Diabeties.ipynb

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  "metadata": {
    "colab": {
      "name": "Diabeties.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "iCq5anyc8dhQ",
        "Xx8bj2UT_i8V",
        "QbgkL3LaBMxb"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "-OCk3-F8TBt_",
        "colab_type": "code",
        "outputId": "05379e04-6a6d-4c8e-9154-55b4c189d73b",
        "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": "4bVgsMd9T-u9",
        "colab_type": "code",
        "outputId": "7bd05b9a-5eb1-40dd-a0d2-9085a1ecca0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "!unzip pima-indians-diabetes-database.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  pima-indians-diabetes-database.zip\n",
            "  inflating: diabetes.csv            \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUzTrdMsUKfa",
        "colab_type": "code",
        "outputId": "ed6d785f-dd9b-4c79-9f8e-9487621e79bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import sklearn.metrics as m"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KvHucVWpgD7s",
        "colab_type": "code",
        "outputId": "f94ed3b7-a4a9-48b4-ca3b-7e3fd804abcf",
        "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": "XUQIfEVohqE7",
        "colab_type": "code",
        "outputId": "6bcdd9c2-9b1d-4820-8f83-6859cab661e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "dataset=pd.read_csv('diabetes.csv')\n",
        "dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
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              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
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              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
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              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
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              "      <td>30.4</td>\n",
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              "<p>768 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  ...  Age  Outcome\n",
              "0              6      148  ...   50        1\n",
              "1              1       85  ...   31        0\n",
              "2              8      183  ...   32        1\n",
              "3              1       89  ...   21        0\n",
              "4              0      137  ...   33        1\n",
              "..           ...      ...  ...  ...      ...\n",
              "763           10      101  ...   63        0\n",
              "764            2      122  ...   27        0\n",
              "765            5      121  ...   30        0\n",
              "766            1      126  ...   47        1\n",
              "767            1       93  ...   23        0\n",
              "\n",
              "[768 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gX7YvIZNhwn5",
        "colab_type": "code",
        "outputId": "225e567a-355d-4b5d-f80f-cd41217c48c7",
        "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WSNBwdbEzCx",
        "colab_type": "code",
        "outputId": "d8b4d139-f5e5-4f8d-d55d-c521e06c618c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        }
      },
      "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": {
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\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ok-nYEj27oGl",
        "colab_type": "code",
        "outputId": "ef3a8284-0830-4066-e899-aaf017a4f0e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.countplot(x='Outcome',data=dataset)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0aa9d4cf60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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q7wFU1dzz/18GvLqNLwL+emybz1RVJbkGuL2qrgFIch2wmtGDCY8Avjw6w8V+jB73IA3KUNBSdz1w8nghyQHA4cDdPPhoev89+HN/3N5/MjaeW14G3A9cVlWv24M/U3pMnj7SUrcVeHqSU6F/Pen7GX0N5E3AkUn2SbKK0bfUzfm/JE9p48uBU5Ic0vZxcKv/O6OnzgK8HvjXx9HXlcDLkzyv7fMZSZ7/eH856fEyFLSk1eiJkL/D6D/1G4H/BO5ldHfRl4H/YnQ0cTbw9bFNNwHbklzcnir7buCLSb4JfKDNeQtwWpJtwBt54FrDJH3tBt4EfLhtfwXwgp/295Qm5VNSJUmdRwqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSuv8HHGGod29RL/oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwVY5rkoAq4T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset=dataset.drop('Pregnancies',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytDtv6qQAzOR",
