927</td>\n",
              "      <td>11.5472</td>\n",
              "      <td>Normal</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>53.936748</td>\n",
              "      <td>20.721496</td>\n",
              "      <td>29.220534</td>\n",
              "      <td>33.215251</td>\n",
              "      <td>114.365845</td>\n",
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              "      <td>0.047913</td>\n",
              "      <td>19.1986</td>\n",
              "      <td>18.1972</td>\n",
              "      <td>7.08745</td>\n",
              "      <td>6.013843</td>\n",
              "      <td>43.8693</td>\n",
              "      <td>Normal</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>61.446597</td>\n",
              "      <td>22.694968</td>\n",
              "      <td>46.170347</td>\n",
              "      <td>38.751628</td>\n",
              "      <td>125.670725</td>\n",
              "      <td>-2.707880</td>\n",
              "      <td>0.081070</td>\n",
              "      <td>16.2059</td>\n",
              "      <td>13.5565</td>\n",
              "      <td>8.89572</td>\n",
              "      <td>3.564463</td>\n",
              "      <td>18.4151</td>\n",
              "      <td>Normal</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>45.252792</td>\n",
              "      <td>8.693157</td>\n",
              "      <td>41.583126</td>\n",
              "      <td>36.559635</td>\n",
              "      <td>118.545842</td>\n",
              "      <td>0.214750</td>\n",
              "      <td>0.159251</td>\n",
              "      <td>14.7334</td>\n",
              "      <td>16.0928</td>\n",
              "      <td>9.75922</td>\n",
              "      <td>5.767308</td>\n",
              "      <td>33.7192</td>\n",
              "      <td>Normal</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>33.841641</td>\n",
              "      <td>5.073991</td>\n",
              "      <td>36.641233</td>\n",
              "      <td>28.767649</td>\n",
              "      <td>123.945244</td>\n",
              "      <td>-0.199249</td>\n",
              "      <td>0.674504</td>\n",
              "      <td>19.3825</td>\n",
              "      <td>17.6963</td>\n",
              "      <td>13.72929</td>\n",
              "      <td>1.783007</td>\n",
              "      <td>40.6049</td>\n",
              "      <td>Normal</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 14 columns</p>\n",
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              "          Col1  ...                                        Unnamed: 13\n",
              "0    63.027818  ...                                                NaN\n",
              "1    39.056951  ...                                                NaN\n",
              "2    68.832021  ...  Prediction is done by using binary classificat...\n",
              "3    69.297008  ...                                                NaN\n",
              "4    49.712859  ...                                                NaN\n",
              "..         ...  ...                                                ...\n",
              "305  47.903565  ...                                                NaN\n",
              "306  53.936748  ...                                                NaN\n",
              "307  61.446597  ...                                                NaN\n",
              "308  45.252792  ...                                                NaN\n",
              "309  33.841641  ...                                                NaN\n",
              "\n",
              "[310 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "qSG0McV2hkSn",
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        "colab": {}
      },
      "source": [
        "dataset=dataset.drop(['Unnamed: 13'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8mnTe3F-RLT",
        "colab_type": "code",
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        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>63.027818</td>\n",
              "      <td>22.552586</td>\n",
              "      <td>39.609117</td>\n",
              "      <td>40.475232</td>\n",
              "      <td>98.672917</td>\n",
              "      <td>-0.254400</td>\n",
              "      <td>0.744503</td>\n",
              "      <td>12.5661</td>\n",
              "      <td>14.5386</td>\n",
              "      <td>15.30468</td>\n",
              "      <td>-28.658501</td>\n",
              "      <td>43.5123</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39.056951</td>\n",
              "      <td>10.060991</td>\n",
              "      <td>25.015378</td>\n",
              "      <td>28.995960</td>\n",
              "      <td>114.405425</td>\n",
              "      <td>4.564259</td>\n",
              "      <td>0.415186</td>\n",
              "      <td>12.8874</td>\n",
              "      <td>17.5323</td>\n",
              "      <td>16.78486</td>\n",
              "      <td>-25.530607</td>\n",
              "      <td>16.1102</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68.832021</td>\n",
              "      <td>22.218482</td>\n",
              "      <td>50.092194</td>\n",
              "      <td>46.613539</td>\n",
              "      <td>105.985135</td>\n",
              "      <td>-3.530317</td>\n",
              "      <td>0.474889</td>\n",
              "      <td>26.8343</td>\n",
              "      <td>17.4861</td>\n",
              "      <td>16.65897</td>\n",
              "      <td>-29.031888</td>\n",
              "      <td>19.2221</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>69.297008</td>\n",
              "      <td>24.652878</td>\n",
              "      <td>44.311238</td>\n",
              "      <td>44.644130</td>\n",
              "      <td>101.868495</td>\n",
              "      <td>11.211523</td>\n",
              "      <td>0.369345</td>\n",
              "      <td>23.5603</td>\n",
              "      <td>12.7074</td>\n",
              "      <td>11.42447</td>\n",
              "      <td>-30.470246</td>\n",
              "      <td>18.8329</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49.712859</td>\n",
