kidney.ipynb

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    "colab": {
      "name": "kidney.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "EWq_qorUc31P",
        "6-htEnDldiaS"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AoPkeUKTD7r",
        "colab_type": "code",
        "outputId": "3579416e-7b1a-4059-a98c-b92bd509b04a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd /content/drive/My Drive/Dava with ML"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Dava with ML\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgeapriHT8Gd",
        "colab_type": "code",
        "outputId": "cd7af850-080a-4067-f887-88513034cc1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "!unzip chronic-kidney-disease.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  chronic-kidney-disease.zip\n",
            "replace new_model.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMSKsOBSUdhb",
        "colab_type": "code",
        "outputId": "2716018d-2070-44b1-ed3e-bbc49da2d869",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "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": 0,
      "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": "4xjLPkJvf_rg",
        "colab_type": "code",
        "outputId": "0b03c6b0-6383-4b86-861f-4aa2c52ddc4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "ls"
      ],
      "execution_count": 0,
      "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",
            "diabetes.csv                      liver.ipynb\n",
            "Diabeties.ipynb                   new_model.csv\n",
            "heart.csv                         pima-indians-diabetes-database.zip\n",
            "heart-disease-uci.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQTnME-0hYuX",
        "colab_type": "code",
        "outputId": "dcbca640-3ad1-4ce9-995d-047bf76d068c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "dataset=pd.read_csv('new_model.csv')\n",
        "dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Bp</th>\n",
              "      <th>Sg</th>\n",
              "      <th>Al</th>\n",
              "      <th>Su</th>\n",
              "      <th>Rbc</th>\n",
              "      <th>Bu</th>\n",
              "      <th>Sc</th>\n",
              "      <th>Sod</th>\n",
              "      <th>Pot</th>\n",
              "      <th>Hemo</th>\n",
              "      <th>Wbcc</th>\n",
              "      <th>Rbcc</th>\n",
              "      <th>Htn</th>\n",
              "      <th>Class</th>\n",
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              "      <th>0</th>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>4.63</td>\n",
              "      <td>15.4</td>\n",
              "      <td>7800.0</td>\n",
              "      <td>5.20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>137.53</td>\n",
              "      <td>4.63</td>\n",
              "      <td>11.3</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>4.71</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>137.53</td>\n",
              "      <td>4.63</td>\n",
              "      <td>9.6</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>4.71</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>4.0</td>\n",
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              "      <td>56.0</td>\n",
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              "      <td>111.00</td>\n",
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              "      <td>11.2</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>137.53</td>\n",
              "      <td>4.63</td>\n",
              "      <td>11.6</td>\n",
              "      <td>7300.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>...</td>\n",
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              "      <th>395</th>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>150.00</td>\n",
              "      <td>4.90</td>\n",
              "      <td>15.7</td>\n",
              "      <td>6700.0</td>\n",
              "      <td>4.90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <th>396</th>\n",
              "      <td>70.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>141.00</td>\n",
              "      <td>3.50</td>\n",
              "      <td>16.5</td>\n",
              "      <td>7800.0</td>\n",
              "      <td>6.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <th>397</th>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>137.00</td>\n",
              "      <td>4.40</td>\n",
              "      <td>15.8</td>\n",
              "      <td>6600.0</td>\n",
              "      <td>5.40</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <th>398</th>\n",
              "      <td>60.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>135.00</td>\n",
              "      <td>4.90</td>\n",
              "      <td>14.2</td>\n",
              "      <td>7200.0</td>\n",
              "      <td>5.90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>399</th>\n",
              "      <td>80.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>141.00</td>\n",
              "      <td>3.50</td>\n",
              "      <td>15.8</td>\n",
              "      <td>6800.0</td>\n",
              "      <td>6.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "</table>\n",
