hepatities.ipynb

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    "colab": {
      "name": "hepatities.ipynb",
      "provenance": [],
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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dlyv4fITC0U",
        "colab_type": "code",
        "outputId": "0ef6b628-6270-4099-e10a-589d5e14a710",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd /content/drive/My Drive/Dava with ML"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Dava with ML\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mh-kgR6TT9WD",
        "colab_type": "code",
        "outputId": "4c0a0e76-26dc-4c56-eddf-d8cf3a8e93de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "!unzip hepatiits_dataset.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  hepatiits_dataset.zip\n",
            "  inflating: hepatitisdata.csv       \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYtLzjD_UU_7",
        "colab_type": "code",
        "colab": {}
      },
      "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-1qCI73gCjI",
        "colab_type": "code",
        "outputId": "c4b747e8-5069-4c60-b639-3c4cacd07bd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "ls"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "breast-cancer-wisconsin-data.zip  hepatities.ipynb\n",
            "Cancer.ipynb                      hepatitisdata.csv\n",
            "cardiovaskular_dataset.zip        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                   parkinson_dataset.zip\n",
            "heart.csv                         Parkinson.ipynb\n",
            "heart-disease-uci.zip             pd_speech_features.csv\n",
            "heart.ipynb                       pima-indians-diabetes-database.zip\n",
            "hepatiits_dataset.zip             Spine.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GGYSBvphgYu",
        "colab_type": "code",
        "outputId": "9cbade1b-1675-4f1f-f6c4-6bff70c7d2a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        }
      },
      "source": [
        "dataset=pd.read_csv('hepatitisdata.csv')\n",
        "dataset"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <th>class</th>\n",
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              "<p>154 rows × 21 columns</p>\n",
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            ],
            "text/plain": [
              "     Unnamed: 0  class  age  sex  ... sgot  albumin protime histology\n",
              "0             0      2   50    1  ...   42      3.5       ?         1\n",
              "1             1      2   78    1  ...   32        4       ?         1\n",
              "2             2      2   31    1  ...   52        4      80         1\n",
              "3             3      2   34    1  ...  200        4       ?         1\n",
              "4             4      2   34    1  ...   28        4      75         1\n",
              "..          ...    ...  ...  ...  ...  ...      ...     ...       ...\n",
              "149         149      1   46    1  ...  242      3.3      50         2\n",
              "150         150      2   44    1  ...  142      4.3       ?         2\n",
              "151         151      2   61    1  ...   20      4.1       ?         2\n",
              "152         152      2   53    2  ...   19      4.1      48         2\n",
              "153         153      1   43    1  ...   19      3.1      42         2\n",
              "\n",
              "[154 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qSG0McV2hkSn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset=dataset.drop(['Unnamed: 0'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8mnTe3F-RLT",
        "colab_type": "code",
        "outputId": "2d6832e9-d208-41ae-fde0-2f9eb8449d94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>200</td>\n",
              "      <td>4</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>95</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>75</td>\n",
              "      <td>1</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",
              "      <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>149</th>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7.6</td>\n",
              "      <td>?</td>\n",
              "      <td>242</td>\n",
              "      <td>3.3</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>126</td>\n",
              "      <td>142</td>\n",
              "      <td>4.3</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>2</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>75</td>\n",
              "      <td>20</td>\n",
              "      <td>4.1</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>81</td>\n",
              "      <td>19</td>\n",
              "      <td>4.1</td>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>100</td>\n",
              "      <td>19</td>\n",
              "      <td>3.1</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     class  age  sex steroid  ...  sgot albumin protime histology\n",
              "0        2   50    1       1  ...    42     3.5       ?         1\n",
              "1        2   78    1       2  ...    32       4       ?         1\n",
              "2        2   31    1       ?  ...    52       4      80         1\n",
              "3        2   34    1       2  ...   200       4       ?         1\n",
              "4        2   34    1       2  ...    28       4      75         1\n",
              "..     ...  ...  ...     ...  ...   ...     ...     ...       ...\n",
              "149      1   46    1       2  ...   242     3.3      50         2\n",
              "150      2   44    1       2  ...   142     4.3       ?         2\n",
              "151      2   61    1       1  ...    20     4.1       ?         2\n",
              "152      2   53    2       1  ...    19     4.1      48         2\n",
              "153      1   43    1       2  ...    19     3.1      42         2\n",
              "\n",
              "[154 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WfT0ynm-eyA",
        "colab_type": "code",
        "outputId": "9db85777-088b-40b4-c2a1-998415d3cd4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(x='class',data=dataset)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17cc6b04e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ErkjyFmBJt/ZV4L1JzgeeZxC0WBgFae+5AnhbVb0I+AvgQICqeivwZ8BKYFN3RPEPwKnAT4AvJDmpn5GlX2QUpOF9CTgzyWEA3emjuQ4Ctncf9fy6XYtJnl9VG6vqfcAssDLJbwH3VtXHgOuBF4/lL5CexNK+B5D2FVV1d5KLgK8keRy4Dbhvzi5/zuBbwWa73wd16x/unkgOsAH4BnA+8IYkPwceAv5qLH+E9CR8olmS1Hj6SJLUGAVJUmMUJEmNUZAkNUZBktQYBUlSYxQkSc3/AawUjmOmC2ndAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBKlySOEA7Hc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s=dataset.replace('?',0)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u_xSwOLYXeWM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "6f21ab8d-5a46-472f-94f7-50441cfd7952"
      },
      "source": [
        "s"
      ],
      "execution_count": 132,
      "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",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>steroid</th>\n",
              "      <th>antivirals</th>\n",
              "      <th>fatigue</th>\n",
              "      <th>malaise</th>\n",
              "      <th>anorexia</th>\n",
              "      <th>liver_big</th>\n",
              "      <th>liver_firm</th>\n",
              "      <th>spleen_palable</th>\n",
              "      <th>spiders</th>\n",
              "      <th>ascites</th>\n",
              "      <th>varices</th>\n",
              "      <th>bilirubin</th>\n",
              "      <th>alk_phosphate</th>\n",
              "      <th>sgot</th>\n",
              "      <th>albumin</th>\n",
              "      <th>protime</th>\n",
              "      <th>histology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>135</td>\n",
              "      <td>42</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>78</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>96</td>\n",
              "      <td>32</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>46</td>\n",
              "      <td>52</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>200</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>95</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>75</td>\n",
              "      <td>1</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",
              "      <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>149</th>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7.6</td>\n",