              "      <td>9.652075</td>\n",
              "      <td>28.317406</td>\n",
              "      <td>40.060784</td>\n",
              "      <td>108.168725</td>\n",
              "      <td>7.918501</td>\n",
              "      <td>0.543360</td>\n",
              "      <td>35.4940</td>\n",
              "      <td>15.9546</td>\n",
              "      <td>8.87237</td>\n",
              "      <td>-16.378376</td>\n",
              "      <td>24.9171</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>305</th>\n",
              "      <td>47.903565</td>\n",
              "      <td>13.616688</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>34.286877</td>\n",
              "      <td>117.449062</td>\n",
              "      <td>-4.245395</td>\n",
              "      <td>0.129744</td>\n",
              "      <td>7.8433</td>\n",
              "      <td>14.7484</td>\n",
              "      <td>8.51707</td>\n",
              "      <td>-15.728927</td>\n",
              "      <td>11.5472</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>53.936748</td>\n",
              "      <td>20.721496</td>\n",
              "      <td>29.220534</td>\n",
              "      <td>33.215251</td>\n",
              "      <td>114.365845</td>\n",
              "      <td>-0.421010</td>\n",
              "      <td>0.047913</td>\n",
              "      <td>19.1986</td>\n",
              "      <td>18.1972</td>\n",
              "      <td>7.08745</td>\n",
              "      <td>6.013843</td>\n",
              "      <td>43.8693</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>61.446597</td>\n",
              "      <td>22.694968</td>\n",
              "      <td>46.170347</td>\n",
              "      <td>38.751628</td>\n",
              "      <td>125.670725</td>\n",
              "      <td>-2.707880</td>\n",
              "      <td>0.081070</td>\n",
              "      <td>16.2059</td>\n",
              "      <td>13.5565</td>\n",
              "      <td>8.89572</td>\n",
              "      <td>3.564463</td>\n",
              "      <td>18.4151</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>45.252792</td>\n",
              "      <td>8.693157</td>\n",
              "      <td>41.583126</td>\n",
              "      <td>36.559635</td>\n",
              "      <td>118.545842</td>\n",
              "      <td>0.214750</td>\n",
              "      <td>0.159251</td>\n",
              "      <td>14.7334</td>\n",
              "      <td>16.0928</td>\n",
              "      <td>9.75922</td>\n",
              "      <td>5.767308</td>\n",
              "      <td>33.7192</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>33.841641</td>\n",
              "      <td>5.073991</td>\n",
              "      <td>36.641233</td>\n",
              "      <td>28.767649</td>\n",
              "      <td>123.945244</td>\n",
              "      <td>-0.199249</td>\n",
              "      <td>0.674504</td>\n",
              "      <td>19.3825</td>\n",
              "      <td>17.6963</td>\n",
              "      <td>13.72929</td>\n",
              "      <td>1.783007</td>\n",
              "      <td>40.6049</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Col1       Col2       Col3  ...      Col11    Col12  Class_att\n",
              "0    63.027818  22.552586  39.609117  ... -28.658501  43.5123   Abnormal\n",
              "1    39.056951  10.060991  25.015378  ... -25.530607  16.1102   Abnormal\n",
              "2    68.832021  22.218482  50.092194  ... -29.031888  19.2221   Abnormal\n",
              "3    69.297008  24.652878  44.311238  ... -30.470246  18.8329   Abnormal\n",
              "4    49.712859   9.652075  28.317406  ... -16.378376  24.9171   Abnormal\n",
              "..         ...        ...        ...  ...        ...      ...        ...\n",
              "305  47.903565  13.616688  36.000000  ... -15.728927  11.5472     Normal\n",
              "306  53.936748  20.721496  29.220534  ...   6.013843  43.8693     Normal\n",
              "307  61.446597  22.694968  46.170347  ...   3.564463  18.4151     Normal\n",
              "308  45.252792   8.693157  41.583126  ...   5.767308  33.7192     Normal\n",
              "309  33.841641   5.073991  36.641233  ...   1.783007  40.6049     Normal\n",
              "\n",
              "[310 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WfT0ynm-eyA",
        "colab_type": "code",
        "outputId": "d3179c23-f33c-49c8-eb81-a00c747153f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.countplot(x='Class_att',data=dataset)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3941216668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBKlySOEA7Hc",
        "colab_type": "code",
        "outputId": "5d15d49f-09a5-490c-d5a0-8fd8fcde1ea1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Col1</th>\n",
              "      <th>Col2</th>\n",
              "      <th>Col3</th>\n",
              "      <th>Col4</th>\n",
              "      <th>Col5</th>\n",
              "      <th>Col6</th>\n",
              "      <th>Col7</th>\n",
              "      <th>Col8</th>\n",
              "      <th>Col9</th>\n",
              "      <th>Col10</th>\n",
              "      <th>Col11</th>\n",
              "      <th>Col12</th>\n",
              "      <th>Class_att</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.027818</td>\n",
              "      <td>22.552586</td>\n",
              "      <td>39.609117</td>\n",
              "      <td>40.475232</td>\n",
              "      <td>98.672917</td>\n",
              "      <td>-0.254400</td>\n",
              "      <td>0.744503</td>\n",
              "      <td>12.5661</td>\n",
              "      <td>14.5386</td>\n",
              "      <td>15.30468</td>\n",
              "      <td>-28.658501</td>\n",
              "      <td>43.5123</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39.056951</td>\n",
              "      <td>10.060991</td>\n",
              "      <td>25.015378</td>\n",
              "      <td>28.995960</td>\n",
              "      <td>114.405425</td>\n",
              "      <td>4.564259</td>\n",