              "<p>400 rows × 14 columns</p>\n",
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            ],
            "text/plain": [
              "       Bp     Sg   Al   Su  Rbc    Bu  ...   Pot  Hemo    Wbcc  Rbcc  Htn  Class\n",
              "0    80.0  1.020  1.0  0.0  1.0  36.0  ...  4.63  15.4  7800.0  5.20  1.0      1\n",
              "1    50.0  1.020  4.0  0.0  1.0  18.0  ...  4.63  11.3  6000.0  4.71  0.0      1\n",
              "2    80.0  1.010  2.0  3.0  1.0  53.0  ...  4.63   9.6  7500.0  4.71  0.0      1\n",
              "3    70.0  1.005  4.0  0.0  1.0  56.0  ...  2.50  11.2  6700.0  3.90  1.0      1\n",
              "4    80.0  1.010  2.0  0.0  1.0  26.0  ...  4.63  11.6  7300.0  4.60  0.0      1\n",
              "..    ...    ...  ...  ...  ...   ...  ...   ...   ...     ...   ...  ...    ...\n",
              "395  80.0  1.020  0.0  0.0  1.0  49.0  ...  4.90  15.7  6700.0  4.90  0.0      0\n",
              "396  70.0  1.025  0.0  0.0  1.0  31.0  ...  3.50  16.5  7800.0  6.20  0.0      0\n",
              "397  80.0  1.020  0.0  0.0  1.0  26.0  ...  4.40  15.8  6600.0  5.40  0.0      0\n",
              "398  60.0  1.025  0.0  0.0  1.0  50.0  ...  4.90  14.2  7200.0  5.90  0.0      0\n",
              "399  80.0  1.025  0.0  0.0  1.0  18.0  ...  3.50  15.8  6800.0  6.10  0.0      0\n",
              "\n",
              "[400 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qpNiu0Vnw9m",
        "colab_type": "code",
        "outputId": "acf6b5a1-9f28-4116-911d-b49c8e1960ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "dataset.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Bp', 'Sg', 'Al', 'Su', 'Rbc', 'Bu', 'Sc', 'Sod', 'Pot', 'Hemo', 'Wbcc',\n",
              "       'Rbcc', 'Htn', 'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-R-L-wpuhcCG",
        "colab_type": "code",
        "outputId": "5d63ba1b-0459-4be2-bf54-3d087d1a4db5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        }
      },
      "source": [
        "dataset.corr()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Bp</th>\n",
              "      <th>Sg</th>\n",
              "      <th>Al</th>\n",
              "      <th>Su</th>\n",
              "      <th>Rbc</th>\n",
              "      <th>Bu</th>\n",
              "      <th>Sc</th>\n",
              "      <th>Sod</th>\n",
              "      <th>Pot</th>\n",
              "      <th>Hemo</th>\n",
              "      <th>Wbcc</th>\n",
              "      <th>Rbcc</th>\n",
              "      <th>Htn</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bp</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.164057</td>\n",
              "      <td>0.146060</td>\n",
              "      <td>0.190277</td>\n",
              "      <td>-0.151478</td>\n",
              "      <td>0.184173</td>\n",
              "      <td>0.144469</td>\n",
              "      <td>-0.103383</td>\n",
              "      <td>0.066791</td>\n",
              "      <td>-0.279441</td>\n",
              "      <td>0.025963</td>\n",
              "      <td>-0.220827</td>\n",
              "      <td>0.268003</td>\n",
              "      <td>0.290145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sg</th>\n",
              "      <td>-0.164057</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.460835</td>\n",
              "      <td>-0.292053</td>\n",
              "      <td>0.253894</td>\n",
              "      <td>-0.249263</td>\n",
              "      <td>-0.176141</td>\n",
              "      <td>0.217456</td>\n",
              "      <td>-0.063450</td>\n",
              "      <td>0.492103</td>\n",
              "      <td>-0.206880</td>\n",
              "      <td>0.443437</td>\n",
              "      <td>-0.318956</td>\n",
              "      <td>-0.659504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Al</th>\n",
              "      <td>0.146060</td>\n",
              "      <td>-0.460835</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.262564</td>\n",
              "      <td>-0.374484</td>\n",
              "      <td>0.405035</td>\n",
              "      <td>0.229396</td>\n",
              "      <td>-0.270709</td>\n",
              "      <td>0.114484</td>\n",
              "      <td>-0.548681</td>\n",
              "      <td>0.200664</td>\n",
              "      <td>-0.454131</td>\n",
              "      <td>0.478309</td>\n",
              "      <td>0.598389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Su</th>\n",
              "      <td>0.190277</td>\n",
              "      <td>-0.292053</td>\n",
              "      <td>0.262564</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.092940</td>\n",
              "      <td>0.126074</td>\n",
              "      <td>0.094568</td>\n",
              "      <td>-0.053448</td>\n",
              "      <td>0.180098</td>\n",
              "      <td>-0.156875</td>\n",
              "      <td>0.159033</td>\n",
              "      <td>-0.163825</td>\n",
              "      <td>0.253179</td>\n",
              "      <td>0.294555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rbc</th>\n",
              "      <td>-0.151478</td>\n",
              "      <td>0.253894</td>\n",
              "      <td>-0.374484</td>\n",