              "      <td>0</td>\n",
              "      <td>242</td>\n",
              "      <td>3.3</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>126</td>\n",
              "      <td>142</td>\n",
              "      <td>4.3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>2</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>75</td>\n",
              "      <td>20</td>\n",
              "      <td>4.1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>81</td>\n",
              "      <td>19</td>\n",
              "      <td>4.1</td>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>100</td>\n",
              "      <td>19</td>\n",
              "      <td>3.1</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     class  age  sex steroid  ...  sgot albumin protime histology\n",
              "0        2   50    1       1  ...    42     3.5       0         1\n",
              "1        2   78    1       2  ...    32       4       0         1\n",
              "2        2   31    1       0  ...    52       4      80         1\n",
              "3        2   34    1       2  ...   200       4       0         1\n",
              "4        2   34    1       2  ...    28       4      75         1\n",
              "..     ...  ...  ...     ...  ...   ...     ...     ...       ...\n",
              "149      1   46    1       2  ...   242     3.3      50         2\n",
              "150      2   44    1       2  ...   142     4.3       0         2\n",
              "151      2   61    1       1  ...    20     4.1       0         2\n",
              "152      2   53    2       1  ...    19     4.1      48         2\n",
              "153      1   43    1       2  ...    19     3.1      42         2\n",
              "\n",
              "[154 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLbFm6dPBNbO",
        "colab_type": "code",
        "outputId": "ac55c2d9-e736-43e5-802f-0b30223ec828",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset['class'].unique()"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "euSGiOusV4P3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "61edeec7-de8c-4011-b2d3-97f171a84600"
      },
      "source": [
        "s.corr()"
      ],
      "execution_count": 134,
      "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",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>antivirals</th>\n",
              "      <th>histology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.217434</td>\n",
              "      <td>0.168242</td>\n",
              "      <td>-0.131803</td>\n",
              "      <td>-0.336033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>-0.217434</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.026052</td>\n",
              "      <td>-0.010648</td>\n",
              "      <td>0.166388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0.168242</td>\n",
              "      <td>0.026052</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.100371</td>\n",
              "      <td>-0.123950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>antivirals</th>\n",
              "      <td>-0.131803</td>\n",
              "      <td>-0.010648</td>\n",
              "      <td>-0.100371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.212459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histology</th>\n",
              "      <td>-0.336033</td>\n",
              "      <td>0.166388</td>\n",
              "      <td>-0.123950</td>\n",
              "      <td>0.212459</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               class       age       sex  antivirals  histology\n",
              "class       1.000000 -0.217434  0.168242   -0.131803  -0.336033\n",
              "age        -0.217434  1.000000  0.026052   -0.010648   0.166388\n",
              "sex         0.168242  0.026052  1.000000   -0.100371  -0.123950\n",
              "antivirals -0.131803 -0.010648 -0.100371    1.000000   0.212459\n",
              "histology  -0.336033  0.166388 -0.123950    0.212459   1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GbtO3L9RAF99",
        "colab_type": "code",
        "outputId": "d9267854-8b5e-4a1f-d73f-cb45ef9c3fcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "plt.figure(figsize=(100,15))\n",
        "sns.heatmap(dataset.corr(),annot=True,fmt='.1f')\n",
        "plt.show()"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 7200x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iT56tZ_aYnwU",
        "colab_type": "code",
        "outputId": "5c858bad-4928-4925-b892-158b201a4065",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "dataset.columns"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['class', 'age', 'sex', 'steroid', 'antivirals', 'fatigue', 'malaise',\n",
              "       'anorexia', 'liver_big', 'liver_firm', 'spleen_palable', 'spiders',\n",
              "       'ascites', 'varices', 'bilirubin', 'alk_phosphate', 'sgot', 'albumin',\n",
              "       'protime', 'histology'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LI9nxFuoaDUv",
        "colab_type": "code",
        "outputId": "ba299b7c-6a59-480b-ad13-312ed8db3017",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": 137,
      "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",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>steroid</th>\n",
              "      <th>antivirals</th>\n",
              "      <th>fatigue</th>\n",
              "      <th>malaise</th>\n",
              "      <th>anorexia</th>\n",
              "      <th>liver_big</th>\n",
              "      <th>liver_firm</th>\n",
              "      <th>spleen_palable</th>\n",
              "      <th>spiders</th>\n",
              "      <th>ascites</th>\n",
              "      <th>varices</th>\n",
              "      <th>bilirubin</th>\n",
              "      <th>alk_phosphate</th>\n",
              "      <th>sgot</th>\n",
              "      <th>albumin</th>\n",
              "      <th>protime</th>\n",
              "      <th>histology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>135</td>\n",
              "      <td>42</td>\n",
              "      <td>3.5</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>78</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>96</td>\n",
              "      <td>32</td>\n",
              "      <td>4</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>46</td>\n",
              "      <td>52</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>200</td>\n",
              "      <td>4</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>95</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>75</td>\n",
              "      <td>1</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",
              "      <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>149</th>\n",
              "      <td>1</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7.6</td>\n",
              "      <td>?</td>\n",
              "      <td>242</td>\n",
              "      <td>3.3</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>126</td>\n",
              "      <td>142</td>\n",
              "      <td>4.3</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>2</td>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>75</td>\n",
              "      <td>20</td>\n",
              "      <td>4.1</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>2</td>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>81</td>\n",
              "      <td>19</td>\n",
              "      <td>4.1</td>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>1</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>100</td>\n",
              "      <td>19</td>\n",
              "      <td>3.1</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     class  age  sex steroid  ...  sgot albumin protime histology\n",
              "0        2   50    1       1  ...    42     3.5       ?         1\n",
              "1        2   78    1       2  ...    32       4       ?         1\n",
              "2        2   31    1       ?  ...    52       4      80         1\n",
              "3        2   34    1       2  ...   200       4       ?         1\n",
              "4        2   34    1       2  ...    28       4      75         1\n",
              "..     ...  ...  ...     ...  ...   ...     ...     ...       ...\n",
              "149      1   46    1       2  ...   242     3.3      50         2\n",
              "150      2   44    1       2  ...   142     4.3       ?         2\n",
              "151      2   61    1       1  ...    20     4.1       ?         2\n",