              "      <td>0.415186</td>\n",
              "      <td>12.8874</td>\n",
              "      <td>17.5323</td>\n",
              "      <td>16.78486</td>\n",
              "      <td>-25.530607</td>\n",
              "      <td>16.1102</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68.832021</td>\n",
              "      <td>22.218482</td>\n",
              "      <td>50.092194</td>\n",
              "      <td>46.613539</td>\n",
              "      <td>105.985135</td>\n",
              "      <td>-3.530317</td>\n",
              "      <td>0.474889</td>\n",
              "      <td>26.8343</td>\n",
              "      <td>17.4861</td>\n",
              "      <td>16.65897</td>\n",
              "      <td>-29.031888</td>\n",
              "      <td>19.2221</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>69.297008</td>\n",
              "      <td>24.652878</td>\n",
              "      <td>44.311238</td>\n",
              "      <td>44.644130</td>\n",
              "      <td>101.868495</td>\n",
              "      <td>11.211523</td>\n",
              "      <td>0.369345</td>\n",
              "      <td>23.5603</td>\n",
              "      <td>12.7074</td>\n",
              "      <td>11.42447</td>\n",
              "      <td>-30.470246</td>\n",
              "      <td>18.8329</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49.712859</td>\n",
              "      <td>9.652075</td>\n",
              "      <td>28.317406</td>\n",
              "      <td>40.060784</td>\n",
              "      <td>108.168725</td>\n",
              "      <td>7.918501</td>\n",
              "      <td>0.543360</td>\n",
              "      <td>35.4940</td>\n",
              "      <td>15.9546</td>\n",
              "      <td>8.87237</td>\n",
              "      <td>-16.378376</td>\n",
              "      <td>24.9171</td>\n",
              "      <td>Abnormal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>47.903565</td>\n",
              "      <td>13.616688</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>34.286877</td>\n",
              "      <td>117.449062</td>\n",
              "      <td>-4.245395</td>\n",
              "      <td>0.129744</td>\n",
              "      <td>7.8433</td>\n",
              "      <td>14.7484</td>\n",
              "      <td>8.51707</td>\n",
              "      <td>-15.728927</td>\n",
              "      <td>11.5472</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>53.936748</td>\n",
              "      <td>20.721496</td>\n",
              "      <td>29.220534</td>\n",
              "      <td>33.215251</td>\n",
              "      <td>114.365845</td>\n",
              "      <td>-0.421010</td>\n",
              "      <td>0.047913</td>\n",
              "      <td>19.1986</td>\n",
              "      <td>18.1972</td>\n",
              "      <td>7.08745</td>\n",
              "      <td>6.013843</td>\n",
              "      <td>43.8693</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>61.446597</td>\n",
              "      <td>22.694968</td>\n",
              "      <td>46.170347</td>\n",
              "      <td>38.751628</td>\n",
              "      <td>125.670725</td>\n",
              "      <td>-2.707880</td>\n",
              "      <td>0.081070</td>\n",
              "      <td>16.2059</td>\n",
              "      <td>13.5565</td>\n",
              "      <td>8.89572</td>\n",
              "      <td>3.564463</td>\n",
              "      <td>18.4151</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>45.252792</td>\n",
              "      <td>8.693157</td>\n",
              "      <td>41.583126</td>\n",
              "      <td>36.559635</td>\n",
              "      <td>118.545842</td>\n",
              "      <td>0.214750</td>\n",
              "      <td>0.159251</td>\n",
              "      <td>14.7334</td>\n",
              "      <td>16.0928</td>\n",
              "      <td>9.75922</td>\n",
              "      <td>5.767308</td>\n",
              "      <td>33.7192</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>33.841641</td>\n",
              "      <td>5.073991</td>\n",
              "      <td>36.641233</td>\n",
              "      <td>28.767649</td>\n",
              "      <td>123.945244</td>\n",
              "      <td>-0.199249</td>\n",
              "      <td>0.674504</td>\n",
              "      <td>19.3825</td>\n",
              "      <td>17.6963</td>\n",
              "      <td>13.72929</td>\n",
              "      <td>1.783007</td>\n",
              "      <td>40.6049</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Col1       Col2       Col3  ...      Col11    Col12  Class_att\n",
              "0    63.027818  22.552586  39.609117  ... -28.658501  43.5123   Abnormal\n",
              "1    39.056951  10.060991  25.015378  ... -25.530607  16.1102   Abnormal\n",
              "2    68.832021  22.218482  50.092194  ... -29.031888  19.2221   Abnormal\n",
              "3    69.297008  24.652878  44.311238  ... -30.470246  18.8329   Abnormal\n",
              "4    49.712859   9.652075  28.317406  ... -16.378376  24.9171   Abnormal\n",
              "..         ...        ...        ...  ...        ...      ...        ...\n",
              "305  47.903565  13.616688  36.000000  ... -15.728927  11.5472     Normal\n",
              "306  53.936748  20.721496  29.220534  ...   6.013843  43.8693     Normal\n",
              "307  61.446597  22.694968  46.170347  ...   3.564463  18.4151     Normal\n",
              "308  45.252792   8.693157  41.583126  ...   5.767308  33.7192     Normal\n",
              "309  33.841641   5.073991  36.641233  ...   1.783007  40.6049     Normal\n",
              "\n",
              "[310 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Sd5cC-_A_1t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le=LabelEncoder()\n",
        "dataset['Class_att']=le.fit_transform(dataset['Class_att'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLbFm6dPBNbO",
        "colab_type": "code",
        "outputId": "922e2fc4-c81b-4036-cd6f-9c86be9ce85e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset['Class_att'].unique()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbtO3L9RAF99",