              "      <td>-0.092940</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.236270</td>\n",
              "      <td>-0.138391</td>\n",
              "      <td>0.140568</td>\n",
              "      <td>0.018164</td>\n",
              "      <td>0.280991</td>\n",
              "      <td>-0.002205</td>\n",
              "      <td>0.202298</td>\n",
              "      <td>-0.139342</td>\n",
              "      <td>-0.282642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bu</th>\n",
              "      <td>0.184173</td>\n",
              "      <td>-0.249263</td>\n",
              "      <td>0.405035</td>\n",
              "      <td>0.126074</td>\n",
              "      <td>-0.236270</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.581176</td>\n",
              "      <td>-0.307357</td>\n",
              "      <td>0.336954</td>\n",
              "      <td>-0.540699</td>\n",
              "      <td>0.041530</td>\n",
              "      <td>-0.465947</td>\n",
              "      <td>0.387503</td>\n",
              "      <td>0.371982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sc</th>\n",
              "      <td>0.144469</td>\n",
              "      <td>-0.176141</td>\n",
              "      <td>0.229396</td>\n",
              "      <td>0.094568</td>\n",
              "      <td>-0.138391</td>\n",
              "      <td>0.581176</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.624493</td>\n",
              "      <td>0.205361</td>\n",
              "      <td>-0.342053</td>\n",
              "      <td>-0.005420</td>\n",
              "      <td>-0.323056</td>\n",
              "      <td>0.273904</td>\n",
              "      <td>0.294076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sod</th>\n",
              "      <td>-0.103383</td>\n",
              "      <td>0.217456</td>\n",
              "      <td>-0.270709</td>\n",
              "      <td>-0.053448</td>\n",
              "      <td>0.140568</td>\n",
              "      <td>-0.307357</td>\n",
              "      <td>-0.624493</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.067414</td>\n",
              "      <td>0.333604</td>\n",
              "      <td>0.006334</td>\n",
              "      <td>0.316883</td>\n",
              "      <td>-0.306501</td>\n",
              "      <td>-0.342268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pot</th>\n",
              "      <td>0.066791</td>\n",
              "      <td>-0.063450</td>\n",
              "      <td>0.114484</td>\n",
              "      <td>0.180098</td>\n",
              "      <td>0.018164</td>\n",
              "      <td>0.336954</td>\n",
              "      <td>0.205361</td>\n",
              "      <td>0.067414</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.100612</td>\n",
              "      <td>-0.074057</td>\n",
              "      <td>-0.120418</td>\n",
              "      <td>0.057028</td>\n",
              "      <td>0.077063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hemo</th>\n",
              "      <td>-0.279441</td>\n",
              "      <td>0.492103</td>\n",
              "      <td>-0.548681</td>\n",
              "      <td>-0.156875</td>\n",
              "      <td>0.280991</td>\n",
              "      <td>-0.540699</td>\n",
              "      <td>-0.342053</td>\n",
              "      <td>0.333604</td>\n",
              "      <td>-0.100612</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.153806</td>\n",
              "      <td>0.681864</td>\n",
              "      <td>-0.576932</td>\n",
              "      <td>-0.729537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wbcc</th>\n",
              "      <td>0.025963</td>\n",
              "      <td>-0.206880</td>\n",
              "      <td>0.200664</td>\n",
              "      <td>0.159033</td>\n",
              "      <td>-0.002205</td>\n",
              "      <td>0.041530</td>\n",
              "      <td>-0.005420</td>\n",
              "      <td>0.006334</td>\n",
              "      <td>-0.074057</td>\n",
              "      <td>-0.153806</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.151380</td>\n",
              "      <td>0.123790</td>\n",
              "      <td>0.205266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rbcc</th>\n",
              "      <td>-0.220827</td>\n",
              "      <td>0.443437</td>\n",
              "      <td>-0.454131</td>\n",
              "      <td>-0.163825</td>\n",
              "      <td>0.202298</td>\n",
              "      <td>-0.465947</td>\n",
              "      <td>-0.323056</td>\n",
              "      <td>0.316883</td>\n",
              "      <td>-0.120418</td>\n",
              "      <td>0.681864</td>\n",
              "      <td>-0.151380</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.527051</td>\n",
              "      <td>-0.590248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Htn</th>\n",
              "      <td>0.268003</td>\n",
              "      <td>-0.318956</td>\n",
              "      <td>0.478309</td>\n",
              "      <td>0.253179</td>\n",
              "      <td>-0.139342</td>\n",
              "      <td>0.387503</td>\n",
              "      <td>0.273904</td>\n",
              "      <td>-0.306501</td>\n",
