liver.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "liver.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "ghY_11iGUuF6",
        "_rHfFK-uXQxn",
        "oPT-_GimYirC",
        "jk2zVdmabo31"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCo-TtMITGEo",
        "colab_type": "code",
        "outputId": "524e21d5-d337-4208-d689-15099d61e445",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd /content/drive/My Drive/Dava with ML"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Dava with ML\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGbbIncyT6KN",
        "colab_type": "code",
        "outputId": "475fcbed-986a-4fbb-cc70-e748b871d272",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "!unzip indian-liver-patient-records.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  indian-liver-patient-records.zip\n",
            "  inflating: indian_liver_patient.csv  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQVtAiGKUmba",
        "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": "1pgJ31f9hJfP",
        "colab_type": "code",
        "outputId": "556ef135-9318-4067-d45c-c814f2ddf5ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "breast-cancer-wisconsin-data.zip  heart.ipynb\n",
            "Cancer.ipynb                      indian_liver_patient.csv\n",
            "chronic-kidney-disease.zip        indian-liver-patient-records.zip\n",
            "data.csv                          kidney.ipynb\n",
            "diabetes.csv                      liver.ipynb\n",
            "Diabeties.ipynb                   new_model.csv\n",
            "heart.csv                         pima-indians-diabetes-database.zip\n",
            "heart-disease-uci.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNp_ZJOtf-he",
        "colab_type": "code",
        "outputId": "a9643717-7058-4a16-b44a-7b585e850875",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "dataset=pd.read_csv('indian_liver_patient.csv')\n",
        "dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Dataset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>65</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>187</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>62</td>\n",
              "      <td>Male</td>\n",
              "      <td>10.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>699</td>\n",
              "      <td>64</td>\n",
              "      <td>100</td>\n",
              "      <td>7.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.74</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>Male</td>\n",
              "      <td>7.3</td>\n",
              "      <td>4.1</td>\n",
              "      <td>490</td>\n",
              "      <td>60</td>\n",
              "      <td>68</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>58</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>182</td>\n",
              "      <td>14</td>\n",
              "      <td>20</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>72</td>\n",
              "      <td>Male</td>\n",
              "      <td>3.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>195</td>\n",
              "      <td>27</td>\n",
              "      <td>59</td>\n",
              "      <td>7.3</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.40</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>60</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>500</td>\n",
              "      <td>20</td>\n",
              "      <td>34</td>\n",
              "      <td>5.9</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.37</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>579</th>\n",
              "      <td>40</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.1</td>\n",
              "      <td>98</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>580</th>\n",
              "      <td>52</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>245</td>\n",
              "      <td>48</td>\n",
              "      <td>49</td>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>581</th>\n",
              "      <td>31</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>184</td>\n",
              "      <td>29</td>\n",
              "      <td>32</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>582</th>\n",
              "      <td>38</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>216</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>7.3</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.50</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>583 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Age  Gender  Total_Bilirubin  ...  Albumin  Albumin_and_Globulin_Ratio  Dataset\n",
              "0     65  Female              0.7  ...      3.3                        0.90        1\n",
              "1     62    Male             10.9  ...      3.2                        0.74        1\n",
              "2     62    Male              7.3  ...      3.3                        0.89        1\n",
              "3     58    Male              1.0  ...      3.4                        1.00        1\n",
              "4     72    Male              3.9  ...      2.4                        0.40        1\n",
              "..   ...     ...              ...  ...      ...                         ...      ...\n",
              "578   60    Male              0.5  ...      1.6                        0.37        2\n",
              "579   40    Male              0.6  ...      3.2                        1.10        1\n",
              "580   52    Male              0.8  ...      3.2                        1.00        1\n",
              "581   31    Male              1.3  ...      3.4                        1.00        1\n",
              "582   38    Male              1.0  ...      4.4                        1.50        2\n",
              "\n",
              "[583 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vu8knyjWhWCg",
        "colab_type": "code",
        "outputId": "6c8ec8db-8d4e-42c1-ee80-df74ee8c5211",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset.isnull().values.any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5LBkNwIyswY7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset=dataset.dropna(axis = 0, how ='any') \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNOU_GqEs4W4",
        "colab_type": "code",
        "outputId": "7dfcb2e0-40ff-4083-ae88-1720539e33d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dataset.isnull().values.any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNvLmv8GLKLV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yR0M6NWoTHR_",