        "colab_type": "code",
        "outputId": "1285f155-59ce-430e-c35b-2f0d85c0ec59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        }
      },
      "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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mDHiZueOnVKeZ+fpEHv7WVSz7ZAm7f/8wDrjiJJ4877d0OLQLm+/egf878grKN2jKdx65gomDXmPpwkX1eEZqLKKsjJOvO4v/PfV65k6bwxV9b2bUgBFMffv96jRzpszi/ot/x5HndK+x7dihb3DdNy8BYONWLbhp8N2MeW5USfOvxivKgqOuO4P7T72ZBdNmc27fG3hzwEhmvv1BdZqpYyZy71FXsvSTJex76uEcedlJPHz+3dXrD/vZd5k4/K1iu5fWqWO+eQQnH9edy6+/vb6zItWq0lBmRRnKrB5ERAD/AvqklHYGdgFaADeuYtPL13XetG5s/KWdWTJpKkvfm05auoz5jz1HyyO+8ql0W/70FGbe908qFy8tup9NjjqQ+f2eX9fZVSO3254d+WDiB0ydPJVlS5cx8NFBdD1y/xppunbbnycf6Q/A4McHs1fXvQD48kH78M6bE3hnzAQAFsxdQGVlZWlPQI1axy67MmXiFKZOnsaypct45tFnOaBbzfJ5QLf9eaq6fD7HXl33/NR+Djv6UAb1fbYUWdZ6aKcuOzNt4jRmvDedZUuXMeSx59nniH1rpJn5/gwmvzWJtEIduWzpMpYtWQZAkw2aUlZmc1x1a8suOzJv4nQWTJ5J5dIKxvUdxg7d9q6R5oOhb7Isn0E4beTbNG/bGoBNd96aKcPHkioqWbZoMbPenMx2B3cu+Tmocdq+y07MnDSNWe/NoGLpMl56bAhduu1TI83s92fywVuTVxqLfu9vfpXXn32lehastLbad9mJ2ZOmM/e9GVQsrWD0Y0PZbYV6892hY1ial7n3XhlPy7zeBNhq9+1p0aYVbz8/uqT5lgD26bIHrVp+ob6zIekz8E6wfhwKfJJSuh8gpVQBXAScGRHnRcRvqxJGRL+IODgibgE2iohXI6J3vu60iHgtIkZFxF/zZR0i4pl8+cCI2DZf3isi7o2IYRExId/nnyPizYjoVXC8bhExNCJGRsQjEdGiZD+VRqxJ281YOnX5NOel02bTtO1mNdI067QjTdttzoeDRtS6n1bf/hrz+g5eZ/nU+qFN2zbMmDKz+vvMqTPZvG2bImlmAFBRUclHCz6i1aYt2WaH9iQSt/e+hT8+eR8n/fCEGttdducl/Kn/7zntJ6eu+xNRo9SmXRtmTC0on9Nm0abdiuVzs+o0lRWVLFzwES03bVkjzcFHHcTARwfVWPaLOy/mD0/dx/cvPGUd5V7ri9ZtN2N2wXV9ztTZbLbCdX1lNmvXhtuf/A33DfsTfe77l7NlVKeat92UhVOWl6mFU+fQou2mtabvdOJBTHo2m3kw681JbHtQZ5o024Bmm7ag/X5f5Atbta51W2lNbLJla+ZMmV39fe7UOWyy5erXnVX2PeoAhvd9oS6zpvVcyy03ZX5B2VwwdQ4tt6y97tv7e4cwPq83I4JvXHkKT97Ye53nU5LUuDgwUz86AS8XLkgpLQAmU0t4uZTSpcCilFKXlNIpEdEJuBI4NKX0JeDCPOndwF9SSp2B3sBdBbvZFNiPbBCoL/C/eV72iIguEdEm3+fhKaW9gBHAT4vlJyJ+EBEjImLEPz6cVCyJ1kQE7a48i6k3/qnWJBt12YW0aDGLx02uNY20rpWXl9P5y7tz/fk38aNjLuRr3+haPVvh+gtupsfh53D+sT/hS/vuwZHHH1HPudX6arc9O7L4k8VMHDuxetmNF9zMWYf/gB9/5yL22HcPuh13eP1lUOu92VNncfHXL+SCA8/l4OMOoVWbVvWdJa2ndj32ALbovAMj73scgPeee51Jg17l+D7XcORvf8S0keOprHBmrBqOVptvwta7bssbhjFTPfnSMQewdefteb5nPwD2/f4RjB30Kgum+ZCFJNUmNfD/6osDM59fhwKPpJRmAaSUqloB+wEP5p//CnQt2OaxlM0JHw1MTymNTilVAm8AHYCvkr3zZkhEvAqcDmxX7OAppZ4ppX1SSvsc/4WiSVRg2bTZNC144rtp281YOm35EzllLTai2S7bscNDN7Hr839k4z13Zbs/XMlGe+xUnWaTbx/IvMeeK2m+1TjNmjaLLbbavPr75u02Z+a0WUXSbAFAeXkZzVs2Z/7cBcyYOotR/x3N/LkLWPzJYoY981922X3n6m0AFn20iAF9nmG3Lh1LdEZqTGZNncUW7QrKZ9s2zJq6YvmcXZ2mrLyMFi2bs2Dugur1h3Q/mGf6DPrUNpCVz4F9nqHjnpZPfXZzps1ms4Lreut2mzG74Lq+uubOmMPkcZPZbd9OdZk9rec+mjaXFgWzXFq0a83CaXM/lW6brp3Y54Lu9DvzTirz8HoAI+7uy0Nfv4JHT7kVIpg3YVpJ8q3Gb970ObTeavkMmU3btWbe9DWrO/f59v688tRwKpZVrDqxtJoWTJ9Lq4Ky2bJdaxZM//RAy44H7M5B5x/D386+g4q83tx2r5356mnd+NkLv+Hrl59Cl+90pdsvTixZ3iVJn18OzNSPMUCNgKUR0RLYFphHzd9Lszo87uL838qCz1XfmwABDMhn5XRJKX0xpXRWHR5/vfXxa+PZsMNWNG2/JdG0Ca2OOpAFTw+vXl/54ce8ufcpjP3a2Yz92tl8/MpYJp1zA4tGv50liKDVt7o6MKM68darb9F++61pt01bmjRtwmFHH8KQ/i/WSDOk/1C+/t1uABz0rYMYOeQVAIYPfokdOm7Phs02pLy8jC5f7czE8ZMoLy+jVR5KqrxJOfsf/lUmjH23tCemRuGtUWPZevutaZuXz0OPPpgXBwytkebFAUM5srp8HsgrQ16tXhcRHHzUQTzTd/nATFl5WXWos/Im5ex3+Fd4962J6/5k1Gi9PWo87bZvxxbbbEGTpk044KivMWLA8FVvSBYGbYMNNwCgecvmdNxnN6a888EqtpJW3/RRE9ikQ1tabrM5ZU3L2aX7V3l3wMgaadp02o5DbjmTfmfeyaLZywe2oyxotkkWyXizjtvQZrdtmPyc70xQ3Zg46m226NCONu23oLxpE7581AGMGlB7GOdi9u1+AMMfM4yZ6tYHo95hsw5t2bT95pQ3LWePo/bjrQE1gpzQrtN2HH3TWfQ++w4+Kqg3H/nJ77j9gB9zR9cLefKm3rz6rxfof+tDpT4FSdLnUNGwWVrnBgK3RMRpKaUHIqIcuAPoBUwAzo2IMmBroPBNsksjomlKaSnwDPDviLgzpTQ7Ilrns2ZeBE4kmy1zCrAmb4ofBvwuInZKKb0dEc2BrVNK49byfFVRyZRr7mP7B34JZWXMfeRpFo+fzBYXncKi0eP58OmVd+Y037cTS6fOZOl700uUYTVmFRWV/PrKu7n9wVspKyvjiYf/w8Rxkzjz4h6MHTWWIQOG8vhDT3DFXZfx4AsP8OG8D7n2vBsAWDh/IQ/3/Ac9n7iHlBLDnhnOsIH/pdlGzbj9wVtp0qQJZeVlvPz8SPr1fqKez1SfR5UVldx11W/5Ve+bKSsr4z8PP8XEcZM44+LTGTtqHC8OGMrjD/2Hy39zKX97oRcL5n3I9efdWL1956/uwcwpM5k6efkT3htssAG39b6Z8qZNKC8r4+UXXuHxBy2f+uwqKyr509U9ueKBaykrL2PQ3wfy/vj3OOGnJ/POa28z4unh7Nh5Jy7peRnNW7Vg78O/zPcuOomfHnEB7Xdqz2lXnklKiYjgsZ59mDzWsLCqO6miksFX/YXuf/s5ZeVljHl4MHPGfcBXfnYcM157l3cHjKTrFSfRdONmfOO+HwPw4ZTZPH7mnZQ1bcJx/7wKgCULF9H/x/eSDGWmOlJZUcmDV/+JnzxwBVFexpC/D2LK+PfpftEJTBr9DqOeHkGHzjty3u8vYeNWzel82N4cfdH3uKZbFl17s/abs2m7NowbNqaez0SNTWVFJf2u7sXpD1ya3cv8/VlmjP+Awy46ng9GT+Ctp0fy9ctOYYONm3HiPVm9Oe+D2fQ+5456zrkEl1xzCy+98hrz5i3gsGNO5byzvs9xRx1Z39mStBoii2ylUouIbYB7gI5kM2SeAC4GlgB/I5tR8ybZe2GuTSk9GxG3At2Bkfl7Zk4HLgEqgFdSSj0iYjvgfqANMBM4I6U0OSJ6Af1SSv+IiA75593zvBSuOxS4Fdgwz+qVKaW+KzuX0dsfZSFSg/SjJR/XdxakosrDCatqmNqUb1zfWZBqdWDlF+o7C1JRr5UvXnUiqZ5sSdP6zoJU1DUjbqjvLEi1atpmh6jvPDQmW7bq2KD7jqfPf6teft8OzGitOTCjhsqBGTVUDsyooXJgRg2ZAzNqqByYUUPmwIwaKgdm1JA5MFO3HJgpzp4hSZIkSZIkSZKkEvEdM5IkSZIkSZIkqc5V0qAnzNQbZ8xIkiRJkiRJkiSViAMzkiRJkiRJkiRJJWIoM0mSJEmSJEmSVOdSMpRZMc6YkSRJkiRJkiRJKhEHZiRJkiRJkiRJkkrEUGaSJEmSJEmSJKnOVRrKrChnzEiSJEmSJEmSJJWIAzOSJEmSJEmSJEklYigzSZIkSZIkSZJU55KhzIpyxowkSZIkSZIkSVKJODAjSZIkSZIkSZJUIoYykyRJkiRJkiRJda4SQ5kV44wZSZIkSZIkSZKkEnFgRpIkSZIkSZIkqUQcmJEkSZIkSZIkSSoR3zEjSZIkSZIkSZLqXEq+Y6YYZ8xIkiRJkiRJkiSViAMzkiRJkiRJkiRJJWIoM0mSJEmSJEmSVOcqDWVWlDNmJEmSJEmSJEmSSsSBGUmSJEmSJEmSpBIxlJkkSZIkSZIkSapzCUOZFeOMGUmSJEmSJEmSpBJxYEaSJEmSJEmSJKlEDGUmSZIkSZIkSZLqXGUylFkxzpiRJEmSJEmSJEkqEQdmJEmSJEmSJEmSSsRQZpIkSZIkSZIkqc4lQ5kV5YwZSZIkSZIkSZKkEnFgRpIkSZIkSZIkqUQcmJEkSZIkSZIkSSoR3zEjSZIkSZIkSZLqXMJ3zBTjjBlJkiRJkiRJkqQScWBGkiRJkiRJkiSpRAxlJkmSJEmSJEmS6lxKhjIrxhkzkiRJkiRJkiRJJeLAjCRJkiRJkiRJUokYykySJEmSJEmSJNU5Q5kV54wZSZIkSZIkSZKkEnFgRpIkSZIkSZIkqUQMZSZJkiRJkiRJkuqcgcyKc8aMJEmSJEmSJElSiTgwI0mSJEmSJEmSVCKRkpOJpIYiIn6QUupZ3/mQirF8qqGybKohs3yqobJsqqGybKohs3yqobJsSp8/zpiRGpYf1HcGpJWwfKqhsmyqIbN8qqGybKqhsmyqIbN8qqGybEqfMw7MSJIkSZIkSZIklYgDM5IkSZIkSZIkSSXiwIzUsBgPVA2Z5VMNlWVTDZnlUw2VZVMNlWVTDZnlUw2VZVP6nImUUn3nQZIkSZIkSZIkab3gjBlJkiRJkiRJkqQScWBGkiRJkiRJkiSpRByYkepARGwZEQ9GxISIeDkihkbEsRFxcET0q+/86fMrIioi4tWIGBURIyNi/3x5h4h4vY6O8WxE7JN/nhgRoyPitYjoHxFt6+IYapwi4oqIeCMvL69GxFfyMtSmSNoXV7Gvf+f7eDsi5uefX42I/Veyz+4RcelK9llnfydaf0XEwjreX3W5jIh9IuKuuty/VGUVbYgUETcUpG0TEUsj4rf592sj4uL6yvv6ruB390b++/tZRJTl61ZZb0REj6rf5Roc8/K1yG+viHg3z/PIiNhvDbbtUVDuzo2I0z5rPlbzeB0iYlFBO+PViNigDvffIyK2Kvj+x4j4Yl3tX+teRByT15Ed6zsvarwion1EPBoR4yPinYj4zarqorWppyU1TA7MSGspIgLoAzyXUtohpbQ3cCLQvn5zpkZiUUqpS0rpS8BlwM0lOOYhKaXOwAigRuMvMiW5dkREk1IcR59N3unybWCvvLwcDrxXW/qU0v4r219K6diUUhfgbOD5vNx3SSnVOqCTUuqbUrrls52BVP9SSiNSSj+u73yo0VpZG+Jd4FsF378LvFHKzGmlqn53nYAjgG8A18A6rTfWtsPvkvw6finw+8+yg5TSfSmlB1Y3/Vq0Fd8paGd0SSkt+Yz7KaYHUD0wk1I6O6U0pg73r3XvJOCF/F+pzuV9SP8C+qSUdgZ2AVoAN65iUwdmpEbGgRlp7R0KLEkp3Ve1IKU0KaV0d2GiFZ88jIjXI6JD/vm0/InzURHx13xZh4h4Jl8+MCK2zZd/N992VEQ8ly8rj4jbIuKlPP3/rPOzVn1oCcxdcWFENIuI+/OZLq9ExCGrWL5RRDwUEW9GxL+BjWo53nPATnlZHBsRDwCvA9tExCUF5e2X+X6bR8Tjedl8PSJOyJffEhFj8rS358t6RcTxBeewMP/34Ih4PiL6AmMs2w1aO2BWSmkxQEppVkppStXKvJz9JyLOyb8X/o6fjYh/RMRbEdE7vzlZlQvyp3BHVz3BuMJTtltGNutmVP5/jYGgiNgh/zv4cr7dvyLiyfwptV8VpOsW2azHkRHxSES0yJcXK8efqo/VeK2s7K5JPVdkn/3yz9dGxJ/zY0yICAdsVJdWbEN8DLwZ+YxZ4ATg7yXPlVYppTQD+AFwfmQK641982vWKxHxYkTsWrDpNnl9Mj4irqlaGBGnRsTwyGaK/D5va90CbJQv672SdOV53fZ6fj2+qEiWnwN2qm0f+fIzImJcRAwHDijIW/X9Un69rpqRe1ssn2nYIyL6RsQzwMDI2p9/zo/zSkQcnadbozZkYR0dEcdHRK/8c6+IuCv/+U5YoV7/Rf5zGJVfB44H9gF65/neKGrOTD8pT/96RNxaeOyIuDHfz7CI2HJledW6k7f7ugJnkT1sSUSURcQ9+bV/QEQ8UVUOImLviBgcWdSMpyKiXT1mX58fhwKfpJTuB0gpVQAXAWdGxHlRMOMxIvrl9X6xenpN+pF6RcS9eR0zId/nnyPrE+hVcLyi90KS1g2fRpbWXidg5GfdOCI6AVcC+6eUZkVE63zV3cBfUkp/iYgzgbuAY4CrgSNTSh9ExCZ52rOA+SmlL0fEhsCQiOifUnr3s+ZLDcZGEfEq0IysI/zQIml+BKSU0h6RdVj3j4hdVrL8h8DHKaXdIqIztZffbwOj8887A6enlIZFRLf8+75AAH0j4kBgc2BKSulbABHRKiI2A44FOqaUUkGZXZm9gN1TSu9GxA+wbDdU/YGrI2Ic8DTwcEppcL6uBfAQ8EAtT77uSVZ3TgGGkHXKvLCK481KKe0VEecBF5PNrCl0FzA4pXRs3vHTAtgUILKOqoeAHimlUXm92yXPx2JgbETcDSwiq48PTyl9FBG/AH4aEb+jeDkuVh+rcftU2Y2IN1nzeq42HYFDgC+Qlct7U0pL1zbTWm+tqg3xEHBiREwHKsjK9VaowUkpTcivbVussOot4GsppWURcThwE3Bcvm5fYHeyQbiXIuJx4COyQbgDUkpLI+Ie4JSU0qURcX4+44WI2K1YOrJZVVunlHbP0xWr744CRte2j4gYAPwS2BuYDwwCXimyn/uBc1JKQ/MOyUJ7AZ1TSnMi4ibgmZTSmXl+hkfE03l+P9WGBBKwY/63ATAkpfSj4j/5au3IOus7An2Bf0TEN4Cjga+klD6OiNZ5fs4HLk4pjch/RuT/bgXcmp/3XLJ2+TEppT5Ac2BYSumKyB4WOQe4AdWHo4EnU0rjImJ2ROwNbA90AL5I9jf4JvDniGhKds9+dEppZmQPpd0InFk/WdfnSCfg5cIFKaUFETGZWvppi9TTa9qPBNm90X5Ad7K67ACye6qXIqIL8D5F7oWA6+rqxCXV5MCMVMfyDryuwBLgktXY5FDgkZTSLICU0px8+X7Ad/LPfwWqnugeAvSKiL+TTX8F6AZ0juVPcLUi6zi38/rzb1FB42s/4IGI2H2FNF3JGmCklN6KiElk06FrW34gWQONlNJrEfHaCvsbFBEVwGtkDbNNgEkppWH5+m75/1U30S3IytvzwB2RPQHYL6X0fGQhJj4B/hTZ052r886l4QUDL5btBiqltDC/Wf0aWUfyw7H8fS+PAr9KKfWuZfPhKaX3AfKOkQ6semCmqr57meV1Y6FDgdPyvFUA8yNiU7IBw0eB76wQSmRgSml+nocxwHZkZf2LZJ03ABsAQ8k6joqV42L1sRq3YmV3GGtez9Xm8XwW2uKImAFsSXaTLH0Wq2pDPAlcD0wHHq6H/GnttQL+EhE7kw04NC1YNyClNBsgIv5F1i5cRjYw8FJ+ndsImFFkv4fVku4xYIf8YYbHyR7SqHJbRFwJzCR7aKy2fXwFeDalNDPP28Nk7dNq+QDLF1JKQ/NFD5I9MFR4blX3TN2A7rE8MkEzYFtqb0OOIw9lVuS8a9MnpVRJNpu7ajbL4cD9KaWPocY9XG2+TM3z7k3WJu9Ddt9Yde14mSx8nerHScBv8s8P5d+bkN2vVwLTImJQvn5XssHPAXkZLwemlja7Wo+taT8SwGP5Q0SjgekppdEAEfEGWZu2PcXvhSStIw7MSGvvDZY/mUZK6UeRvaR6xArpllEzfGCzz3KwlNK5EfEVsrjgL+cdowFckFJ66rPsU58P+RODbcg6mtelQ6oaeFB9c/xRwfoAbk4pfSp+eETsBXwTuCEiBqaUrouIfcluzo8HzidrRFb/PUT2zprCFx2ueCzLdgOVD4A8CzybN/BPz1cNAb4eEQ+mlFKRTRcXfK5g9dojVdusbvoq84HJZB1ShQMzxfIQZJ09n4opXqwcF6uPqzrB1Gh9qtzkT6qvaT232vuvi0xLxdoQKaUlEfEy8DOyjpju9ZU/rVxE7EBWJ8wAditYdT0wKJ8t2oHsmlxlxetvIrvO/SWldNmqDllbuoj4EnAkcC7wPZbPDrgkpfSPgnSHFNtHRBzD2luxrXhcSmnsCscp2obMf07FFP68VrxPK6ybVyf86ppaWtBesu6vJ/mMg0OBPSIikQ20JODftW0CvJFS2q9EWVTjMYaszVgtIlqSDSrPow76jWpRVZdVUrNeqySrdyqo5V5I0rrhO2aktfcM0CwifliwbOMi6SaSTbuv6rzevmD770YW8qmqQQjwInlcW7Kp+M/n63dMKf03pXQ12RNp2wBPAT/Mp1MTEbtERPO6OT01FJGFIysHVuz4fZ6sjBBZqLJtgbErWf4ccHK+fHeg8xpm5Smy+LdV797YOiK2yEM0fJxS+htwG7BXnqZVSukJsri5X8r3MZHsKUrIOoIKn/Bc8ViW7QYoInbNn9Ct0gWYlH++mixMx+9KmKWBZGH6quLKt8qXLyELM3VaRJy8in0MIwtNVRUbv3le5oqW41rqY61n6qCek9a5lbQh7gB+sRpP+6ueRMTmwH3Ab4s87NAK+CD/3GOFdUdEROuI2IgsjM0Qsmvl8RGxRb7v1hGxXZ5+aVV7q7Z0+eBeWUrpn2SzqvdaSdZrO9Z/gYMiYrP8eN9dccOU0jzgw/zhB1h+T1TMU2Tvoat659eeBcvXpA05PSJ2ywfSj11JuioDgDMiYuOq88uXf0gWjnJFw8nOu01kYelOAgYXSaf6czzw15TSdimlDimlbchm6c8BjovsXTNbAgfn6ccCm+czEomIppGFl5JWZSCwcUScBtm9C9n1uBcwAeiSl7dtyMJSVimsp9eoH2k1Fb0XWtOTk7T6fBJDWkv5VNBjgP+NiJ+Tdc59BPxihaT/JOsYfIPshmRcvv0bEXEjMDiy8FGvkN1YXQDcHxGX5Ps8I9/PbXlnaJBd0EeRhZzqAIzMb0pmsjyOqD7fquLDQ/Y7Pz2lVBE135V+D3BvPmNhGdl7NBZHFsu72PJ7ycrWm2QxkmvEt12VlFL/yOKGD83zsRA4lexFr7dFRCWwlKyT/AvAoxHRLM//T/Pd/CFfPooslMpHFPdHLNsNVQvg7nxG1TLgbbKXE1eFGrmQLP72r1JKPy9Bfi4EekbEWWRPe/2QPJxEHiP522ShJj71AvYqeXzwHsD/RRaPHrKOpw8pXo6L1cda/6xtPSetK6tsQ6SU3iCb/a2Gpep315TsGvtX4M4i6X5FFsrsSrLQYoWGk91/tAf+VvDOkyvJ3m9SRtZe+xHZgxU9gdciYmRK6ZRa0i0ia0NWPeBZ68yblNKYYvtI2fsKryULjzMPeLWWXZwF/CFvVw4mmwFbzPXAr/O8l5F1pH+bNW9DXkoWTmwmWeSDlb7wOqX0ZGTvZBgREUuAJ4DLyTpW74uIRWQhharST40s5Osgsr/Hx1NKj67sGCq5k8jeA1Ton2Sz1N4nm+XwHtn7Oefnsw6PB+7KHwhqQlYWrVO1Unkf0rHAPRFxFdlD81V1yBKyemwM2b164ftgV6yn16QfaXXyVdu90LjPfLKSViqKRxiRJEmSJEkqvYhokVJamH++FGiXUrqwnrOl9VRVecxnJwwHDkgpTavvfEmSPt+cMSNJkiRJkhqSb0XEZWR9FpP4dKg2qZT65bPENwCud1BGklQXnDEjSZIkSZIkSZJUImWrTiJJkiRJkiRJkqS64MCMJEmSJEmSJElSiTgwI0mSJEmSJEmSVCIOzEiSJEmSJEmSJJWIAzOSJEmSJEmSJEkl8v+RSygK81BUBwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 2160x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWk_mNeT7iIB",
        "colab_type": "text"
      },
      "source": [
        "##Splitting Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0O2ii2YE2tG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=dataset.iloc[:,:-1]\n",
        "labels=dataset.iloc[:,[-1]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GUMUiTd79ir",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features=np.array(features)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NASocfK-7_aP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "moZ58Nsd8Awm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_train,feature_test,label_train,label_test=train_test_split(features,labels,test_size=0.2,random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34Y8vi1d8XwB",
        "colab_type": "code",
        "outputId": "de1dece2-0bc0-4a49-bbee-ec84c3e72d3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "feature_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 84.   ,   0.   ,   0.   , ...,   0.   ,   0.304,  21.   ],\n",
              "       [112.   ,  82.   ,  24.   , ...,  28.2  ,   1.282,  50.   ],\n",
              "       [139.   ,  46.   ,  19.   , ...,  28.7  ,   0.654,  22.   ],\n",
              "       ...,\n",
              "       [101.   ,  86.   ,  37.   , ...,  45.6  ,   1.136,  38.   ],\n",
              "       [141.   ,   0.   ,   0.   , ...,  42.4  ,   0.205,  29.   ],\n",