              "152      2   53    2       1  ...    19     4.1      48         2\n",
              "153      1   43    1       2  ...    19     3.1      42         2\n",
              "\n",
              "[154 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1KbbHg7aHDk",
        "colab_type": "code",
        "outputId": "4e11cbe2-745b-4186-cb4f-2e6cbc9df830",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        }
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "sns.heatmap(dataset.corr(),annot=True,fmt='.1f')\n",
        "plt.show()"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "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": "4795a1e1-0ee6-4c6b-e93c-566a5971e79f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "features"
      ],
      "execution_count": 140,
      "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>steroid</th>\n",
              "      <th>antivirals</th>\n",
              "      <th>fatigue</th>\n",
              "      <th>malaise</th>\n",
              "      <th>anorexia</th>\n",
              "      <th>liver_big</th>\n",
              "      <th>liver_firm</th>\n",
              "      <th>spleen_palable</th>\n",
              "      <th>spiders</th>\n",
              "      <th>ascites</th>\n",
              "      <th>varices</th>\n",
              "      <th>bilirubin</th>\n",
              "      <th>alk_phosphate</th>\n",
              "      <th>sgot</th>\n",
              "      <th>albumin</th>\n",
              "      <th>protime</th>\n",
              "      <th>histology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>135</td>\n",
              "      <td>42</td>\n",
              "      <td>3.5</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>78</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>96</td>\n",
              "      <td>32</td>\n",
              "      <td>4</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.7</td>\n",
              "      <td>46</td>\n",
              "      <td>52</td>\n",
              "      <td>4</td>\n",
              "      <td>80</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>?</td>\n",
              "      <td>200</td>\n",
              "      <td>4</td>\n",
              "      <td>?</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>95</td>\n",
              "      <td>28</td>\n",
              "      <td>4</td>\n",
              "      <td>75</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7.6</td>\n",
              "      <td>?</td>\n",
              "      <td>242</td>\n",
              "      <td>3.3</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.9</td>\n",
              "      <td>126</td>\n",
              "      <td>142</td>\n",
              "      <td>4.3</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>75</td>\n",
              "      <td>20</td>\n",
              "      <td>4.1</td>\n",
              "      <td>?</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>53</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>81</td>\n",
              "      <td>19</td>\n",
              "      <td>4.1</td>\n",
              "      <td>48</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>100</td>\n",
              "      <td>19</td>\n",
              "      <td>3.1</td>\n",
              "      <td>42</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex steroid  antivirals  ... sgot albumin protime histology\n",
              "0     50    1       1           2  ...   42     3.5       ?         1\n",
              "1     78    1       2           2  ...   32       4       ?         1\n",
              "2     31    1       ?           1  ...   52       4      80         1\n",
              "3     34    1       2           2  ...  200       4       ?         1\n",
              "4     34    1       2           2  ...   28       4      75         1\n",
              "..   ...  ...     ...         ...  ...  ...     ...     ...       ...\n",
              "149   46    1       2           2  ...  242     3.3      50         2\n",
              "150   44    1       2           2  ...  142     4.3       ?         2\n",
              "151   61    1       1           2  ...   20     4.1       ?         2\n",
              "152   53    2       1           2  ...   19     4.1      48         2\n",
              "153   43    1       2           2  ...   19     3.1      42         2\n",
              "\n",
              "[154 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUcCM9eii0H5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels=dataset.iloc[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3JMfNUMi39m",
        "colab_type": "code",
        "outputId": "367b47f6-768b-4908-c8ef-80fb1611f9b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "labels"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2\n",
              "1      2\n",
              "2      2\n",
              "3      2\n",
              "4      2\n",
              "      ..\n",
              "149    1\n",
              "150    2\n",
              "151    2\n",
              "152    2\n",
              "153    1\n",
              "Name: class, Length: 154, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrEYDN-3gZIm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "013cc802-793d-402b-8919-953f7dc5319b"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>antivirals</th>\n",
              "      <th>histology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>154.000000</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>154.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.792208</td>\n",
              "      <td>41.272727</td>\n",
              "      <td>1.097403</td>\n",
              "      <td>1.844156</td>\n",
              "      <td>1.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.407051</td>\n",
              "      <td>12.574106</td>\n",
              "      <td>0.297473</td>\n",
              "      <td>0.363891</td>\n",
              "      <td>0.499554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            class         age         sex  antivirals   histology\n",
              "count  154.000000  154.000000  154.000000  154.000000  154.000000\n",
              "mean     1.792208   41.272727    1.097403    1.844156    1.454545\n",
              "std      0.407051   12.574106    0.297473    0.363891    0.499554\n",
              "min      1.000000    7.000000    1.000000    1.000000    1.000000\n",
              "25%      2.000000   32.000000    1.000000    2.000000    1.000000\n",
              "50%      2.000000   39.000000    1.000000    2.000000    1.000000\n",
              "75%      2.000000   50.000000    1.000000    2.000000    2.000000\n",
              "max      2.000000   78.000000    2.000000    2.000000    2.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgHY324dgnLH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "101d0a02-7bd6-49d4-ba73-5b43968400d5"
      },
      "source": [
        "dataset.dtypes"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class              int64\n",
              "age                int64\n",
              "sex                int64\n",
              "steroid           object\n",
              "antivirals         int64\n",
              "fatigue           object\n",
              "malaise           object\n",
              "anorexia          object\n",
              "liver_big         object\n",
              "liver_firm        object\n",
              "spleen_palable    object\n",
              "spiders           object\n",
              "ascites           object\n",
              "varices           object\n",
              "bilirubin         object\n",
              "alk_phosphate     object\n",
              "sgot              object\n",
              "albumin           object\n",
              "protime           object\n",
              "histology          int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_yCtxT9zggKb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "150fa8fb-2785-4c40-831e-eede0c19bc75"
      },
      "source": [
        "dataset.isnull().sum()"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class             0\n",
              "age               0\n",
              "sex               0\n",
              "steroid           0\n",
              "antivirals        0\n",
              "fatigue           0\n",
              "malaise           0\n",
              "anorexia          0\n",
              "liver_big         0\n",
              "liver_firm        0\n",
              "spleen_palable    0\n",