        "colab_type": "code",
        "outputId": "d503bec6-8424-4e61-c80f-e537502636c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        }
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "sns.heatmap(dataset.corr(),annot=True,fmt='.1f')\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LI9nxFuoaDUv",
        "colab_type": "code",
        "outputId": "1e7d4a8c-cf02-46c2-f670-83009af9bace",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Col1</th>\n",
              "      <th>Col2</th>\n",
              "      <th>Col3</th>\n",
              "      <th>Col4</th>\n",
              "      <th>Col5</th>\n",
              "      <th>Col6</th>\n",
              "      <th>Col7</th>\n",
              "      <th>Col8</th>\n",
              "      <th>Col9</th>\n",
              "      <th>Col10</th>\n",
              "      <th>Col11</th>\n",
              "      <th>Col12</th>\n",
              "      <th>Class_att</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.027818</td>\n",
              "      <td>22.552586</td>\n",
              "      <td>39.609117</td>\n",
              "      <td>40.475232</td>\n",
              "      <td>98.672917</td>\n",
              "      <td>-0.254400</td>\n",
              "      <td>0.744503</td>\n",
              "      <td>12.5661</td>\n",
              "      <td>14.5386</td>\n",
              "      <td>15.30468</td>\n",
              "      <td>-28.658501</td>\n",
              "      <td>43.5123</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39.056951</td>\n",
              "      <td>10.060991</td>\n",
              "      <td>25.015378</td>\n",
              "      <td>28.995960</td>\n",
              "      <td>114.405425</td>\n",
              "      <td>4.564259</td>\n",
              "      <td>0.415186</td>\n",
              "      <td>12.8874</td>\n",
              "      <td>17.5323</td>\n",
              "      <td>16.78486</td>\n",
              "      <td>-25.530607</td>\n",
              "      <td>16.1102</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68.832021</td>\n",
              "      <td>22.218482</td>\n",
              "      <td>50.092194</td>\n",
              "      <td>46.613539</td>\n",
              "      <td>105.985135</td>\n",
              "      <td>-3.530317</td>\n",
              "      <td>0.474889</td>\n",
              "      <td>26.8343</td>\n",
              "      <td>17.4861</td>\n",
              "      <td>16.65897</td>\n",
              "      <td>-29.031888</td>\n",
              "      <td>19.2221</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>69.297008</td>\n",
              "      <td>24.652878</td>\n",
              "      <td>44.311238</td>\n",
              "      <td>44.644130</td>\n",
              "      <td>101.868495</td>\n",
              "      <td>11.211523</td>\n",
              "      <td>0.369345</td>\n",
              "      <td>23.5603</td>\n",
              "      <td>12.7074</td>\n",
              "      <td>11.42447</td>\n",
              "      <td>-30.470246</td>\n",
              "      <td>18.8329</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49.712859</td>\n",
              "      <td>9.652075</td>\n",
              "      <td>28.317406</td>\n",
              "      <td>40.060784</td>\n",
              "      <td>108.168725</td>\n",
              "      <td>7.918501</td>\n",
              "      <td>0.543360</td>\n",
              "      <td>35.4940</td>\n",
              "      <td>15.9546</td>\n",
              "      <td>8.87237</td>\n",
              "      <td>-16.378376</td>\n",
              "      <td>24.9171</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>47.903565</td>\n",
              "      <td>13.616688</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>34.286877</td>\n",
              "      <td>117.449062</td>\n",
              "      <td>-4.245395</td>\n",
              "      <td>0.129744</td>\n",
              "      <td>7.8433</td>\n",
              "      <td>14.7484</td>\n",
              "      <td>8.51707</td>\n",
              "      <td>-15.728927</td>\n",
              "      <td>11.5472</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>53.936748</td>\n",
              "      <td>20.721496</td>\n",
              "      <td>29.220534</td>\n",
              "      <td>33.215251</td>\n",
              "      <td>114.365845</td>\n",
              "      <td>-0.421010</td>\n",
              "      <td>0.047913</td>\n",
              "      <td>19.1986</td>\n",
              "      <td>18.1972</td>\n",
              "      <td>7.08745</td>\n",
              "      <td>6.013843</td>\n",
              "      <td>43.8693</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>61.446597</td>\n",
              "      <td>22.694968</td>\n",
              "      <td>46.170347</td>\n",
              "      <td>38.751628</td>\n",
              "      <td>125.670725</td>\n",
              "      <td>-2.707880</td>\n",
              "      <td>0.081070</td>\n",
              "      <td>16.2059</td>\n",
              "      <td>13.5565</td>\n",
              "      <td>8.89572</td>\n",
              "      <td>3.564463</td>\n",
              "      <td>18.4151</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>45.252792</td>\n",
              "      <td>8.693157</td>\n",
              "      <td>41.583126</td>\n",
              "      <td>36.559635</td>\n",
              "      <td>118.545842</td>\n",
              "      <td>0.214750</td>\n",
              "      <td>0.159251</td>\n",
              "      <td>14.7334</td>\n",
              "      <td>16.0928</td>\n",
              "      <td>9.75922</td>\n",
              "      <td>5.767308</td>\n",
              "      <td>33.7192</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>33.841641</td>\n",
              "      <td>5.073991</td>\n",
              "      <td>36.641233</td>\n",
              "      <td>28.767649</td>\n",
              "      <td>123.945244</td>\n",
              "      <td>-0.199249</td>\n",
              "      <td>0.674504</td>\n",
              "      <td>19.3825</td>\n",
              "      <td>17.6963</td>\n",