              "       [125.   ,  96.   ,   0.   , ...,  22.5  ,   0.262,  21.   ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iCq5anyc8dhQ",
        "colab_type": "text"
      },
      "source": [
        "##Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZYDsYMJA8ZIz",
        "colab_type": "code",
        "outputId": "2eaf325f-c0f0-41e2-fa39-9a8227c0a767",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 154
        }
      },
      "source": [
        "model=LogisticRegression(max_iter=300)\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=300,\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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsxXQkXW8nuc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6PEUF299C89",
        "colab_type": "code",
        "outputId": "5b4dc27d-0d45-45ec-f518-cea12d65944d",
        "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": [
              "0.7662337662337663"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JalYExvC9RBH",
        "colab_type": "code",
        "outputId": "6301f057-9969-4f37-ed02-82ddc8aecaea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
              "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,\n",
              "       0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
              "       0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "diY8PaHi9gK9",
        "colab_type": "code",
        "outputId": "71045fc7-21a2-4aa7-ac57-d50f5dd2258a",
        "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>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>668</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>624</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>690</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>534</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>344</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>462</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Outcome\n",
              "668        0\n",
              "324        0\n",
              "624        0\n",
              "690        0\n",
              "473        0\n",
              "..       ...\n",
              "355        1\n",
              "534        0\n",
              "344        0\n",
              "296        1\n",
              "462        0\n",
              "\n",
              "[154 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_zSzdIf9h94",
        "colab_type": "code",
        "outputId": "f60236a0-35f3-452b-8f13-51cfa981b28f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.83      0.82        99\n",
            "           1       0.68      0.65      0.67        55\n",
            "\n",
            "    accuracy                           0.77       154\n",
            "   macro avg       0.75      0.74      0.74       154\n",
            "weighted avg       0.76      0.77      0.77       154\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1v2eFWz897CP",
        "colab_type": "code",
        "outputId": "5ed1ab1a-f920-4034-d2ab-d48c07041fb2",
        "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": [
            "[[82 17]\n",
            " [19 36]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3D1TJdYi-FL5",
        "colab_type": "text"
      },
      "source": [
        "##KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-sT8gdt-A6T",
        "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": "n6nU7BJO-OBA",
        "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": "glt1M5eM-QIQ",
        "colab_type": "code",
        "outputId": "10e8b61e-5bfb-4428-f921-96a2c5f557d0",
        "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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3pRIhod-UQN",
        "colab_type": "code",
        "outputId": "5bf1fdac-96b0-44d1-c01c-dfebf2e8f393",
        "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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agQF8OB--uf-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0v5qp_56-4AV",
        "colab_type": "code",
        "outputId": "bee1995f-7ea6-4b52-d89e-f7518e68cfed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1,\n",