              "spiders           0\n",
              "ascites           0\n",
              "varices           0\n",
              "bilirubin         0\n",
              "alk_phosphate     0\n",
              "sgot              0\n",
              "albumin           0\n",
              "protime           0\n",
              "histology         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YsWC_Dkhcy-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset=dataset.replace('?',0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eigVG3Wmg06M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset['bilirubin']=dataset['bilirubin'].astype('float32')\n",
        "dataset['steroid']=dataset['steroid'].astype('float32')\n",
        "dataset['protime']=dataset['protime'].astype('float32')\n",
        "dataset['albumin']=dataset['albumin'].astype('float32')\n",
        "dataset['sgot']=dataset['sgot'].astype('float32')\n",
        "dataset['fatigue']=dataset['fatigue'].astype('float32')\n",
        "dataset['malaise']=dataset['malaise'].astype('float32')\n",
        "dataset['anorexia']=dataset['anorexia'].astype('float32')\n",
        "dataset['liver_big']=dataset['liver_big'].astype('float32')\n",
        "dataset['liver_firm']=dataset['liver_firm'].astype('float32')\n",
        "dataset['spleen_palable']=dataset['spleen_palable'].astype('float32')\n",
        "dataset['spiders']=dataset['spiders'].astype('float32')\n",
        "dataset['ascites']=dataset['ascites'].astype('float32')\n",
        "dataset['varices']=dataset['varices'].astype('float32')\n",
        "dataset['alk_phosphate']=dataset['alk_phosphate'].astype('float32')\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cY4brqGSjCKm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "bdddf33b-65d1-4e31-dd03-8f1870fc7e30"
      },
      "source": [
        "dataset.dtypes"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class               int64\n",
              "age                 int64\n",
              "sex                 int64\n",
              "steroid           float32\n",
              "antivirals          int64\n",
              "fatigue           float32\n",
              "malaise           float32\n",
              "anorexia          float32\n",
              "liver_big         float32\n",
              "liver_firm        float32\n",
              "spleen_palable    float32\n",
              "spiders           float32\n",
              "ascites           float32\n",
              "varices           float32\n",
              "bilirubin         float32\n",
              "alk_phosphate     float32\n",
              "sgot              float32\n",
              "albumin           float32\n",
              "protime           float32\n",
              "histology           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2wH7inojGBe",
        "colab_type": "code",
        "colab": {
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          "height": 659
        },
        "outputId": "db5ff3a4-398a-4e7b-939c-8ce2582095d1"
      },
      "source": [
        "dataset.corr()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>class</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
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              "      <th>malaise</th>\n",
              "      <th>anorexia</th>\n",
              "      <th>liver_big</th>\n",
              "      <th>liver_firm</th>\n",
              "      <th>spleen_palable</th>\n",
              "      <th>spiders</th>\n",
              "      <th>ascites</th>\n",
              "      <th>varices</th>\n",
              "      <th>bilirubin</th>\n",
              "      <th>alk_phosphate</th>\n",
              "      <th>sgot</th>\n",
              "      <th>albumin</th>\n",
              "      <th>protime</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.217434</td>\n",
              "      <td>0.168242</td>\n",
              "      <td>0.124835</td>\n",
              "      <td>-0.131803</td>\n",
              "      <td>0.289698</td>\n",
              "      <td>0.314228</td>\n",
              "      <td>0.109207</td>\n",
              "      <td>0.106824</td>\n",
              "      <td>0.152417</td>\n",
              "      <td>0.179508</td>\n",
              "      <td>0.328650</td>\n",
              "      <td>0.331895</td>\n",
              "      <td>0.248646</td>\n",
              "      <td>-0.423997</td>\n",
              "      <td>-0.050816</td>\n",
              "      <td>-0.054383</td>\n",
              "      <td>0.295469</td>\n",
              "      <td>0.158651</td>\n",
              "      <td>-0.336033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>-0.217434</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.026052</td>\n",
              "      <td>-0.073752</td>\n",
              "      <td>-0.010648</td>\n",
              "      <td>-0.259006</td>\n",
              "      <td>-0.095283</td>\n",
              "      <td>0.103915</td>\n",
              "      <td>-0.134483</td>\n",
              "      <td>-0.096542</td>\n",
              "      <td>-0.124726</td>\n",
              "      <td>-0.203266</td>\n",
              "      <td>-0.123015</td>\n",
              "      <td>-0.112485</td>\n",
              "      <td>0.094122</td>\n",
              "      <td>0.045388</td>\n",
              "      <td>0.074070</td>\n",
              "      <td>-0.189235</td>\n",
              "      <td>-0.066151</td>\n",
              "      <td>0.166388</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0.168242</td>\n",
              "      <td>0.026052</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021353</td>\n",
              "      <td>-0.100371</td>\n",
              "      <td>-0.092964</td>\n",
              "      <td>-0.037475</td>\n",
              "      <td>0.015869</td>\n",
              "      <td>0.048725</td>\n",
              "      <td>-0.000454</td>\n",
              "      <td>-0.090850</td>\n",
              "      <td>-0.081743</td>\n",
              "      <td>0.042879</td>\n",
              "      <td>-0.012920</td>\n",
              "      <td>-0.034479</td>\n",
              "      <td>0.167846</td>\n",
              "      <td>0.005254</td>\n",
              "      <td>-0.028128</td>\n",
              "      <td>0.089103</td>\n",
              "      <td>-0.123950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>steroid</th>\n",
              "      <td>0.124835</td>\n",
              "      <td>-0.073752</td>\n",
              "      <td>-0.021353</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.104731</td>\n",
              "      <td>0.156180</td>\n",
              "      <td>0.238343</td>\n",
              "      <td>0.088312</td>\n",
              "      <td>0.153380</td>\n",
              "      <td>0.101146</td>\n",
              "      <td>0.074901</td>\n",
              "      <td>0.034440</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.027391</td>\n",
              "      <td>-0.007787</td>\n",
              "      <td>-0.042438</td>\n",
              "      <td>-0.025786</td>\n",
              "      <td>0.206630</td>\n",
              "      <td>0.026870</td>\n",
              "      <td>-0.076290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>antivirals</th>\n",
              "      <td>-0.131803</td>\n",
              "      <td>-0.010648</td>\n",
              "      <td>-0.100371</td>\n",
              "      <td>0.104731</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.069783</td>\n",
              "      <td>-0.029023</td>\n",
              "      <td>-0.054053</td>\n",
              "      <td>-0.057534</td>\n",
              "      <td>-0.022287</td>\n",
              "      <td>-0.184754</td>\n",
              "      <td>-0.178756</td>\n",
              "      <td>-0.177731</td>\n",
              "      <td>-0.168995</td>\n",
              "      <td>0.178970</td>\n",
              "      <td>0.041817</td>\n",
              "      <td>0.098257</td>\n",
              "      <td>-0.155348</td>\n",
              "      <td>-0.274729</td>\n",
              "      <td>0.212459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fatigue</th>\n",
              "      <td>0.289698</td>\n",
              "      <td>-0.259006</td>\n",
              "      <td>-0.092964</td>\n",
              "      <td>0.156180</td>\n",
              "      <td>-0.069783</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.615538</td>\n",
              "      <td>0.418346</td>\n",
              "      <td>0.181495</td>\n",
              "      <td>0.249264</td>\n",