              "      <td>13.72929</td>\n",
              "      <td>1.783007</td>\n",
              "      <td>40.6049</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>310 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Col1       Col2       Col3  ...      Col11    Col12  Class_att\n",
              "0    63.027818  22.552586  39.609117  ... -28.658501  43.5123          0\n",
              "1    39.056951  10.060991  25.015378  ... -25.530607  16.1102          0\n",
              "2    68.832021  22.218482  50.092194  ... -29.031888  19.2221          0\n",
              "3    69.297008  24.652878  44.311238  ... -30.470246  18.8329          0\n",
              "4    49.712859   9.652075  28.317406  ... -16.378376  24.9171          0\n",
              "..         ...        ...        ...  ...        ...      ...        ...\n",
              "305  47.903565  13.616688  36.000000  ... -15.728927  11.5472          1\n",
              "306  53.936748  20.721496  29.220534  ...   6.013843  43.8693          1\n",
              "307  61.446597  22.694968  46.170347  ...   3.564463  18.4151          1\n",
              "308  45.252792   8.693157  41.583126  ...   5.767308  33.7192          1\n",
              "309  33.841641   5.073991  36.641233  ...   1.783007  40.6049          1\n",
              "\n",
              "[310 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7r5hqtRQon6-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4362d002-8049-4608-9c26-0b495f11b98c"
      },
      "source": [
        "le.classes_"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Abnormal', 'Normal'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xKO955BinV3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=dataset.iloc[:,:-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dYRCAqULiyTq",
        "colab_type": "code",
        "outputId": "0b5925ed-79eb-4283-b5f5-862b78146e90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "features"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>39.056951</td>\n",
              "      <td>10.060991</td>\n",
              "      <td>25.015378</td>\n",
              "      <td>28.995960</td>\n",
              "      <td>114.405425</td>\n",
              "      <td>4.564259</td>\n",
              "      <td>0.415186</td>\n",
              "      <td>12.8874</td>\n",
              "      <td>17.5323</td>\n",
              "      <td>16.78486</td>\n",
              "      <td>-25.530607</td>\n",
              "      <td>16.1102</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>68.832021</td>\n",
              "      <td>22.218482</td>\n",
              "      <td>50.092194</td>\n",
              "      <td>46.613539</td>\n",
              "      <td>105.985135</td>\n",
              "      <td>-3.530317</td>\n",
              "      <td>0.474889</td>\n",
              "      <td>26.8343</td>\n",
              "      <td>17.4861</td>\n",
              "      <td>16.65897</td>\n",
              "      <td>-29.031888</td>\n",
              "      <td>19.2221</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>69.297008</td>\n",
              "      <td>24.652878</td>\n",
              "      <td>44.311238</td>\n",
              "      <td>44.644130</td>\n",
              "      <td>101.868495</td>\n",
              "      <td>11.211523</td>\n",
              "      <td>0.369345</td>\n",
              "      <td>23.5603</td>\n",
              "      <td>12.7074</td>\n",
              "      <td>11.42447</td>\n",
              "      <td>-30.470246</td>\n",
              "      <td>18.8329</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49.712859</td>\n",
              "      <td>9.652075</td>\n",
              "      <td>28.317406</td>\n",
              "      <td>40.060784</td>\n",
              "      <td>108.168725</td>\n",
              "      <td>7.918501</td>\n",
              "      <td>0.543360</td>\n",
              "      <td>35.4940</td>\n",
              "      <td>15.9546</td>\n",
              "      <td>8.87237</td>\n",
              "      <td>-16.378376</td>\n",
              "      <td>24.9171</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>305</th>\n",
              "      <td>47.903565</td>\n",
              "      <td>13.616688</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>34.286877</td>\n",
              "      <td>117.449062</td>\n",
              "      <td>-4.245395</td>\n",
              "      <td>0.129744</td>\n",
              "      <td>7.8433</td>\n",
              "      <td>14.7484</td>\n",
              "      <td>8.51707</td>\n",
              "      <td>-15.728927</td>\n",
              "      <td>11.5472</td>\n",
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              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>53.936748</td>\n",
              "      <td>20.721496</td>\n",
              "      <td>29.220534</td>\n",
              "      <td>33.215251</td>\n",
              "      <td>114.365845</td>\n",
              "      <td>-0.421010</td>\n",
              "      <td>0.047913</td>\n",
              "      <td>19.1986</td>\n",
              "      <td>18.1972</td>\n",
              "      <td>7.08745</td>\n",
              "      <td>6.013843</td>\n",
              "      <td>43.8693</td>\n",
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              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>61.446597</td>\n",
              "      <td>22.694968</td>\n",
              "      <td>46.170347</td>\n",
              "      <td>38.751628</td>\n",
              "      <td>125.670725</td>\n",
              "      <td>-2.707880</td>\n",
              "      <td>0.081070</td>\n",
              "      <td>16.2059</td>\n",
              "      <td>13.5565</td>\n",
              "      <td>8.89572</td>\n",
              "      <td>3.564463</td>\n",
              "      <td>18.4151</td>\n",
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              "      <th>308</th>\n",
              "      <td>45.252792</td>\n",
              "      <td>8.693157</td>\n",
              "      <td>41.583126</td>\n",
              "      <td>36.559635</td>\n",
              "      <td>118.545842</td>\n",