              "      <td>0.057028</td>\n",
              "      <td>-0.576932</td>\n",
              "      <td>0.123790</td>\n",
              "      <td>-0.527051</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.586340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Class</th>\n",
              "      <td>0.290145</td>\n",
              "      <td>-0.659504</td>\n",
              "      <td>0.598389</td>\n",
              "      <td>0.294555</td>\n",
              "      <td>-0.282642</td>\n",
              "      <td>0.371982</td>\n",
              "      <td>0.294076</td>\n",
              "      <td>-0.342268</td>\n",
              "      <td>0.077063</td>\n",
              "      <td>-0.729537</td>\n",
              "      <td>0.205266</td>\n",
              "      <td>-0.590248</td>\n",
              "      <td>0.586340</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Bp        Sg        Al  ...      Rbcc       Htn     Class\n",
              "Bp     1.000000 -0.164057  0.146060  ... -0.220827  0.268003  0.290145\n",
              "Sg    -0.164057  1.000000 -0.460835  ...  0.443437 -0.318956 -0.659504\n",
              "Al     0.146060 -0.460835  1.000000  ... -0.454131  0.478309  0.598389\n",
              "Su     0.190277 -0.292053  0.262564  ... -0.163825  0.253179  0.294555\n",
              "Rbc   -0.151478  0.253894 -0.374484  ...  0.202298 -0.139342 -0.282642\n",
              "Bu     0.184173 -0.249263  0.405035  ... -0.465947  0.387503  0.371982\n",
              "Sc     0.144469 -0.176141  0.229396  ... -0.323056  0.273904  0.294076\n",
              "Sod   -0.103383  0.217456 -0.270709  ...  0.316883 -0.306501 -0.342268\n",
              "Pot    0.066791 -0.063450  0.114484  ... -0.120418  0.057028  0.077063\n",
              "Hemo  -0.279441  0.492103 -0.548681  ...  0.681864 -0.576932 -0.729537\n",
              "Wbcc   0.025963 -0.206880  0.200664  ... -0.151380  0.123790  0.205266\n",
              "Rbcc  -0.220827  0.443437 -0.454131  ...  1.000000 -0.527051 -0.590248\n",
              "Htn    0.268003 -0.318956  0.478309  ... -0.527051  1.000000  0.586340\n",
              "Class  0.290145 -0.659504  0.598389  ... -0.590248  0.586340  1.000000\n",
              "\n",
              "[14 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R9yzXAaXqUHP",
        "colab_type": "code",
        "outputId": "826cd82f-700d-485c-c3f2-c2c8ae036985",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset.isnull().values.any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H35s7ACnq4mx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sns.set(style=\"ticks\", color_codes=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmXMZWgXketT",
        "colab_type": "code",
        "outputId": "73f62ef5-85ed-4d8b-910b-9c5c022aac98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        }
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "sns.heatmap(dataset.corr(),annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9J3pIdXbIlu",
        "colab_type": "code",
        "outputId": "42d273fc-96fe-4d27-abcb-a60ae2e74a78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        }
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": 0,
      "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>Bp</th>\n",
              "      <th>Sg</th>\n",
              "      <th>Al</th>\n",
              "      <th>Su</th>\n",
              "      <th>Rbc</th>\n",
              "      <th>Bu</th>\n",
              "      <th>Sc</th>\n",
              "      <th>Sod</th>\n",
              "      <th>Pot</th>\n",
              "      <th>Hemo</th>\n",
              "      <th>Wbcc</th>\n",
              "      <th>Rbcc</th>\n",
              "      <th>Htn</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.00000</td>\n",
              "      <td>400.00000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>76.455000</td>\n",
              "      <td>1.017712</td>\n",
              "      <td>1.015000</td>\n",
              "      <td>0.395000</td>\n",
              "      <td>0.882500</td>\n",
              "      <td>57.40550</td>\n",
              "      <td>3.07235</td>\n",
              "      <td>137.529025</td>\n",
              "      <td>4.627850</td>\n",
              "      <td>12.526900</td>\n",
              "      <td>8406.090000</td>\n",
              "      <td>4.708275</td>\n",
              "      <td>0.369350</td>\n",
              "      <td>0.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>13.476536</td>\n",
              "      <td>0.005434</td>\n",
              "      <td>1.272329</td>\n",
              "      <td>1.040038</td>\n",
              "      <td>0.322418</td>\n",
              "      <td>49.28597</td>\n",
              "      <td>5.61749</td>\n",
              "      <td>9.204273</td>\n",
              "      <td>2.819783</td>\n",
              "      <td>2.716171</td>\n",
              "      <td>2523.219976</td>\n",
              "      <td>0.840315</td>\n",
              "      <td>0.482023</td>\n",
              "      <td>0.484729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.50000</td>\n",
              "      <td>0.40000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>70.000000</td>\n",
              "      <td>1.015000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>27.00000</td>\n",