        "colab_type": "code",
        "outputId": "116fb113-a0ea-425c-ea0d-ba670a2103f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "le=LabelEncoder()\n",
        "dataset['Gender']=le.fit_transform(dataset['Gender'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lp1-eMjTeMjI",
        "colab_type": "code",
        "outputId": "3f23c02f-bbb1-452a-a14b-658d0d9ee19f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "le.classes_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Female', 'Male'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 217
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkv25NEps_YQ",
        "colab_type": "code",
        "outputId": "2b807a96-b589-4484-87fd-de8fbbd6144c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        }
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "sns.heatmap(dataset.corr(),annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqLgCyEkUMY7",
        "colab_type": "code",
        "outputId": "4acb329f-80da-475a-c0e1-37ef793cb34e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "dataset.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',\n",
              "       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',\n",
              "       'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',\n",
              "       'Albumin_and_Globulin_Ratio', 'Dataset'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_eKrkuJDiXu",
        "colab_type": "code",
        "outputId": "bc97a026-097a-4c82-e4f0-5dd02abb8f98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "'''dataset=dataset.drop('Albumin',axis=1)'''\n",
        "dataset=dataset.drop('Age',axis=1)\n",
        "dataset=dataset.drop('Gender',axis=1)\n",
        "'''\n",
        "dataset=dataset.drop('Aspartate_Aminotransferase',axis=1)\n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\ndataset=dataset.drop('Aspartate_Aminotransferase',axis=1)\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKmS-O_OUeUt",
        "colab_type": "code",
        "outputId": "0403a4be-fd6c-4b33-f521-8365935fbba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "sns.heatmap(dataset.corr(),annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghY_11iGUuF6",
        "colab_type": "text"
      },
      "source": [
        "##Dataset spliting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3BJzI2TUiHp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=dataset.iloc[:,:-1]\n",
        "labels=dataset.iloc[:,[-1]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kSJtcyNKVBb_",
        "colab_type": "code",
        "outputId": "4e859ef8-cc64-47db-e9c2-2f5a7a94f7b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "features"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>187</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>699</td>\n",
              "      <td>64</td>\n",
              "      <td>100</td>\n",
              "      <td>7.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.3</td>\n",
              "      <td>4.1</td>\n",
              "      <td>490</td>\n",
              "      <td>60</td>\n",
              "      <td>68</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>182</td>\n",
              "      <td>14</td>\n",
              "      <td>20</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>195</td>\n",
              "      <td>27</td>\n",
              "      <td>59</td>\n",
              "      <td>7.3</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.40</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.1</td>\n",
              "      <td>500</td>\n",
              "      <td>20</td>\n",
              "      <td>34</td>\n",
              "      <td>5.9</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>579</th>\n",
              "      <td>0.6</td>\n",
              "      <td>0.1</td>\n",
              "      <td>98</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>580</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>245</td>\n",
              "      <td>48</td>\n",
              "      <td>49</td>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>581</th>\n",
              "      <td>1.3</td>\n",
              "      <td>0.5</td>\n",
              "      <td>184</td>\n",
              "      <td>29</td>\n",
              "      <td>32</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>582</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>216</td>\n",
              "      <td>21</td>\n",
              "      <td>24</td>\n",
              "      <td>7.3</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>579 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Total_Bilirubin  Direct_Bilirubin  ...  Albumin  Albumin_and_Globulin_Ratio\n",
              "0                0.7               0.1  ...      3.3                        0.90\n",
              "1               10.9               5.5  ...      3.2                        0.74\n",
              "2                7.3               4.1  ...      3.3                        0.89\n",
              "3                1.0               0.4  ...      3.4                        1.00\n",
              "4                3.9               2.0  ...      2.4                        0.40\n",
              "..               ...               ...  ...      ...                         ...\n",
              "578              0.5               0.1  ...      1.6                        0.37\n",
              "579              0.6               0.1  ...      3.2                        1.10\n",
              "580              0.8               0.2  ...      3.2                        1.00\n",
              "581              1.3               0.5  ...      3.4                        1.00\n",
              "582              1.0               0.3  ...      4.4                        1.50\n",
              "\n",
              "[579 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rki_Zh_ZVCzl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_train,feature_test,label_train,label_test=train_test_split(features,labels,test_size=0.3,random_state=7)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "voZ6gPqjVRO5",
        "colab_type": "code",
        "outputId": "848f63c0-2127-4e18-bc6c-38e0afd2e186",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        }
      },
      "source": [
        "feature_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>434</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>194</td>\n",
              "      <td>32</td>\n",
              "      <td>36</td>\n",