              "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,\n",
              "       0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1,\n",
              "       0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0,\n",
              "       0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBUiVzbO-5Fk",
        "colab_type": "code",
        "outputId": "d121eded-8287-4629-a0e1-d01b74ad6d62",
        "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": [
              "0.7207792207792207"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_dfg0je--PE",
        "colab_type": "code",
        "outputId": "5f3e7936-d6c0-4cc9-a160-ec3573febb32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      0.77      0.78        99\n",
            "           1       0.60      0.64      0.62        55\n",
            "\n",
            "    accuracy                           0.72       154\n",
            "   macro avg       0.70      0.70      0.70       154\n",
            "weighted avg       0.72      0.72      0.72       154\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRN-9gRR_KuB",
        "colab_type": "code",
        "outputId": "525d6e0e-457c-4fc4-96a1-35495f52f84c",
        "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": [
            "[[76 23]\n",
            " [20 35]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xx8bj2UT_i8V",
        "colab_type": "text"
      },
      "source": [
        "##Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfhuCmZa_fmq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "dt=DecisionTreeClassifier()\n",
        "param={'max_depth':list(np.arange(1,20))}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idl_ilFz_psQ",
        "colab_type": "code",
        "outputId": "35d81e73-50bf-4e46-8b73-3340cf733a6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "model=GridSearchCV(dt,param_grid=param)\n",
        "model.fit(feature_train,label_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=None, error_score=nan,\n",
              "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
              "                                              criterion='gini', max_depth=None,\n",
              "                                              max_features=None,\n",
              "                                              max_leaf_nodes=None,\n",
              "                                              min_impurity_decrease=0.0,\n",
              "                                              min_impurity_split=None,\n",
              "                                              min_samples_leaf=1,\n",
              "                                              min_samples_split=2,\n",
              "                                              min_weight_fraction_leaf=0.0,\n",
              "                                              presort='deprecated',\n",
              "                                              random_state=None,\n",
              "                                              splitter='best'),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
              "                                       13, 14, 15, 16, 17, 18, 19]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJ7-OJ_5_sTj",
        "colab_type": "code",
        "outputId": "d550a63b-99e5-4f00-eabe-83a7290393ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "model.best_params_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxUttILe_vHs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=model.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IipE1ppS_x6R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Kl57nm2_2vt",
        "colab_type": "code",
        "outputId": "ab7f2f66-6080-4c87-b978-63263b7a4d82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "       0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
              "       0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CK5s8rc_38p",
        "colab_type": "code",
        "outputId": "8ab96534-9954-451e-f6f5-70d2b5f75bcc",
        "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": [