              "      <td>0.214750</td>\n",
              "      <td>0.159251</td>\n",
              "      <td>14.7334</td>\n",
              "      <td>16.0928</td>\n",
              "      <td>9.75922</td>\n",
              "      <td>5.767308</td>\n",
              "      <td>33.7192</td>\n",
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              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>33.841641</td>\n",
              "      <td>5.073991</td>\n",
              "      <td>36.641233</td>\n",
              "      <td>28.767649</td>\n",
              "      <td>123.945244</td>\n",
              "      <td>-0.199249</td>\n",
              "      <td>0.674504</td>\n",
              "      <td>19.3825</td>\n",
              "      <td>17.6963</td>\n",
              "      <td>13.72929</td>\n",
              "      <td>1.783007</td>\n",
              "      <td>40.6049</td>\n",
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              "<p>310 rows × 12 columns</p>\n",
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              "          Col1       Col2       Col3  ...     Col10      Col11    Col12\n",
              "0    63.027818  22.552586  39.609117  ...  15.30468 -28.658501  43.5123\n",
              "1    39.056951  10.060991  25.015378  ...  16.78486 -25.530607  16.1102\n",
              "2    68.832021  22.218482  50.092194  ...  16.65897 -29.031888  19.2221\n",
              "3    69.297008  24.652878  44.311238  ...  11.42447 -30.470246  18.8329\n",
              "4    49.712859   9.652075  28.317406  ...   8.87237 -16.378376  24.9171\n",
              "..         ...        ...        ...  ...       ...        ...      ...\n",
              "305  47.903565  13.616688  36.000000  ...   8.51707 -15.728927  11.5472\n",
              "306  53.936748  20.721496  29.220534  ...   7.08745   6.013843  43.8693\n",
              "307  61.446597  22.694968  46.170347  ...   8.89572   3.564463  18.4151\n",
              "308  45.252792   8.693157  41.583126  ...   9.75922   5.767308  33.7192\n",
              "309  33.841641   5.073991  36.641233  ...  13.72929   1.783007  40.6049\n",
              "\n",
              "[310 rows x 12 columns]"
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      "metadata": {
        "id": "XUcCM9eii0H5",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "labels=dataset.iloc[:,-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3JMfNUMi39m",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
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      },
      "source": [
        "labels"
      ],
      "execution_count": 21,
      "outputs": [
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          "data": {
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              "0      0\n",
              "1      0\n",
              "2      0\n",
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              "      ..\n",
              "305    1\n",
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DY5eUHcWi4w3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_train,feature_test,label_train,label_test=train_test_split(features,labels,test_size=0.2,random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aDrTU9y2jc5s",
        "colab_type": "text"
      },
      "source": [
        "##Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8u5Kex0jYUJ",
        "colab_type": "code",
        "outputId": "d4863745-a6fa-46c5-e886-068f6ec5400f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "model=LogisticRegression(max_iter=1000)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zeE5-7MKkJnL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9cLi14YBkQYj",
        "colab_type": "code",
        "outputId": "2b35a9a4-bcde-4e06-f005-4240b93a1915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8387096774193549"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOYOrZmbkUpS",
        "colab_type": "code",
        "outputId": "df468fa9-3d2e-4685-e9b6-3c5826bea437",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHxfJoqxkW3k",
        "colab_type": "code",
        "outputId": "70a337ee-ec3e-4127-fb6a-701003ef9e94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "label_test.tolist()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 1,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 1,\n",
              " 1,\n",
              " 1,\n",
              " 1,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 1,\n",
              " 1,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 1,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 1,\n",
              " 1,\n",
              " 0,\n",
              " 1,\n",
              " 0,\n",
              " 1,\n",
              " 0,\n",
              " 0,\n",
              " 1,\n",
              " 0,\n",
              " 0,\n",
              " 0,\n",
              " 1,\n",
              " 1,\n",
              " 0,\n",
              " 0,\n",
              " 1,\n",
              " 1,\n",
              " 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcgVZeISkZIQ",
        "colab_type": "code",
        "outputId": "f858404b-90e3-486d-baa0-f3a878435a10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.89      0.89        44\n",
            "           1       0.72      0.72      0.72        18\n",
            "\n",
            "    accuracy                           0.84        62\n",
            "   macro avg       0.80      0.80      0.80        62\n",
            "weighted avg       0.84      0.84      0.84        62\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55wQQmNCknS0",
        "colab_type": "code",
        "outputId": "cbfc861a-64ac-4efb-f043-58bc1514335a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[39  5]\n",
            " [ 5 13]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x8d6ZRRZpJHa",
        "colab_type": "text"
      },
      "source": [
        "##KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6srfjV_kxzZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "knn=KNeighborsClassifier()\n",
        "param={'n_neighbors':list(np.arange(1,20))}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oM0GK_Wdpt3c",
        "colab_type": "code",