              "      <td>7.5</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>245</td>\n",
              "      <td>22</td>\n",
              "      <td>24</td>\n",
              "      <td>7.1</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>198</td>\n",
              "      <td>42</td>\n",
              "      <td>30</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>0.9</td>\n",
              "      <td>0.2</td>\n",
              "      <td>170</td>\n",
              "      <td>33</td>\n",
              "      <td>66</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.2</td>\n",
              "      <td>185</td>\n",
              "      <td>196</td>\n",
              "      <td>401</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.50</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>2.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>160</td>\n",
              "      <td>82</td>\n",
              "      <td>127</td>\n",
              "      <td>5.5</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506</th>\n",
              "      <td>17.7</td>\n",
              "      <td>8.8</td>\n",
              "      <td>239</td>\n",
              "      <td>43</td>\n",
              "      <td>185</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>198</td>\n",
              "      <td>29</td>\n",
              "      <td>19</td>\n",
              "      <td>6.6</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>2.0</td>\n",
              "      <td>1.1</td>\n",
              "      <td>664</td>\n",
              "      <td>52</td>\n",
              "      <td>104</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>198</td>\n",
              "      <td>43</td>\n",
              "      <td>31</td>\n",
              "      <td>7.3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>405 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Total_Bilirubin  Direct_Bilirubin  ...  Albumin  Albumin_and_Globulin_Ratio\n",
              "434              0.7               0.2  ...      3.6                        0.92\n",
              "292              0.6               0.2  ...      3.4                        0.90\n",
              "317              0.7               0.2  ...      3.4                        1.00\n",
              "427              0.9               0.2  ...      3.0                        0.75\n",
              "494              0.7               0.2  ...      3.9                        1.50\n",
              "..               ...               ...  ...      ...                         ...\n",
              "212              2.7               1.0  ...      3.1                        1.20\n",
              "506             17.7               8.8  ...      2.4                        0.70\n",
              "541              0.8               0.2  ...      3.0                        0.80\n",
              "196              2.0               1.1  ...      2.1                        0.53\n",
              "175              0.8               0.2  ...      4.0                        1.20\n",
              "\n",
              "[405 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 292
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EffKLe3GXKAI",
        "colab_type": "code",
        "outputId": "20a26c13-6437-4eba-ad9b-c3e1bb08d219",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>434</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>317</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>405 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Dataset\n",
              "434        2\n",
              "292        1\n",
              "317        1\n",
              "427        1\n",
              "494        1\n",
              "..       ...\n",
              "212        2\n",
              "506        1\n",
              "541        2\n",
              "196        1\n",
              "175        1\n",
              "\n",
              "[405 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 293
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_rHfFK-uXQxn",
        "colab_type": "text"
      },
      "source": [
        "##Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7HbgZ1AXOdd",
        "colab_type": "code",
        "outputId": "a57ae903-bad8-4440-8388-c1c5eb762aef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        }
      },
      "source": [
        "model=LogisticRegression(max_iter=1000)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=1000,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7jQMQ_3XVnt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LUHnMNgXXsx",
        "colab_type": "code",
        "outputId": "0981217e-eac8-456f-da09-834a93cab5b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7816091954022989"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 296
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aelqf_4PXZvW",
        "colab_type": "code",
        "outputId": "c181630e-b3dd-4094-9d04-058579a4f98a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1,\n",
              "       1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1,\n",
              "       1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 297
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_8uuDEoXc1c",
        "colab_type": "code",
        "outputId": "1dfff1c3-1047-4c1a-bf55-59d2af921799",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1,\n",
              "       1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1,\n",
              "       1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Slm0tERzXmAf",
        "colab_type": "code",
        "outputId": "45127a7e-8537-488d-c167-38ea2950c111",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>174 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Dataset\n",
              "512        2\n",
              "443        1\n",
              "237        1\n",
              "502        1\n",
              "461        1\n",
              "..       ...\n",
              "308        1\n",
              "546        1\n",
              "20         1\n",
              "113        1\n",
              "0          1\n",
              "\n",
              "[174 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BoRRE1wXndG",
        "colab_type": "code",
        "outputId": "61e68227-5fd6-4acf-8bd2-a72816ca2c23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.80      0.95      0.87       130\n",
            "           2       0.65      0.30      0.41        44\n",
            "\n",
            "    accuracy                           0.78       174\n",