              "      <td>0.223897</td>\n",
              "      <td>0.377534</td>\n",
              "      <td>0.287126</td>\n",
              "      <td>0.215261</td>\n",
              "      <td>-0.242794</td>\n",
              "      <td>-0.111075</td>\n",
              "      <td>-0.170336</td>\n",
              "      <td>0.196424</td>\n",
              "      <td>0.083309</td>\n",
              "      <td>-0.124294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>malaise</th>\n",
              "      <td>0.314228</td>\n",
              "      <td>-0.095283</td>\n",
              "      <td>-0.037475</td>\n",
              "      <td>0.238343</td>\n",
              "      <td>-0.029023</td>\n",
              "      <td>0.615538</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.630853</td>\n",
              "      <td>0.200376</td>\n",
              "      <td>0.194336</td>\n",
              "      <td>0.142988</td>\n",
              "      <td>0.374335</td>\n",
              "      <td>0.375023</td>\n",
              "      <td>0.265671</td>\n",
              "      <td>-0.268957</td>\n",
              "      <td>-0.115001</td>\n",
              "      <td>-0.174118</td>\n",
              "      <td>0.222917</td>\n",
              "      <td>0.072812</td>\n",
              "      <td>-0.112753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>anorexia</th>\n",
              "      <td>0.109207</td>\n",
              "      <td>0.103915</td>\n",
              "      <td>0.015869</td>\n",
              "      <td>0.088312</td>\n",
              "      <td>-0.054053</td>\n",
              "      <td>0.418346</td>\n",
              "      <td>0.630853</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.190325</td>\n",
              "      <td>0.142199</td>\n",
              "      <td>0.183622</td>\n",
              "      <td>0.370430</td>\n",
              "      <td>0.300567</td>\n",
              "      <td>0.287971</td>\n",
              "      <td>-0.243359</td>\n",
              "      <td>0.036048</td>\n",
              "      <td>-0.186226</td>\n",
              "      <td>0.132482</td>\n",
              "      <td>0.112102</td>\n",
              "      <td>-0.046855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liver_big</th>\n",
              "      <td>0.106824</td>\n",
              "      <td>-0.134483</td>\n",
              "      <td>0.048725</td>\n",
              "      <td>0.153380</td>\n",
              "      <td>-0.057534</td>\n",
              "      <td>0.181495</td>\n",
              "      <td>0.200376</td>\n",
              "      <td>0.190325</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.710371</td>\n",
              "      <td>0.345002</td>\n",
              "      <td>0.378395</td>\n",
              "      <td>0.344262</td>\n",
              "      <td>0.243075</td>\n",
              "      <td>-0.031457</td>\n",
              "      <td>0.018907</td>\n",
              "      <td>-0.032367</td>\n",
              "      <td>0.109654</td>\n",
              "      <td>0.168080</td>\n",
              "      <td>-0.112834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>liver_firm</th>\n",
              "      <td>0.152417</td>\n",
              "      <td>-0.096542</td>\n",
              "      <td>-0.000454</td>\n",
              "      <td>0.101146</td>\n",
              "      <td>-0.022287</td>\n",
              "      <td>0.249264</td>\n",
              "      <td>0.194336</td>\n",
              "      <td>0.142199</td>\n",
              "      <td>0.710371</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.321171</td>\n",
              "      <td>0.404799</td>\n",
              "      <td>0.308963</td>\n",
              "      <td>0.293777</td>\n",
              "      <td>-0.067848</td>\n",
              "      <td>-0.180342</td>\n",
              "      <td>-0.146431</td>\n",
              "      <td>0.057459</td>\n",
              "      <td>0.089304</td>\n",
              "      <td>-0.202660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spleen_palable</th>\n",
              "      <td>0.179508</td>\n",
              "      <td>-0.124726</td>\n",
              "      <td>-0.090850</td>\n",
              "      <td>0.074901</td>\n",
              "      <td>-0.184754</td>\n",
              "      <td>0.223897</td>\n",
              "      <td>0.142988</td>\n",
              "      <td>0.183622</td>\n",
              "      <td>0.345002</td>\n",
              "      <td>0.321171</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.514382</td>\n",
              "      <td>0.495017</td>\n",
              "      <td>0.574090</td>\n",
              "      <td>-0.177554</td>\n",
              "      <td>-0.160313</td>\n",
              "      <td>-0.114304</td>\n",
              "      <td>0.195131</td>\n",
              "      <td>0.128290</td>\n",
              "      <td>-0.098180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spiders</th>\n",
              "      <td>0.328650</td>\n",
              "      <td>-0.203266</td>\n",
              "      <td>-0.081743</td>\n",
              "      <td>0.034440</td>\n",
              "      <td>-0.178756</td>\n",
              "      <td>0.377534</td>\n",
              "      <td>0.374335</td>\n",
              "      <td>0.370430</td>\n",
              "      <td>0.378395</td>\n",
              "      <td>0.404799</td>\n",
              "      <td>0.514382</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.552960</td>\n",
              "      <td>0.608986</td>\n",
              "      <td>-0.240823</td>\n",
              "      <td>-0.136405</td>\n",
              "      <td>-0.177686</td>\n",
              "      <td>0.114287</td>\n",
              "      <td>0.209949</td>\n",
              "      <td>-0.242918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ascites</th>\n",
              "      <td>0.331895</td>\n",
              "      <td>-0.123015</td>\n",
              "      <td>0.042879</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.177731</td>\n",
              "      <td>0.287126</td>\n",
              "      <td>0.375023</td>\n",
              "      <td>0.300567</td>\n",
              "      <td>0.344262</td>\n",
              "      <td>0.308963</td>\n",
              "      <td>0.495017</td>\n",
              "      <td>0.552960</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.672463</td>\n",
              "      <td>-0.253914</td>\n",
              "      <td>-0.058941</td>\n",
              "      <td>-0.124025</td>\n",
              "      <td>0.143211</td>\n",
              "      <td>0.123842</td>\n",
              "      <td>-0.176216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>varices</th>\n",
              "      <td>0.248646</td>\n",
              "      <td>-0.112485</td>\n",
              "      <td>-0.012920</td>\n",
              "      <td>-0.027391</td>\n",
              "      <td>-0.168995</td>\n",
              "      <td>0.215261</td>\n",
              "      <td>0.265671</td>\n",
              "      <td>0.287971</td>\n",
              "      <td>0.243075</td>\n",
              "      <td>0.293777</td>\n",
              "      <td>0.574090</td>\n",
              "      <td>0.608986</td>\n",
              "      <td>0.672463</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.315683</td>\n",
              "      <td>-0.148601</td>\n",
              "      <td>-0.159235</td>\n",
              "      <td>0.124160</td>\n",
              "      <td>0.127491</td>\n",
              "      <td>-0.176958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bilirubin</th>\n",
              "      <td>-0.423997</td>\n",
              "      <td>0.094122</td>\n",
              "      <td>-0.034479</td>\n",
              "      <td>-0.007787</td>\n",
              "      <td>0.178970</td>\n",
              "      <td>-0.242794</td>\n",
              "      <td>-0.268957</td>\n",
              "      <td>-0.243359</td>\n",
              "      <td>-0.031457</td>\n",
              "      <td>-0.067848</td>\n",
              "      <td>-0.177554</td>\n",
              "      <td>-0.240823</td>\n",
              "      <td>-0.253914</td>\n",
              "      <td>-0.315683</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.074373</td>\n",
              "      <td>0.251978</td>\n",
              "      <td>-0.063987</td>\n",
              "      <td>-0.127687</td>\n",
              "      <td>0.260610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alk_phosphate</th>\n",
              "      <td>-0.050816</td>\n",