        "outputId": "fa23e507-a940-465e-a281-50f416293c33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "model=GridSearchCV(knn,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
              "                                            metric='minkowski',\n",
              "                                            metric_params=None, n_jobs=None,\n",
              "                                            n_neighbors=5, p=2,\n",
              "                                            weights='uniform'),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
              "                                         13, 14, 15, 16, 17, 18, 19]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmoRtmHFp7FI",
        "colab_type": "code",
        "outputId": "43469cf9-c90b-435f-ae69-6a7280d2e014",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 10}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IyXV9Rvgp8xv",
        "colab_type": "code",
        "outputId": "d170c9f9-defa-4fd6-f0f3-d2d2fd3b4257",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "model=KNeighborsClassifier(n_neighbors=10)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=10, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M6d0GnSDsmD5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAXNaGyUsu7Z",
        "colab_type": "code",
        "outputId": "e52e7630-6538-4294-8a15-2596320b80f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8709677419354839"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "id2py1hQs1ou",
        "colab_type": "code",
        "outputId": "3555c96f-25a6-4631-e06f-1788005d791e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.98      0.91        44\n",
            "           1       0.92      0.61      0.73        18\n",
            "\n",
            "    accuracy                           0.87        62\n",
            "   macro avg       0.89      0.79      0.82        62\n",
            "weighted avg       0.88      0.87      0.86        62\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ye-C-MA9s7dI",
        "colab_type": "code",
        "outputId": "989a42f8-9ad7-4d18-d5ff-6c209603539d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[43  1]\n",
            " [ 7 11]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Fzmt-yctBAG",
        "colab_type": "code",
        "outputId": "d985f8e1-6da4-42e4-e84a-13cbdf43bc73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xn-1949tDXw",
        "colab_type": "code",
        "outputId": "b9f0c2cc-370e-4411-9b0a-e94e55fcc58e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "289    1\n",
              "9      0\n",
              "57     0\n",
              "60     0\n",
              "25     0\n",
              "      ..\n",
              "198    0\n",
              "195    0\n",
              "210    1\n",
              "224    1\n",
              "158    0\n",
              "Name: Class_att, Length: 62, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Uz7dXkytHxH",
        "colab_type": "text"
      },
      "source": [
        "##Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gol6IeX4tEvz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "dt=DecisionTreeClassifier()\n",
        "param={'max_depth':list(np.arange(1,20))}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JyWozlxctlZk",
        "colab_type": "code",
        "outputId": "c1e8c6a5-6f85-4baa-f3f4-d2d167b144c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "model=GridSearchCV(dt,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features=None,\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              presort='deprecated',\n",
              "                                              random_state=None,\n",
              "                                              splitter='best'),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
              "                                       13, 14, 15, 16, 17, 18, 19]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whcgKGF8tvJr",
        "colab_type": "code",
        "outputId": "1c3570c9-6710-4b02-e98b-3454443dfaf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WdJHHZTtysc",
        "colab_type": "code",
        "outputId": "ae15ca4f-704e-4f4c-8ab6-4af7a65f0509",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "model=DecisionTreeClassifier(max_depth=3)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=3, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qQJOjqct70G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ww_DBDApuCle",
        "colab_type": "code",
        "outputId": "ab37c78e-8701-456e-a0f6-e3f869d6d72d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8387096774193549"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKBr6M1huGvy",
        "colab_type": "code",
        "outputId": "668c33c8-9609-417f-c906-72a4753cbd52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.87      0.91      0.89        44\n",
            "           1       0.75      0.67      0.71        18\n",
            "\n",
            "    accuracy                           0.84        62\n",
            "   macro avg       0.81      0.79      0.80        62\n",
            "weighted avg       0.83      0.84      0.84        62\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rP4kZHvwuNvG",
        "colab_type": "code",
        "outputId": "4461753e-cad2-41ab-ed5f-b995b772fc26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[40  4]\n",
            " [ 6 12]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVfLWulOuSD-",
        "colab_type": "code",
        "outputId": "6e071810-b144-48d3-8f01-f4efb23db66f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
              "       1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFDDASxRuUmm",
        "colab_type": "code",
        "outputId": "6513db69-dd7e-4ee0-ed2e-31edde46cc0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "289    1\n",
              "9      0\n",
              "57     0\n",
              "60     0\n",
              "25     0\n",
              "      ..\n",
              "198    0\n",
              "195    0\n",
              "210    1\n",
              "224    1\n",
              "158    0\n",
