08_Decision_Tree_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "id": "004733d9",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>926424</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>926682</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>569 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "           id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302        17.99         10.38          122.80     1001.0   \n",
       "1      842517        20.57         17.77          132.90     1326.0   \n",
       "2    84300903        19.69         21.25          130.00     1203.0   \n",
       "3    84348301        11.42         20.38           77.58      386.1   \n",
       "4    84358402        20.29         14.34          135.10     1297.0   \n",
       "..        ...          ...           ...             ...        ...   \n",
       "564    926424        21.56         22.39          142.00     1479.0   \n",
       "565    926682        20.13         28.25          131.20     1261.0   \n",
       "566    926954        16.60         28.08          108.30      858.1   \n",
       "567    927241        20.60         29.33          140.10     1265.0   \n",
       "568     92751         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0            0.11840           0.27760         0.30010              0.14710   \n",
       "1            0.08474           0.07864         0.08690              0.07017   \n",
       "2            0.10960           0.15990         0.19740              0.12790   \n",
       "3            0.14250           0.28390         0.24140              0.10520   \n",
       "4            0.10030           0.13280         0.19800              0.10430   \n",
       "..               ...               ...             ...                  ...   \n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     symmetry_mean  fractal_dimension_mean diagnosis  \n",
       "0           0.2419                 0.07871         M  \n",
       "1           0.1812                 0.05667         M  \n",
       "2           0.2069                 0.05999         M  \n",
       "3           0.2597                 0.09744         M  \n",
       "4           0.1809                 0.05883         M  \n",
       "..             ...                     ...       ...  \n",
       "564         0.1726                 0.05623         M  \n",
       "565         0.1752                 0.05533         M  \n",
       "566         0.1590                 0.05648         M  \n",
       "567         0.2397                 0.07016         M  \n",
       "568         0.1587                 0.05884         B  \n",
       "\n",
       "[569 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "from scipy import stats\n",
    "df=pd.read_csv('cancer.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e125a92",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>fractal_dimension_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
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       "      <td>0.3001</td>\n",
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       "      <td>0.07871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302        17.99         10.38          122.80     1001.0   \n",
       "1    842517        20.57         17.77          132.90     1326.0   \n",
       "2  84300903        19.69         21.25          130.00     1203.0   \n",
       "3  84348301        11.42         20.38           77.58      386.1   \n",
       "4  84358402        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean  fractal_dimension_mean  \n",
       "0         0.2419                 0.07871  \n",
       "1         0.1812                 0.05667  \n",
       "2         0.2069                 0.05999  \n",
       "3         0.2597                 0.09744  \n",
       "4         0.1809                 0.05883  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Use the columns: 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',\n",
    "'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean',\n",
    "'symmetry_mean', 'fractal_dimension_mean' as the independent variables'''\n",
    "x=df.iloc[:,0:11]\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fa20254f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    M\n",
       "1    M\n",
       "2    M\n",
       "3    M\n",
       "4    M\n",
       "Name: diagnosis, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the target variable as 'diagnosis' (Malignant – M, Benign – B)\n",
    "y=df.iloc[:,11]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b3e901c4",
   "metadata": {},
   "outputs": [
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       "       0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Encode the categorical value of the target column to numerical value\n",
    "le=LabelEncoder()\n",
    "y=le.fit_transform(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "03b25b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Divide the data into training (75%) and testing set (25%)\n",
    "x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "56f90c27",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Perform the classification with entropy and information gain\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "clf=clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "27ca131d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=clf.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3c90e258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[83,  8],\n",
       "       [ 6, 46]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Analyse the performance of the classifier with various performance measures␣,suchasconfusion matrix, accuracy, recall,\n",
    "precision, specificity, f-score, Receiver operatingcharacteristic (ROC) curve,and Area Under Curve (AUC) score.'''\n",
    "conf_matrix=confusion_matrix(y_test,y_pred)\n",
    "cm=conf_matrix\n",
    "conf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "dbe46f53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9020979020979021\n",
      "specificity: 0.8846153846153846\n",
      "recall: 0.8846153846153846\n",
      "presiction: 0.8518518518518519\n",
      "f1: 0.8679245283018868\n"
     ]
    }
   ],
   "source": [
    "accc=accuracy_score(y_test,y_pred)\n",
    "fsc=f1_score(y_test,y_pred)\n",
    "print(\"accuracy:\",accuracy_score(y_test,y_pred))\n",
    "print(\"specificity:\",recall_score(y_test,y_pred))\n",
    "print(\"recall:\",recall_score(y_test,y_pred))\n",
    "print(\"presiction:\",precision_score(y_test,y_pred))\n",
    "print(\"f1:\",f1_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2133072e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc31ed9b390>,\n",
       " <matplotlib.lines.Line2D at 0x7fc31ed9b4e0>,\n",