              "      <td>0.045388</td>\n",
              "      <td>0.167846</td>\n",
              "      <td>-0.042438</td>\n",
              "      <td>0.041817</td>\n",
              "      <td>-0.111075</td>\n",
              "      <td>-0.115001</td>\n",
              "      <td>0.036048</td>\n",
              "      <td>0.018907</td>\n",
              "      <td>-0.180342</td>\n",
              "      <td>-0.160313</td>\n",
              "      <td>-0.136405</td>\n",
              "      <td>-0.058941</td>\n",
              "      <td>-0.148601</td>\n",
              "      <td>0.074373</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.141403</td>\n",
              "      <td>0.103932</td>\n",
              "      <td>0.136324</td>\n",
              "      <td>0.127205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sgot</th>\n",
              "      <td>-0.054383</td>\n",
              "      <td>0.074070</td>\n",
              "      <td>0.005254</td>\n",
              "      <td>-0.025786</td>\n",
              "      <td>0.098257</td>\n",
              "      <td>-0.170336</td>\n",
              "      <td>-0.174118</td>\n",
              "      <td>-0.186226</td>\n",
              "      <td>-0.032367</td>\n",
              "      <td>-0.146431</td>\n",
              "      <td>-0.114304</td>\n",
              "      <td>-0.177686</td>\n",
              "      <td>-0.124025</td>\n",
              "      <td>-0.159235</td>\n",
              "      <td>0.251978</td>\n",
              "      <td>0.141403</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.077963</td>\n",
              "      <td>-0.043869</td>\n",
              "      <td>0.134354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>albumin</th>\n",
              "      <td>0.295469</td>\n",
              "      <td>-0.189235</td>\n",
              "      <td>-0.028128</td>\n",
              "      <td>0.206630</td>\n",
              "      <td>-0.155348</td>\n",
              "      <td>0.196424</td>\n",
              "      <td>0.222917</td>\n",
              "      <td>0.132482</td>\n",
              "      <td>0.109654</td>\n",
              "      <td>0.057459</td>\n",
              "      <td>0.195131</td>\n",
              "      <td>0.114287</td>\n",
              "      <td>0.143211</td>\n",
              "      <td>0.124160</td>\n",
              "      <td>-0.063987</td>\n",
              "      <td>0.103932</td>\n",
              "      <td>-0.077963</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.338930</td>\n",
              "      <td>-0.133265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>protime</th>\n",
              "      <td>0.158651</td>\n",
              "      <td>-0.066151</td>\n",
              "      <td>0.089103</td>\n",
              "      <td>0.026870</td>\n",
              "      <td>-0.274729</td>\n",
              "      <td>0.083309</td>\n",
              "      <td>0.072812</td>\n",
              "      <td>0.112102</td>\n",
              "      <td>0.168080</td>\n",
              "      <td>0.089304</td>\n",
              "      <td>0.128290</td>\n",
              "      <td>0.209949</td>\n",
              "      <td>0.123842</td>\n",
              "      <td>0.127491</td>\n",
              "      <td>-0.127687</td>\n",
              "      <td>0.136324</td>\n",
              "      <td>-0.043869</td>\n",
              "      <td>0.338930</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.160877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histology</th>\n",
              "      <td>-0.336033</td>\n",
              "      <td>0.166388</td>\n",
              "      <td>-0.123950</td>\n",
              "      <td>-0.076290</td>\n",
              "      <td>0.212459</td>\n",
              "      <td>-0.124294</td>\n",
              "      <td>-0.112753</td>\n",
              "      <td>-0.046855</td>\n",
              "      <td>-0.112834</td>\n",
              "      <td>-0.202660</td>\n",
              "      <td>-0.098180</td>\n",
              "      <td>-0.242918</td>\n",
              "      <td>-0.176216</td>\n",
              "      <td>-0.176958</td>\n",
              "      <td>0.260610</td>\n",
              "      <td>0.127205</td>\n",
              "      <td>0.134354</td>\n",
              "      <td>-0.133265</td>\n",
              "      <td>-0.160877</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   class       age       sex  ...   albumin   protime  histology\n",
              "class           1.000000 -0.217434  0.168242  ...  0.295469  0.158651  -0.336033\n",
              "age            -0.217434  1.000000  0.026052  ... -0.189235 -0.066151   0.166388\n",
              "sex             0.168242  0.026052  1.000000  ... -0.028128  0.089103  -0.123950\n",
              "steroid         0.124835 -0.073752 -0.021353  ...  0.206630  0.026870  -0.076290\n",
              "antivirals     -0.131803 -0.010648 -0.100371  ... -0.155348 -0.274729   0.212459\n",
              "fatigue         0.289698 -0.259006 -0.092964  ...  0.196424  0.083309  -0.124294\n",
              "malaise         0.314228 -0.095283 -0.037475  ...  0.222917  0.072812  -0.112753\n",
              "anorexia        0.109207  0.103915  0.015869  ...  0.132482  0.112102  -0.046855\n",
              "liver_big       0.106824 -0.134483  0.048725  ...  0.109654  0.168080  -0.112834\n",
              "liver_firm      0.152417 -0.096542 -0.000454  ...  0.057459  0.089304  -0.202660\n",
              "spleen_palable  0.179508 -0.124726 -0.090850  ...  0.195131  0.128290  -0.098180\n",
              "spiders         0.328650 -0.203266 -0.081743  ...  0.114287  0.209949  -0.242918\n",
              "ascites         0.331895 -0.123015  0.042879  ...  0.143211  0.123842  -0.176216\n",
              "varices         0.248646 -0.112485 -0.012920  ...  0.124160  0.127491  -0.176958\n",
              "bilirubin      -0.423997  0.094122 -0.034479  ... -0.063987 -0.127687   0.260610\n",
              "alk_phosphate  -0.050816  0.045388  0.167846  ...  0.103932  0.136324   0.127205\n",
              "sgot           -0.054383  0.074070  0.005254  ... -0.077963 -0.043869   0.134354\n",
              "albumin         0.295469 -0.189235 -0.028128  ...  1.000000  0.338930  -0.133265\n",
              "protime         0.158651 -0.066151  0.089103  ...  0.338930  1.000000  -0.160877\n",
              "histology      -0.336033  0.166388 -0.123950  ... -0.133265 -0.160877   1.000000\n",
              "\n",
              "[20 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRA2UiUvjNcr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "5e7dc1cc-7216-40b8-f890-9cab458baff3"
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "sns.heatmap(dataset.corr(),annot=True,fmt='.1f')\n",
        "plt.show()"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ewwxDHoo_my",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset=dataset.drop('liver_big',axis=1).drop('spleen_palable',axis=1).drop('spiders',axis=1).drop('varices',axis=1).drop('malaise',axis=1).drop('fatigue',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmbfeM0JscBg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "4eae5829-82ad-4512-bbe0-2673ad09c5a0"
      },
      "source": [
        "dataset"
      ],
      "execution_count": 152,
      "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",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>steroid</th>\n",
              "      <th>antivirals</th>\n",
              "      <th>anorexia</th>\n",
              "      <th>liver_firm</th>\n",
              "      <th>ascites</th>\n",
              "      <th>bilirubin</th>\n",
              "      <th>alk_phosphate</th>\n",
              "      <th>sgot</th>\n",
              "      <th>albumin</th>\n",
              "      <th>protime</th>\n",
              "      <th>histology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>42.0</td>\n",
              "      <td>3.5</td>\n",
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              "    <tr>\n",
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              "      <td>78</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>96.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>46.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>95.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
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              "      <th>149</th>\n",
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              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>7.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