              "      <td>0.90000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>10.875000</td>\n",
              "      <td>6975.000000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>78.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>44.00000</td>\n",
              "      <td>1.40000</td>\n",
              "      <td>137.530000</td>\n",
              "      <td>4.630000</td>\n",
              "      <td>12.530000</td>\n",
              "      <td>8406.000000</td>\n",
              "      <td>4.710000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>61.75000</td>\n",
              "      <td>3.07000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>4.800000</td>\n",
              "      <td>14.625000</td>\n",
              "      <td>9400.000000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>180.000000</td>\n",
              "      <td>1.025000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>391.00000</td>\n",
              "      <td>76.00000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>17.800000</td>\n",
              "      <td>26400.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Bp          Sg          Al  ...        Rbcc         Htn       Class\n",
              "count  400.000000  400.000000  400.000000  ...  400.000000  400.000000  400.000000\n",
              "mean    76.455000    1.017712    1.015000  ...    4.708275    0.369350    0.625000\n",
              "std     13.476536    0.005434    1.272329  ...    0.840315    0.482023    0.484729\n",
              "min     50.000000    1.005000    0.000000  ...    2.100000    0.000000    0.000000\n",
              "25%     70.000000    1.015000    0.000000  ...    4.500000    0.000000    0.000000\n",
              "50%     78.000000    1.020000    1.000000  ...    4.710000    0.000000    1.000000\n",
              "75%     80.000000    1.020000    2.000000  ...    5.100000    1.000000    1.000000\n",
              "max    180.000000    1.025000    5.000000  ...    8.000000    1.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NDfpCtjSa6-4",
        "colab_type": "text"
      },
      "source": [
        "##Splitting of Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwiWGsqpq24O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=dataset.iloc[:,:-1]\n",
        "labels=dataset.iloc[:,[-1]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "l9QNAJWXlEXt",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "features"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Bp</th>\n",
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              "      <td>4.63</td>\n",
              "      <td>11.3</td>\n",
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              "      <td>4.71</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>3.0</td>\n",
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              "      <td>53.0</td>\n",
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              "      <td>4.63</td>\n",
              "      <td>9.6</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>4.71</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70.0</td>\n",
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              "      <td>4.0</td>\n",
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              "      <td>11.2</td>\n",
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              "      <td>3.90</td>\n",
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              "      <th>4</th>\n",
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              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1.4</td>\n",
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              "      <td>4.63</td>\n",
              "      <td>11.6</td>\n",
              "      <td>7300.0</td>\n",
              "      <td>4.60</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>0.5</td>\n",
              "      <td>150.00</td>\n",
              "      <td>4.90</td>\n",
              "      <td>15.7</td>\n",
              "      <td>6700.0</td>\n",
              "      <td>4.90</td>\n",
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              "      <th>396</th>\n",
              "      <td>70.0</td>\n",
              "      <td>1.025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>141.00</td>\n",
              "      <td>3.50</td>\n",
              "      <td>16.5</td>\n",
              "      <td>7800.0</td>\n",
              "      <td>6.20</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>397</th>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>4.40</td>\n",
              "      <td>15.8</td>\n",
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              "      <td>5.40</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>4.90</td>\n",
              "      <td>14.2</td>\n",
              "      <td>7200.0</td>\n",
              "      <td>5.90</td>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1.1</td>\n",
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              "      <td>15.8</td>\n",
              "      <td>6800.0</td>\n",
              "      <td>6.10</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Bp     Sg   Al   Su  Rbc    Bu  ...     Sod   Pot  Hemo    Wbcc  Rbcc  Htn\n",