              "0.7597402597402597"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCNDHeHxAMiv",
        "colab_type": "code",
        "outputId": "5803d4ba-2f13-48d3-a179-2644acd95ac0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.84      0.82        99\n",
            "           1       0.68      0.62      0.65        55\n",
            "\n",
            "    accuracy                           0.76       154\n",
            "   macro avg       0.74      0.73      0.73       154\n",
            "weighted avg       0.76      0.76      0.76       154\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kM2Rn5KOAZDa",
        "colab_type": "code",
        "outputId": "7bcc0865-072f-4a84-f973-30c75bf10ed1",
        "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": [
            "[[83 16]\n",
            " [21 34]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbgkL3LaBMxb",
        "colab_type": "text"
      },
      "source": [
        "##SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ycy7J3piAfD1",
        "colab_type": "code",
        "outputId": "858dac83-008a-468d-ed78-a2a9fec90eed",
        "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": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWqonh9eBYSv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKnww4B-BdP9",
        "colab_type": "code",
        "outputId": "735fa281-c414-4a9d-b67d-72a4a3581ac8",
        "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": [
              "0.7857142857142857"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxFTEhHaBf90",
        "colab_type": "code",
        "outputId": "1970c334-aa89-468d-82a2-af4e0948d750",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.85      0.84        99\n",
            "           1       0.71      0.67      0.69        55\n",
            "\n",
            "    accuracy                           0.79       154\n",
            "   macro avg       0.77      0.76      0.76       154\n",
            "weighted avg       0.78      0.79      0.78       154\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dUqC7kdB1zs",
        "colab_type": "code",
        "outputId": "3a9a215d-a78d-4c94-dcf5-d6e9117ffb4f",
        "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": [
            "[[84 15]\n",
            " [18 37]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ss_0OYL8B4Y2",
        "colab_type": "code",
        "outputId": "7f40f126-3921-424e-ce7b-57986bc9c876",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0,\n",
              "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
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          "metadata": {
            "tags": []
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          "execution_count": 46
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRduREsNB5yK",
        "colab_type": "code",
        "outputId": "1b74e508-79b0-4221-950e-519406cd30f7",
        "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>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>668</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>624</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>690</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>534</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>344</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>462</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Outcome\n",
              "668        0\n",
              "324        0\n",
              "624        0\n",
              "690        0\n",
              "473        0\n",
              "..       ...\n",
              "355        1\n",
              "534        0\n",
              "344        0\n",
              "296        1\n",
              "462        0\n",
              "\n",
              "[154 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fxf70cDRCBEH",
        "colab_type": "text"
      },
      "source": [
        "## Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jtHTo6tB7J5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "rfc=RandomForestClassifier()\n",
        "param={'n_estimators':[50,100,150,200],'max_depth':list(np.arange(1,20))}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gq9kSdHiCI_D",