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              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>126.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>4.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
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              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>75.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
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              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>81.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>48.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153</th>\n",
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              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>100.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3.1</td>\n",
              "      <td>42.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     class  age  sex  steroid  ...   sgot  albumin  protime  histology\n",
              "0        2   50    1      1.0  ...   42.0      3.5      0.0          1\n",
              "1        2   78    1      2.0  ...   32.0      4.0      0.0          1\n",
              "2        2   31    1      0.0  ...   52.0      4.0     80.0          1\n",
              "3        2   34    1      2.0  ...  200.0      4.0      0.0          1\n",
              "4        2   34    1      2.0  ...   28.0      4.0     75.0          1\n",
              "..     ...  ...  ...      ...  ...    ...      ...      ...        ...\n",
              "149      1   46    1      2.0  ...  242.0      3.3     50.0          2\n",
              "150      2   44    1      2.0  ...  142.0      4.3      0.0          2\n",
              "151      2   61    1      1.0  ...   20.0      4.1      0.0          2\n",
              "152      2   53    2      1.0  ...   19.0      4.1     48.0          2\n",
              "153      1   43    1      2.0  ...   19.0      3.1     42.0          2\n",
              "\n",
              "[154 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycjA50P6seiW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "2e576a76-c372-4fb7-f67f-9a005d425166"
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "sns.heatmap(dataset.corr(),annot=True,fmt='.1f')\n",
        "plt.show()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diKWZwLsjeXH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=dataset.iloc[:,1:]\n",
        "labels=dataset.iloc[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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": "code",
      "metadata": {
        "id": "v01CjRfvjajR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "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": "3919c0c6-2aa6-46b8-9ded-ed0f7d2e0700",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "model=LogisticRegression(max_iter=1000)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 156,
      "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": 156
        }
      ]
    },
    {
      "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": "d96cac41-6fdf-488d-acdc-30272e44b397",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7419354838709677"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOYOrZmbkUpS",
        "colab_type": "code",
        "outputId": "2ab1cf99-68b9-4593-d202-c6a6436d6697",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHxfJoqxkW3k",
        "colab_type": "code",
        "outputId": "ba50001b-a349-4889-d9f0-fba75e17f04d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15     2\n",
              "94     2\n",
              "152    2\n",
              "105    1\n",
              "109    2\n",
              "65     2\n",
              "18     2\n",
              "45     2\n",
              "36     2\n",
              "55     2\n",
              "85     1\n",
              "142    1\n",
              "19     2\n",
              "147    2\n",
              "86     1\n",
              "93     1\n",
              "119    1\n",
              "12     2\n",
              "9      2\n",
              "114    2\n",
              "29     1\n",
              "42     2\n",
              "135    2\n",
              "67     2\n",
              "136    2\n",
              "30     1\n",
              "96     2\n",
              "31     2\n",
              "78     2\n",
              "66     1\n",
              "140    1\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcgVZeISkZIQ",
        "colab_type": "code",
        "outputId": "40917b7f-55fa-4e05-f85c-0c0cc7cd630b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.67      0.40      0.50        10\n",
            "           2       0.76      0.90      0.83        21\n",
            "\n",
            "    accuracy                           0.74        31\n",
            "   macro avg       0.71      0.65      0.66        31\n",
            "weighted avg       0.73      0.74      0.72        31\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55wQQmNCknS0",
        "colab_type": "code",
        "outputId": "c6790fde-9c7a-4e9f-a98e-08cedc88156b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 4  6]\n",
            " [ 2 19]]\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": "23d5ae0e-8fa9-4c42-dcc0-7214880b65d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "model=GridSearchCV(knn,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 164,
      "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": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmoRtmHFp7FI",
        "colab_type": "code",
        "outputId": "83cad258-4b6f-400d-b2ef-c14e56b9bbc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 9}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IyXV9Rvgp8xv",
        "colab_type": "code",
        "outputId": "5121a3b0-4e82-49ca-cd6a-6cd66ac32545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "model=KNeighborsClassifier(n_neighbors=9)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 166,
      "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=9, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 166
        }
      ]
    },
    {
      "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": "a4d07f6a-fa5f-4b94-92bb-afd48da32d41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6774193548387096"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "id2py1hQs1ou",
        "colab_type": "code",
        "outputId": "741212bc-4ea5-4451-8c2a-fdeea05c8986",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 169,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.00      0.00      0.00        10\n",
            "           2       0.68      1.00      0.81        21\n",
            "\n",
            "    accuracy                           0.68        31\n",
            "   macro avg       0.34      0.50      0.40        31\n",
            "weighted avg       0.46      0.68      0.55        31\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ye-C-MA9s7dI",
        "colab_type": "code",
        "outputId": "b2a9fc31-6242-4c98-8699-25c4ea20d09c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0 10]\n",
            " [ 0 21]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Fzmt-yctBAG",
        "colab_type": "code",
        "outputId": "10d81065-702f-468a-f0cc-09c302028ed7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xn-1949tDXw",
        "colab_type": "code",
        "outputId": "3426039f-4885-4d0a-de14-a846aa152e44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15     2\n",
              "94     2\n",
              "152    2\n",
              "105    1\n",
              "109    2\n",
              "65     2\n",
              "18     2\n",
              "45     2\n",
              "36     2\n",
              "55     2\n",
              "85     1\n",
              "142    1\n",
              "19     2\n",
              "147    2\n",
              "86     1\n",
              "93     1\n",
              "119    1\n",
              "12     2\n",
              "9      2\n",
              "114    2\n",
              "29     1\n",
              "42     2\n",
              "135    2\n",
              "67     2\n",
              "136    2\n",
              "30     1\n",
              "96     2\n",
              "31     2\n",
              "78     2\n",
              "66     1\n",
              "140    1\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "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": "58da4724-c0e5-4ae3-ebd5-16906d5a80f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "model=GridSearchCV(dt,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 174,