              "0    80.0  1.020  1.0  0.0  1.0  36.0  ...  137.53  4.63  15.4  7800.0  5.20  1.0\n",
              "1    50.0  1.020  4.0  0.0  1.0  18.0  ...  137.53  4.63  11.3  6000.0  4.71  0.0\n",
              "2    80.0  1.010  2.0  3.0  1.0  53.0  ...  137.53  4.63   9.6  7500.0  4.71  0.0\n",
              "3    70.0  1.005  4.0  0.0  1.0  56.0  ...  111.00  2.50  11.2  6700.0  3.90  1.0\n",
              "4    80.0  1.010  2.0  0.0  1.0  26.0  ...  137.53  4.63  11.6  7300.0  4.60  0.0\n",
              "..    ...    ...  ...  ...  ...   ...  ...     ...   ...   ...     ...   ...  ...\n",
              "395  80.0  1.020  0.0  0.0  1.0  49.0  ...  150.00  4.90  15.7  6700.0  4.90  0.0\n",
              "396  70.0  1.025  0.0  0.0  1.0  31.0  ...  141.00  3.50  16.5  7800.0  6.20  0.0\n",
              "397  80.0  1.020  0.0  0.0  1.0  26.0  ...  137.00  4.40  15.8  6600.0  5.40  0.0\n",
              "398  60.0  1.025  0.0  0.0  1.0  50.0  ...  135.00  4.90  14.2  7200.0  5.90  0.0\n",
              "399  80.0  1.025  0.0  0.0  1.0  18.0  ...  141.00  3.50  15.8  6800.0  6.10  0.0\n",
              "\n",
              "[400 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2VBi3ATbQws",
        "colab_type": "code",
        "outputId": "528f8411-1fe3-45f6-c0a8-de55a1cbb9a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "labels"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>399</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>400 rows × 1 columns</p>\n",
              "</div>"
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            "text/plain": [
              "     Class\n",
              "0        1\n",
              "1        1\n",
              "2        1\n",
              "3        1\n",
              "4        1\n",
              "..     ...\n",
              "395      0\n",
              "396      0\n",
              "397      0\n",
              "398      0\n",
              "399      0\n",
              "\n",
              "[400 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgDt98osbRzC",
        "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": "0iLyWp1lbbZ4",
        "colab_type": "text"
      },
      "source": [
        "##Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWSB_FahbZTz",
        "colab_type": "code",
        "outputId": "2d264f0b-3011-450d-8fbb-73e409da2ba2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        }
      },
      "source": [
        "model=LogisticRegression(max_iter=1000)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmdpD5FPbhWl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4WoE2V2bnIl",
        "colab_type": "code",
        "outputId": "d663bb4e-cf94-4bf2-d504-9bff71bd37bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.975"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfUJR-b7bp5U",
        "colab_type": "code",
        "outputId": "d40d2688-5dbd-4f72-c260-0bb9ff49d21a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "       0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
              "       0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0])"
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      "source": [
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      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Class\n",
              "209      1\n",
              "280      0\n",
              "33       1\n",
              "210      1\n",
              "93       1\n",
              "..     ...\n",
              "246      1\n",
              "227      1\n",
              "369      0\n",
              "176      1\n",
              "289      0\n",
              "\n",
              "[80 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RalndosbziW",
        "colab_type": "code",
        "outputId": "13b6dec8-f94e-45bb-d9e8-f890b9bdd3f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.96      0.96        28\n",
            "           1       0.98      0.98      0.98        52\n",
            "\n",
            "    accuracy                           0.97        80\n",
            "   macro avg       0.97      0.97      0.97        80\n",
            "weighted avg       0.97      0.97      0.97        80\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_SIt83YVb6sL",
        "colab_type": "code",
        "outputId": "189233de-c9eb-443c-9079-c57eb09d5112",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[27  1]\n",
            " [ 1 51]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9s4Iiz4cAY_",
        "colab_type": "text"
      },
      "source": [
        "##KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lzY90J5b82i",
        "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": "_YmI3KnycFQr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=GridSearchCV(knn,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hpjHo2n5cIGw",
        "colab_type": "code",
        "outputId": "2bfedb4e-9842-4208-bccc-19402d966195",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQZZW99PcLdX",
        "colab_type": "code",
        "outputId": "ade9900c-4284-491c-ec1e-a7e2dc47aec2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "model=KNeighborsClassifier(n_neighbors=10)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "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": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEGF9W8ccPyK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25zX9TCucSO6",
