Assignment 05april.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3e4170ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%matplot` not found.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplot inline\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ebaafcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split in independent and the depenent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "00b5d32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb022a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3b96996e",
   "metadata": {},
   "outputs": [
    {
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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",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f794b4dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   int64\n",
       "Glucose                       int64\n",
       "BloodPressure                 int64\n",
       "SkinThickness                 int64\n",
       "Insulin                       int64\n",
       "BMI                         float64\n",
       "DiabetesPedigreeFunction    float64\n",
       "Age                           int64\n",
       "Outcome                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b4818a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "073ccca2",
   "metadata": {},
   "outputs": [
    {
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       "      <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",
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       "      <td>72</td>\n",
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       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
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       "      <th>2</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                       0.627   50  \n",
       "1                       0.351   31  \n",
       "2                       0.672   32  \n",
       "3                       0.167   21  \n",
       "4                       2.288   33  \n",
       "..                        ...  ...  \n",
       "763                     0.171   63  \n",
       "764                     0.340   27  \n",
       "765                     0.245   30  \n",
       "766                     0.349   47  \n",
       "767                     0.315   23  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "237f7d7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1dd51ace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "763    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: Outcome, Length: 768, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "570a92d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06ee9b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "df936faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a90e433",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "53e747a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=42,test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aa06d332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(514, 8)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "49ecf41b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(514,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "456128b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(254, 8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5cbb7149",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(254,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "012bfba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "518e4b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "trees=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c0dacced",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f7b2a64a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trees.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "368959c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=trees.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "41f2e11e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "67908b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "32853599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " Text(0.8421052631578947, 0.19230769230769232, 'X[6] <= 1.379\\ngini = 0.278\\nsamples = 6\\nvalue = [1, 5]'),\n",
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       " Text(0.8421052631578947, 0.2692307692307692, 'gini = 0.0\\nsamples = 8\\nvalue = [0, 8]'),\n",
       " Text(0.8631578947368421, 0.4230769230769231, 'X[4] <= 215.0\\ngini = 0.32\\nsamples = 5\\nvalue = [4, 1]'),\n",
       " Text(0.8526315789473684, 0.34615384615384615, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]'),\n",
       " Text(0.8736842105263158, 0.34615384615384615, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.9078947368421053, 0.8846153846153846, 'X[5] <= 28.7\\ngini = 0.232\\nsamples = 82\\nvalue = [11, 71]'),\n",
       " Text(0.8631578947368421, 0.8076923076923077, 'X[5] <= 25.35\\ngini = 0.486\\nsamples = 12\\nvalue = [5, 7]'),\n",
       " Text(0.8526315789473684, 0.7307692307692307, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
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       " Text(0.8631578947368421, 0.6538461538461539, 'X[0] <= 1.5\\ngini = 0.48\\nsamples = 5\\nvalue = [2, 3]'),\n",
       " Text(0.8526315789473684, 0.5769230769230769, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.8736842105263158, 0.5769230769230769, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
       " Text(0.8842105263157894, 0.6538461538461539, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
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       " Text(0.9052631578947369, 0.6538461538461539, 'X[7] <= 34.0\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.8947368421052632, 0.5769230769230769, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.9157894736842105, 0.5769230769230769, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.9473684210526315, 0.6538461538461539, 'X[2] <= 69.0\\ngini = 0.091\\nsamples = 63\\nvalue = [3, 60]'),\n",
       " Text(0.9368421052631579, 0.5769230769230769, 'X[3] <= 26.5\\ngini = 0.291\\nsamples = 17\\nvalue = [3, 14]'),\n",
       " Text(0.9263157894736842, 0.5, 'X[2] <= 63.0\\ngini = 0.49\\nsamples = 7\\nvalue = [3, 4]'),\n",
       " Text(0.9157894736842105, 0.4230769230769231, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
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       " Text(0.9473684210526315, 0.5, 'gini = 0.0\\nsamples = 10\\nvalue = [0, 10]'),\n",
       " Text(0.9578947368421052, 0.5769230769230769, 'gini = 0.0\\nsamples = 46\\nvalue = [0, 46]'),\n",
       " Text(0.9789473684210527, 0.7307692307692307, 'X[1] <= 181.5\\ngini = 0.48\\nsamples = 5\\nvalue = [2, 3]'),\n",
       " Text(0.968421052631579, 0.6538461538461539, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
       " Text(0.9894736842105263, 0.6538461538461539, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tree(trees,filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "906ebf87",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5f5bbea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7086614173228346\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9ff33798",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.80      0.77       156\n",
      "           1       0.64      0.56      0.60        98\n",
      "\n",
      "    accuracy                           0.71       254\n",
      "   macro avg       0.69      0.68      0.68       254\n",
      "weighted avg       0.70      0.71      0.70       254\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b0c1da34",
   "metadata": {},
   "outputs": [],
   "source": [
    "###Hyperparametertunning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5ab33616",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters={\n",
    "    'criterion':['ginni','entropy','log_loss'],\n",
    "    'splitter':['best','random'],\n",
    "    'max_depth':[1,2,3,4,5,6,7,8],\n",
    "    'max_features':['auto','sqrt','log2']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "92072b2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "132a06c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "trees1=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "752f8a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=GridSearchCV(trees1,param_grid=parameters,cv=5,scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c227c457",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:372: FitFailedWarning: \n",
      "480 fits failed out of a total of 720.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "240 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\PC\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\PC\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 937, in fit\n",
      "    super().fit(\n",
      "  File \"C:\\Users\\PC\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 352, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](\n",
      "KeyError: 'ginni'\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "240 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\PC\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\PC\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 937, in fit\n",
      "    super().fit(\n",
      "  File \"C:\\Users\\PC\\anaconda3\\lib\\site-packages\\sklearn\\tree\\_classes.py\", line 352, in fit\n",
      "    criterion = CRITERIA_CLF[self.criterion](\n",
      "KeyError: 'log_loss'\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\PC\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:969: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.64009138 0.66360175 0.68280982 0.65172283 0.67898344 0.64397487\n",
      " 0.68083    0.7178755  0.71210737 0.67892633 0.71206929 0.67511898\n",
      " 0.73346659 0.67696554 0.67504283 0.6849229  0.69447935 0.64791548\n",
      " 0.71210737 0.72760327 0.70810965 0.69072911 0.75090425 0.6888064\n",
      " 0.71408719 0.69836284 0.7141443  0.66337331 0.70824291 0.71020369\n",
      " 0.70232248 0.6867885  0.7275652  0.69261374 0.72562345 0.69457453\n",
      " 0.71205026 0.68298115 0.68288597 0.69063392 0.71591472 0.67888825\n",
      " 0.69832477 0.69457453 0.69653531 0.73934894 0.73342852 0.71214544\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan        nan        nan        nan        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['ginni', 'entropy', 'log_loss'],\n",
       "                         'max_depth': [1, 2, 3, 4, 5, 6, 7, 8],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'splitter': ['best', 'random']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "55a00940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 4,\n",
       " 'max_features': 'log2',\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9c252621",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0851eec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddc54541",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(accuracy_score(y_pred1,y_test))"
   ]
  }
 ],
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