      "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": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whcgKGF8tvJr",
        "colab_type": "code",
        "outputId": "adb96d14-66f0-471e-ec1e-8ca46217c8c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 4}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WdJHHZTtysc",
        "colab_type": "code",
        "outputId": "e113be89-c68f-4006-88fd-b2fb133496db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "model=DecisionTreeClassifier(max_depth=4)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=4, 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": 176
        }
      ]
    },
    {
      "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": "374bf2de-1b97-482c-9d1d-7c990704d9b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6451612903225806"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKBr6M1huGvy",
        "colab_type": "code",
        "outputId": "542ca538-703d-40ad-e2fc-cd4a86d84ebf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.43      0.30      0.35        10\n",
            "           2       0.71      0.81      0.76        21\n",
            "\n",
            "    accuracy                           0.65        31\n",
            "   macro avg       0.57      0.55      0.55        31\n",
            "weighted avg       0.62      0.65      0.63        31\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rP4kZHvwuNvG",
        "colab_type": "code",
        "outputId": "cb0b33d8-8151-496c-ab3f-b9574ba66c5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 3  7]\n",
            " [ 4 17]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVfLWulOuSD-",
        "colab_type": "code",
        "outputId": "d473a747-eba4-4842-90eb-06b4efdf1de5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2,\n",
              "       2, 2, 2, 2, 1, 2, 2, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFDDASxRuUmm",
        "colab_type": "code",
        "outputId": "5027ad5c-463e-4e82-cc93-6b87b77e01cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15     2\n",
              "94     2\n",
              "152    2\n",
              "105    1\n",
              "109    2\n",
              "65     2\n",
              "18     2\n",
              "45     2\n",
              "36     2\n",
              "55     2\n",
              "85     1\n",
              "142    1\n",
              "19     2\n",
              "147    2\n",
              "86     1\n",
              "93     1\n",
              "119    1\n",
              "12     2\n",
              "9      2\n",
              "114    2\n",
              "29     1\n",
              "42     2\n",
              "135    2\n",
              "67     2\n",
              "136    2\n",
              "30     1\n",
              "96     2\n",
              "31     2\n",
              "78     2\n",
              "66     1\n",
              "140    1\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJGeBX82uaUF",
        "colab_type": "text"
      },
      "source": [
        "##SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGpZGfn3uVz9",
        "colab_type": "code",
        "outputId": "474521a6-a884-4f93-a5c9-1352f4a69d00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "model=SVC(kernel='linear')\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 183,
      "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": 183
        }
      ]
    },
    {
      "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": "552aa1fa-be09-44e6-ecd8-b0dd574f1756",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7419354838709677"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oKJ4acPsu2SU",
        "colab_type": "code",
        "outputId": "b570abcd-47de-4716-8af2-daf10c6a37aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      0.20      0.33        10\n",
            "           2       0.72      1.00      0.84        21\n",
            "\n",
            "    accuracy                           0.74        31\n",
            "   macro avg       0.86      0.60      0.59        31\n",
            "weighted avg       0.81      0.74      0.68        31\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UF5EGLeOvDac",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "9dea2b4d-de89-4906-df2c-310beaeb0ef6"
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 2  8]\n",
            " [ 0 21]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohO7X7KsvKaG",
        "colab_type": "code",
        "outputId": "b234fe4a-25e8-4463-b907-cf9b6330d8ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9SZ3NqWvNLo",
        "colab_type": "code",
        "outputId": "677121a1-d024-4bc7-a7d8-d4996b6e563a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15     2\n",
              "94     2\n",
              "152    2\n",
              "105    1\n",
              "109    2\n",
              "65     2\n",
              "18     2\n",
              "45     2\n",
              "36     2\n",
              "55     2\n",
              "85     1\n",
              "142    1\n",
              "19     2\n",
              "147    2\n",
              "86     1\n",
              "93     1\n",
              "119    1\n",
              "12     2\n",
              "9      2\n",
              "114    2\n",
              "29     1\n",
              "42     2\n",
              "135    2\n",
              "67     2\n",
              "136    2\n",
              "30     1\n",
              "96     2\n",
              "31     2\n",
              "78     2\n",
              "66     1\n",
              "140    1\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "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": "371f9a63-85e2-4db7-cd16-e18b36f9dfdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "model=GridSearchCV(rfc,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 191,
      "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": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AgU7374wMSa",
        "colab_type": "code",
        "outputId": "2a503d4a-1e97-4e3b-a87d-81a9f3bed1ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 10, 'n_estimators': 40}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        }
      ]
    },
    {
      "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": "fe4ea3fa-f66c-441f-abfc-a577f7ecea2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6774193548387096"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFI1iWo7xyKk",
        "colab_type": "code",
        "outputId": "4c295550-f563-49e9-8cd9-0f1e45d16f6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 196,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.50      0.20      0.29        10\n",
            "           2       0.70      0.90      0.79        21\n",
            "\n",
            "    accuracy                           0.68        31\n",
            "   macro avg       0.60      0.55      0.54        31\n",
            "weighted avg       0.64      0.68      0.63        31\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUSkgQmVx5lu",
        "colab_type": "code",
        "outputId": "29951164-02aa-49d2-9099-05f5f65992b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 2  8]\n",
            " [ 2 19]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eYOFClsBx9Yf",
        "colab_type": "code",
        "outputId": "8493900d-9de0-44a4-a891-e96f081403ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vJb263CyTjN",
        "colab_type": "code",
        "outputId": "d26f5c44-6ad6-4073-f7fd-6fbba5dd849c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15     2\n",
              "94     2\n",
              "152    2\n",
              "105    1\n",
              "109    2\n",
              "65     2\n",
              "18     2\n",
              "45     2\n",
              "36     2\n",
              "55     2\n",
              "85     1\n",
              "142    1\n",
              "19     2\n",
              "147    2\n",
              "86     1\n",
              "93     1\n",
              "119    1\n",
              "12     2\n",
              "9      2\n",
              "114    2\n",
              "29     1\n",
              "42     2\n",
              "135    2\n",
              "67     2\n",
              "136    2\n",
              "30     1\n",
              "96     2\n",
              "31     2\n",
              "78     2\n",
              "66     1\n",
              "140    1\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g6LWmsd6yU3x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}