        "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": "ec7Jv5-PCLPk",
        "colab_type": "code",
        "outputId": "0923f439-5a81-485a-d61d-ba68c7f516a6",
        "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': 12, 'n_estimators': 200}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w6EUn7VYD9mB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=model.best_estimator_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WtAQlCZD_zf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_pred=model.predict(feature_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCYdcC-0EEUf",
        "colab_type": "code",
        "outputId": "064cd684-78a4-4bee-8126-e683b9a50c13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1,\n",
              "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,\n",
              "       0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
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          },
          "metadata": {
            "tags": []
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          "execution_count": 66
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "8g_sCN4FEFcJ",
        "colab_type": "code",
        "outputId": "833aad74-44ef-41ec-bdb0-ed01c7b5c32c",
        "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": [
              "0.7402597402597403"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVeQq87AEISn",
        "colab_type": "code",
        "outputId": "cc41526b-cbf5-46a3-c1dc-3fa212a8dde0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "print(m.classification_report(label_test,label_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.77      0.79        99\n",
            "           1       0.62      0.69      0.66        55\n",
            "\n",
            "    accuracy                           0.74       154\n",
            "   macro avg       0.72      0.73      0.72       154\n",
            "weighted avg       0.75      0.74      0.74       154\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cl-WG-EEPSq",
        "colab_type": "code",
        "outputId": "8408c7d3-967d-499a-f3a3-ee2ccde8f52f",
        "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": [
            "[[76 23]\n",
            " [17 38]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQX9QlRZER7W",
        "colab_type": "code",
        "outputId": "239f9d08-5fe7-49f5-d352-119eb8311c96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "label_pred"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "array([0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
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              "       0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,\n",
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7T2egeSOEW0l",
        "colab_type": "code",
        "outputId": "08ac5459-0e1f-45c2-c13a-17c8305f4185",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
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      },
      "source": [
        "label_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>668</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>324</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>624</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>690</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>355</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>534</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>344</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>462</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>154 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Outcome\n",
              "668        0\n",
              "324        0\n",
              "624        0\n",
              "690        0\n",
              "473        0\n",
              "..       ...\n",
              "355        1\n",
              "534        0\n",
              "344        0\n",
              "296        1\n",
              "462        0\n",
              "\n",
              "[154 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8wMuoMOEX-_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}