       " <matplotlib.lines.Line2D at 0x7fc31ed9b630>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(roc_curve(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "58156585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8983516483516484"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc1,auc2,thresholds=roc_curve(y_test,y_pred)\n",
    "auccc=auc(auc1,auc2)\n",
    "auc(auc1,auc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3aa0e7eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Display the constructed decision tree.\n",
    "tree.plot_tree(clf);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "81c7c8e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=           id  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0      842302        17.99         10.38          122.80     1001.0   \n",
       "1      842517        20.57         17.77          132.90     1326.0   \n",
       "2    84300903        19.69         21.25          130.00     1203.0   \n",
       "3    84348301        11.42         20.38           77.58      386.1   \n",
       "4    84358402        20.29         14.34          135.10     1297.0   \n",
       "..        ...          ...           ...             ...        ...   \n",
       "564    926424        21.56         22.39          142.00     1479.0   \n",
       "565    926682        20.13         28.25          131.20     1261.0   \n",
       "566    926954        16.60         28.08          108.30      858.1   \n",
       "567    927241        20.60         29.33          140....\n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     symmetry_mean  fractal_dimension_mean  \n",
       "0           0.2419                 0.07871  \n",
       "1           0.1812                 0.05667  \n",
       "2           0.2069                 0.05999  \n",
       "3           0.2597                 0.09744  \n",
       "4           0.1809                 0.05883  \n",
       "..             ...                     ...  \n",
       "564         0.1726                 0.05623  \n",
       "565         0.1752                 0.05533  \n",
       "566         0.1590                 0.05648  \n",
       "567         0.2397                 0.07016  \n",
       "568         0.1587                 0.05884  \n",
       "\n",
       "[569 rows x 11 columns],\n",
       "               with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_f=StandardScaler(x)\n",
    "x_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f9827de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.36405166e-01,  1.09706398e+00, -2.07333501e+00, ...,\n",
       "         2.53247522e+00,  2.21751501e+00,  2.25574689e+00],\n",
       "       [-2.36403445e-01,  1.82982061e+00, -3.53632408e-01, ...,\n",
       "         5.48144156e-01,  1.39236330e-03, -8.68652457e-01],\n",
       "       [ 4.31741086e-01,  1.57988811e+00,  4.56186952e-01, ...,\n",
       "         2.03723076e+00,  9.39684817e-01, -3.98007910e-01],\n",
       "       ...,\n",
       "       [-2.35727466e-01,  7.02284249e-01,  2.04557380e+00, ...,\n",
       "         1.05777359e-01, -8.09117071e-01, -8.95586935e-01],\n",
       "       [-2.35725168e-01,  1.83834103e+00,  2.33645719e+00, ...,\n",
       "         2.65886573e+00,  2.13719425e+00,  1.04369542e+00],\n",
       "       [-2.42405862e-01, -1.80840125e+00,  1.22179204e+00, ...,\n",
       "        -1.26181958e+00, -8.20069901e-01, -5.61032377e-01]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_p=StandardScaler().fit_transform(x)\n",
    "x_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "50aa98b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.88150465532582e-17"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "94b10363",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.23581339121091452"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(x_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4fec0ec6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([[-0.24307899, -0.50761623, -1.01451947, -0.38550046, -0.40582245,\n",
       "         0.30883801,  0.19631867, -1.11487284, -1.26181958, -0.76895669,\n",
       "        -0.86865246]]), count=array([[ 1,  4,  3,  3,  3,  5,  3, 13, 13,  4,  3]]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(x_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e11ae180",
   "metadata": {},
   "outputs": [],
   "source": [
    "ftclfs=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "93a7726b",
   "metadata": {},
   "outputs": [
    {
     "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>Depth</th>\n",
       "      <th>TP</th>\n",
       "      <th>NP</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F-score</th>\n",
       "      <th>Auc-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Default</td>\n",
       "      <td>84</td>\n",
       "      <td>47</td>\n",
       "      <td>0.916084</td>\n",
       "      <td>0.886792</td>\n",
       "      <td>0.913462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>84</td>\n",
       "      <td>46</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.876190</td>\n",
       "      <td>0.903846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>82</td>\n",
       "      <td>46</td>\n",
       "      <td>0.895105</td>\n",
       "      <td>0.859813</td>\n",
       "      <td>0.892857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>83</td>\n",
       "      <td>46</td>\n",
       "      <td>0.902098</td>\n",
       "      <td>0.867925</td>\n",
       "      <td>0.898352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Depth  TP  NP  Accuracy   F-score  Auc-score\n",
       "0  Default  84  47  0.916084  0.886792   0.913462\n",
       "1        3  84  46  0.909091  0.876190   0.903846\n",
       "2        5  82  46  0.895105  0.859813   0.892857\n",
       "3        7  83  46  0.902098  0.867925   0.898352"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Prune the tree with maximum depth as 3,5,7 and tabulate the various TP, TN,accuracy, f-score and AUC score obtained.\n",
    "for i in range(1,8,2):\n",
    " if i==1:\n",
    "  clf=tree.DecisionTreeClassifier()\n",
    " else:\n",
    "  clf=tree.DecisionTreeClassifier(max_depth=i)\n",
    " clf.fit(x_train,y_train)\n",
    " y_pred=clf.predict(x_test)\n",
    " conf_matrix=confusion_matrix(y_test,y_pred)\n",
    " cm=conf_matrix\n",
    " accc=accuracy_score(y_test,y_pred)\n",
    " fsc=f1_score(y_test,y_pred)\n",
    " auc1,auc2,thresholds=roc_curve(y_test,y_pred)\n",
    " auccc=auc(auc1,auc2)\n",
    " if i==1:\n",
    "  ins=['Default',cm[0][0],cm[1][1],accc,fsc,auccc]\n",
    " else:\n",
    "  ins=[i,cm[0][0],cm[1][1],accc,fsc,auccc]\n",
    " ftclfs.append(ins)\n",
    "ftdn=pd.DataFrame(ftclfs,columns=['Depth','TP','NP','Accuracy','F-score','Auc-score'])\n",
    "ftdn"
   ]
  }
 ],
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