        "colab_type": "code",
        "outputId": "d40c3d84-5f58-4886-e6d7-7b6fb1753170",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.725"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edisbD0-cUvk",
        "colab_type": "code",
        "outputId": "1ceba8bb-5c1f-436f-ce80-a1260e921b7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.57      0.86      0.69        28\n",
            "           1       0.89      0.65      0.76        52\n",
            "\n",
            "    accuracy                           0.73        80\n",
            "   macro avg       0.73      0.76      0.72        80\n",
            "weighted avg       0.78      0.72      0.73        80\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miLkHaeUclbe",
        "colab_type": "code",
        "outputId": "12e61e96-28b2-4910-9c1b-3629a4805706",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[24  4]\n",
            " [18 34]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jcf0blpOcwk2",
        "colab_type": "code",
        "outputId": "68b53b50-4f78-4189-9d6e-67c384fc9531",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1,\n",
              "       0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1,\n",
              "       1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
              "       1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-pof8OXcyTM",
        "colab_type": "code",
        "outputId": "035b1bb7-4522-4faf-d7d4-c22daa10298d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "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>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Class\n",
              "209      1\n",
              "280      0\n",
              "33       1\n",
              "210      1\n",
              "93       1\n",
              "..     ...\n",
              "246      1\n",
              "227      1\n",
              "369      0\n",
              "176      1\n",
              "289      0\n",
              "\n",
              "[80 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWq_qorUc31P",
        "colab_type": "text"
      },
      "source": [
        "##Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pydnj0rMc1Fo",
        "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": "89SFAxMLc82L",
        "colab_type": "code",
        "outputId": "a5cc27f5-eb25-466b-d7ac-280c99fd719b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "model=GridSearchCV(dt,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "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": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWbrJT2Zc_3h",
        "colab_type": "code",
        "outputId": "fa0c05d2-6a36-4d78-a5b9-0680a040105d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 7}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KdgeZO95dCsP",
        "colab_type": "code",
        "outputId": "c3cc6c54-c88b-449a-c441-3ba60041dd6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "model=DecisionTreeClassifier(max_depth=7)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=7, 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": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGkhZQg_dHY1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_TV9xq2dJ_N",
        "colab_type": "code",
        "outputId": "aafa4d5e-045c-43fc-bacb-8d0e8fabc10f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9875"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynD_lJv-dMGV",
        "colab_type": "code",
        "outputId": "9a853633-e660-426d-ee31-eac934731fca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      1.00      0.98        28\n",
            "           1       1.00      0.98      0.99        52\n",
            "\n",
            "    accuracy                           0.99        80\n",
            "   macro avg       0.98      0.99      0.99        80\n",
            "weighted avg       0.99      0.99      0.99        80\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9ibnmqFdQPS",
        "colab_type": "code",
        "outputId": "36e41d49-d8b3-4acd-d47e-5a0249cd710b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[28  0]\n",
            " [ 1 51]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPEq9QVQdTWu",
        "colab_type": "code",
        "outputId": "e59080ae-1369-4a44-c306-ce61d5c2f50e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "       0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
              "       0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dQA3V5Vxdf7_",
        "colab_type": "code",
        "outputId": "10ab1e77-c170-4482-93e2-683053e5adfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "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>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Class\n",
              "209      1\n",
              "280      0\n",
              "33       1\n",
              "210      1\n",
              "93       1\n",
              "..     ...\n",
              "246      1\n",
              "227      1\n",
              "369      0\n",
              "176      1\n",
              "289      0\n",
              "\n",