            "   macro avg       0.72      0.62      0.64       174\n",
            "weighted avg       0.76      0.78      0.75       174\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmQXav9gX1cn",
        "colab_type": "code",
        "outputId": "b24e55e6-ea3d-4a6b-f58a-56573a1adf68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[123   7]\n",
            " [ 31  13]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7g6HcrKvX8z3",
        "colab_type": "text"
      },
      "source": [
        "#KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FdGntpU8X3F8",
        "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": "ui7zwcJAYBD9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=GridSearchCV(knn,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oja4nbDrYDvm",
        "colab_type": "code",
        "outputId": "7f181c0f-115b-4c3f-e593-470f38e654fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'n_neighbors': 7}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZO3--C6OYL1v",
        "colab_type": "code",
        "outputId": "2b1d0c97-8c3e-4531-eef1-8da0a47d98a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "model=KNeighborsClassifier(n_neighbors=7)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XK6YeYBWYQrs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0I_kU0ZYTq5",
        "colab_type": "code",
        "outputId": "22c9ab28-760f-4a19-a028-a72c6464febd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6609195402298851"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCgxJPWFYVsK",
        "colab_type": "code",
        "outputId": "ab79bcdf-7b16-4f0b-bedb-2d99fc3f78dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.77      0.78      0.77       130\n",
            "           2       0.33      0.32      0.32        44\n",
            "\n",
            "    accuracy                           0.66       174\n",
            "   macro avg       0.55      0.55      0.55       174\n",
            "weighted avg       0.66      0.66      0.66       174\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwiOxxnRYYbQ",
        "colab_type": "code",
        "outputId": "53c9b2e9-dbfa-44b6-86c4-af4449cd75b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[101  29]\n",
            " [ 30  14]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvaF6DpmYauk",
        "colab_type": "code",
        "outputId": "b1ed3db2-1cac-46d7-d775-320e2b4cda26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1,\n",
              "       1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 1, 2, 1,\n",
              "       1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1,\n",
              "       2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2,\n",
              "       1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1,\n",
              "       1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 310
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQsERstNYdZm",
        "colab_type": "code",
        "outputId": "f69b803f-b1b3-4cc9-fcb2-99fabfc88a00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>174 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Dataset\n",
              "512        2\n",
              "443        1\n",
              "237        1\n",
              "502        1\n",
              "461        1\n",
              "..       ...\n",
              "308        1\n",
              "546        1\n",
              "20         1\n",
              "113        1\n",
              "0          1\n",
              "\n",
              "[174 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 311
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPT-_GimYirC",
        "colab_type": "text"
      },
      "source": [
        "##Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZnxRnFPWYfNW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "dt=DecisionTreeClassifier()\n",
        "param={'max_depth':list(np.arange(1,20)),'criterion':['gini','entropy']}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKbXlyvNYnW1",
        "colab_type": "code",
        "outputId": "f688f7bb-cb06-4257-e51d-28020956769e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "model=GridSearchCV(dt,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features=None,\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              presort='deprecated',\n",
              "                                              random_state=None,\n",
              "                                              splitter='best'),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         '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": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38FUcemUYpjM",
        "colab_type": "code",
        "outputId": "5c45104a-101e-4fb5-ef3a-f56f85553d21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini', 'max_depth': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvXJANe0YsxQ",
        "colab_type": "code",
        "outputId": "ba3a7b55-6c52-4254-fd42-9bc8ec6837cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "model=DecisionTreeClassifier(max_depth=1)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=1, 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": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyAM3ISxZQtm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "65QRutoJbSXm",
        "colab_type": "code",
        "outputId": "f16344ad-6af5-4722-8e1c-7ebfd3f5691d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7471264367816092"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 317
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ISfzX_abUVY",
        "colab_type": "code",
        "outputId": "399cadf6-8894-4e2c-a9e3-3f1c493c5187",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.75      1.00      0.86       130\n",
            "           2       0.00      0.00      0.00        44\n",
            "\n",
            "    accuracy                           0.75       174\n",
            "   macro avg       0.37      0.50      0.43       174\n",
            "weighted avg       0.56      0.75      0.64       174\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": "pnVR7sgEbYec",
        "colab_type": "code",
        "outputId": "42b2bbe3-91cb-4174-c1e0-ec5eb8207140",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[130   0]\n",
            " [ 44   0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "27uhB7K1bbes",
        "colab_type": "code",
        "outputId": "2af8c946-1286-401e-dad0-3ead7848c40f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 320