              "Name: Class_att, Length: 62, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJGeBX82uaUF",
        "colab_type": "text"
      },
      "source": [
        "##SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGpZGfn3uVz9",
        "colab_type": "code",
        "outputId": "d45924e5-1357-4ebb-a817-d435b1e2711e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "model=SVC(kernel='linear')\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcgyR6rrutLZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Spj58q0uxxg",
        "colab_type": "code",
        "outputId": "ac04746e-c827-49ef-b343-3e1c8db6074b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8548387096774194"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKJ4acPsu2SU",
        "colab_type": "code",
        "outputId": "28be8379-7153-498b-d0ef-d6253398c025",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.89      0.90        44\n",
            "           1       0.74      0.78      0.76        18\n",
            "\n",
            "    accuracy                           0.85        62\n",
            "   macro avg       0.82      0.83      0.83        62\n",
            "weighted avg       0.86      0.85      0.86        62\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UF5EGLeOvDac",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "349a347a-5cb2-4bdf-c59e-d067fa9c7803"
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[39  5]\n",
            " [ 4 14]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohO7X7KsvKaG",
        "colab_type": "code",
        "outputId": "3bd11bcd-2486-4777-f13c-a6fd2ec73599",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
              "       1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9SZ3NqWvNLo",
        "colab_type": "code",
        "outputId": "50f4f976-bc1f-4b32-b59a-212dd5f6337c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "289    1\n",
              "9      0\n",
              "57     0\n",
              "60     0\n",
              "25     0\n",
              "      ..\n",
              "198    0\n",
              "195    0\n",
              "210    1\n",
              "224    1\n",
              "158    0\n",
              "Name: Class_att, Length: 62, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLASA4nsvS8p",
        "colab_type": "text"
      },
      "source": [
        "##RandomForest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fYUrRiEvPkc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "rfc=RandomForestClassifier()\n",
        "param={'n_estimators':[10,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,200],'max_depth':list(np.arange(1,20))}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MzTGnVSwEGe",
        "colab_type": "code",
        "outputId": "1f1159e7-3fa2-47c2-bd01-5cfdc9943434",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "model=GridSearchCV(rfc,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                              class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features='auto',\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              max_samples=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              n_estimators=100, n_jobs=None,\n",
              "                                              oob_score=False,\n",
              "                                              random_state=None, verbose=0,\n",
              "                                              warm_start=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
              "                                       13, 14, 15, 16, 17, 18, 19],\n",
              "                         'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90,\n",
              "                                          100, 110, 120, 130, 140, 150, 160,\n",
              "                                          170, 180, 190, 200]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AgU7374wMSa",
        "colab_type": "code",
        "outputId": "3ea56be2-2522-4849-8d26-07e78eb49c46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 11, 'n_estimators': 30}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPabpCPAxd-c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=model.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_R6qv2WrxiGY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4d96Smrxmwc",
        "colab_type": "code",
        "outputId": "05aa3a97-bb06-4e47-ead7-c6d6a8fb3397",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8387096774193549"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFI1iWo7xyKk",
        "colab_type": "code",
        "outputId": "39c55cae-0687-4dcb-870b-f4e76411c829",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.89      0.89        44\n",
            "           1       0.72      0.72      0.72        18\n",
            "\n",
            "    accuracy                           0.84        62\n",
            "   macro avg       0.80      0.80      0.80        62\n",
            "weighted avg       0.84      0.84      0.84        62\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUSkgQmVx5lu",
        "colab_type": "code",
        "outputId": "8aef3eec-14eb-49f7-b95b-f12a4a24a26f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[39  5]\n",
            " [ 5 13]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYOFClsBx9Yf",
        "colab_type": "code",
        "outputId": "bc9fe0e3-e208-4e8b-a6cf-ee76e2e3feec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
              "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vJb263CyTjN",
        "colab_type": "code",
        "outputId": "a385c7ef-e079-4977-8b17-94b5f128c9c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "289    1\n",
              "9      0\n",
              "57     0\n",
              "60     0\n",
              "25     0\n",
              "      ..\n",
              "198    0\n",
              "195    0\n",
              "210    1\n",
              "224    1\n",
              "158    0\n",
              "Name: Class_att, Length: 62, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6LWmsd6yU3x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}