              "[80 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-htEnDldiaS",
        "colab_type": "text"
      },
      "source": [
        "##SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUvaArNHdhMj",
        "colab_type": "code",
        "outputId": "df3ed3f6-be7c-4dd1-bfa2-96d068a2ca31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "model=SVC(kernel='linear')\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "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": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qoVPUKE-dmul",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SHdwmFmdoya",
        "colab_type": "code",
        "outputId": "58bde7fd-cc32-40bf-a3a9-d7e1323bf54e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9625"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIim1bAEdq9p",
        "colab_type": "code",
        "outputId": "8d946edc-3c3b-4bda-d492-58f51fdd9d2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.93      0.95        28\n",
            "           1       0.96      0.98      0.97        52\n",
            "\n",
            "    accuracy                           0.96        80\n",
            "   macro avg       0.96      0.95      0.96        80\n",
            "weighted avg       0.96      0.96      0.96        80\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHI2_NbmeJms",
        "colab_type": "code",
        "outputId": "da33b94b-d9d0-492a-936a-90ba43795369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[26  2]\n",
            " [ 1 51]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTA92j4ieLpd",
        "colab_type": "code",
        "outputId": "65b1e7f3-aa58-4dba-c791-3186dc2af1ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "       0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
              "       0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAhd6m6yeNbq",
        "colab_type": "code",
        "outputId": "f1ebaa3e-9d75-4e98-d4e6-9c33f44a8c8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "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>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Class\n",
              "209      1\n",
              "280      0\n",
              "33       1\n",
              "210      1\n",
              "93       1\n",
              "..     ...\n",
              "246      1\n",
              "227      1\n",
              "369      0\n",
              "176      1\n",
              "289      0\n",
              "\n",
              "[80 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b5npxGYWeRla",
        "colab_type": "text"
      },
      "source": [
        "##Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ui3n2LrpeO4O",
        "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": "T-gqqN2lfBWp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=GridSearchCV(rfc,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WsJ3AyPfEyb",
        "colab_type": "code",
        "outputId": "97977832-739a-472d-a5bf-6e464c44fe77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 3, 'n_estimators': 30}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXUZ-E6ZKDw3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=model.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "anuLw44mKGp_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MX1E0GC2KJ5X",
        "colab_type": "code",
        "outputId": "6c25ba74-52f1-464c-d393-f2d508bd848d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XjoSpR5KMEk",
        "colab_type": "code",
        "outputId": "cd513391-f5a4-4a2c-ac5b-60156774c3fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        28\n",
            "           1       1.00      1.00      1.00        52\n",
            "\n",
            "    accuracy                           1.00        80\n",
            "   macro avg       1.00      1.00      1.00        80\n",
            "weighted avg       1.00      1.00      1.00        80\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "901eUh8VKQK1",
        "colab_type": "code",
        "outputId": "5ef90d19-7330-4333-c8a6-342a7985edb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[28  0]\n",
            " [ 0 52]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NbYrvSBKTOv",
        "colab_type": "code",
        "outputId": "ab0c3932-919a-479d-835f-f928a8323e40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "       0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,\n",
              "       0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2B8TRlz0KVP5",
        "colab_type": "code",
        "outputId": "189b2c31-d78e-4805-a0e2-35ef8fd3f6a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "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>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Class\n",
              "209      1\n",
              "280      0\n",
              "33       1\n",
              "210      1\n",
              "93       1\n",
              "..     ...\n",
              "246      1\n",
              "227      1\n",
              "369      0\n",
              "176      1\n",
              "289      0\n",
              "\n",
              "[80 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKAZ4bECKWgk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}