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Clc_FlKVbiGY",
        "colab_type": "code",
        "outputId": "937cfac2-03e3-4892-ab81-25e18930c684",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>174 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Dataset\n",
              "512        2\n",
              "443        1\n",
              "237        1\n",
              "502        1\n",
              "461        1\n",
              "..       ...\n",
              "308        1\n",
              "546        1\n",
              "20         1\n",
              "113        1\n",
              "0          1\n",
              "\n",
              "[174 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 321
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jk2zVdmabo31",
        "colab_type": "text"
      },
      "source": [
        "##SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6RpefFZebjkW",
        "colab_type": "code",
        "outputId": "7ab377af-e0a2-4335-fd57-e9141b221abb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "model=SVC(kernel='linear')\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 322
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZFglAjjbuyd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8JWLU-JUbx7r",
        "colab_type": "code",
        "outputId": "6f6ed798-24c2-4c23-83b5-dfe125bfb4c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7471264367816092"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 324
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbYAkLt4bz31",
        "colab_type": "code",
        "outputId": "bb79df57-0ff9-46e3-d04f-7a23dc703f62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.75      1.00      0.86       130\n",
            "           2       0.00      0.00      0.00        44\n",
            "\n",
            "    accuracy                           0.75       174\n",
            "   macro avg       0.37      0.50      0.43       174\n",
            "weighted avg       0.56      0.75      0.64       174\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": "_UsTj668b2Bi",
        "colab_type": "code",
        "outputId": "9bbbcf99-dbb8-4ee0-c9a8-cb3be7d8f22b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[130   0]\n",
            " [ 44   0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OM4J_IMnb7vT",
        "colab_type": "code",
        "outputId": "1599ae92-1ecf-4ca3-9265-327795de0d55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 327
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67hZr4Sib-W3",
        "colab_type": "code",
        "outputId": "5e7fe99b-0133-42b7-9b58-9e1ddb7e3a25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>174 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Dataset\n",
              "512        2\n",
              "443        1\n",
              "237        1\n",
              "502        1\n",
              "461        1\n",
              "..       ...\n",
              "308        1\n",
              "546        1\n",
              "20         1\n",
              "113        1\n",
              "0          1\n",
              "\n",
              "[174 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bbVsS8gcHIm",
        "colab_type": "text"
      },
      "source": [
        "##Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EgTPbllb_oq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "rfc=RandomForestClassifier()\n",
        "param={'n_estimators':[100,200,300,400],'max_depth':list(np.arange(1,20))}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PLf7L0BacQ8q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=GridSearchCV(rfc,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcCGt5vNcTUg",
        "colab_type": "code",
        "outputId": "315503ad-055d-40ba-eec6-f74fbcaaf445",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 2, 'n_estimators': 400}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 351
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2t6fXoDvcW2C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=model.best_estimator_\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_O7tTPYcZek",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhumYNciccFT",
        "colab_type": "code",
        "outputId": "7ff84e3e-930a-45f0-e0b4-aa3ee385ddb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "m.accuracy_score(label_test,label_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7471264367816092"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 356
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhlxgjAqceTQ",
        "colab_type": "code",
        "outputId": "42289b0e-5811-4cf4-b7d3-79d8961deef2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.75      1.00      0.86       130\n",
            "           2       0.00      0.00      0.00        44\n",
            "\n",
            "    accuracy                           0.75       174\n",
            "   macro avg       0.37      0.50      0.43       174\n",
            "weighted avg       0.56      0.75      0.64       174\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": "61-NjRSWcgq3",
        "colab_type": "code",
        "outputId": "a7509c47-79f2-40ba-eb5e-8e00eb05ea1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(m.confusion_matrix(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[130   0]\n",
            " [ 44   0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKEmizxYcn-A",
        "colab_type": "code",
        "outputId": "7af45e40-8efc-430a-a88b-0b2eca9fac8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 359
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R4lbo1Gfctyx",
        "colab_type": "code",
        "outputId": "88bca47f-8d4a-44eb-87fb-40c1511b20ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>512</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>308</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>546</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>174 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Dataset\n",
              "512        2\n",
              "443        1\n",
              "237        1\n",
              "502        1\n",
              "461        1\n",
              "..       ...\n",
              "308        1\n",
              "546        1\n",
              "20         1\n",
              "113        1\n",
              "0          1\n",
              "\n",
              "[174 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 360
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNU_rsgXcv4M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}