3B. Decision Tree and RF .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee04daa6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------Implement the Random Forest and Decision Tree classifier--------------\n",
      "Name: Farhana Khatoon Abdul Rashid\n",
      "Roll No.21\n",
      "College Name: Vivek College of Commerce\n",
      "M.Sc(I.T.)[Sem 3]\n",
      "Date and Time: 29/11/2022 08:20:10\n",
      "======Features=======\n",
      "   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  \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",
      "\n",
      "=====Target=====\n",
      "0    1\n",
      "1    0\n",
      "2    1\n",
      "3    0\n",
      "4    1\n",
      "Name: Outcome, dtype: int64\n",
      "\n",
      "\n",
      "\n",
      "===================Creating a Random Forest Model===================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Evaluation F1-Score=> 0.6770186335403726\n",
      "Testing Set Evaluation F1-Score=> 0.5223880597014926\n",
      "\n",
      "Test Score(Accuracy):-\n",
      " 0.7480314960629921\n",
      "\n",
      "Recall:-\n",
      " 0.44871794871794873\n",
      "\n",
      "Precision:-\n",
      " 0.625\n",
      "\n",
      "=======Confusion Matrix======\n",
      "[[155  21]\n",
      " [ 43  35]]\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n",
      "C:\\Users\\Mscit6\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======Cross-validation for Random Forest======\n",
      "[0.80518519 0.80740741 0.8437037  0.69037037 0.8        0.87407407\n",
      " 0.84592593 0.88962963 0.83153846 0.85      ]\n",
      "\n",
      "\n",
      "======Classification report======\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.88      0.83       176\n",
      "           1       0.62      0.45      0.52        78\n",
      "\n",
      "    accuracy                           0.75       254\n",
      "   macro avg       0.70      0.66      0.68       254\n",
      "weighted avg       0.73      0.75      0.73       254\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "===================Creating a Decision Tree Model===================\n",
      "\n",
      "Training Set Evaluation F1-Score=> 0.7089947089947088\n",
      "Testing Set Evaluation F1-Score=> 0.5033112582781456\n",
      "\n",
      "Test Score(Accuracy):-\n",
      " 0.7047244094488189\n",
      "\n",
      "Recall:-\n",
      " 0.48717948717948717\n",
      "\n",
      "Precision:-\n",
      " 0.5205479452054794\n",
      "\n",
      "=======Confusion Matrix======\n",
      "[[141  35]\n",
      " [ 40  38]]\n",
      "\n",
      "\n",
      "======Cross-validation for Decision Tree======\n",
      "[0.78407407 0.81333333 0.77222222 0.68296296 0.69296296 0.82925926\n",
      " 0.79259259 0.85814815 0.72615385 0.80807692]\n",
      "\n",
      "\n",
      "======Classification report======\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.80      0.79       176\n",
      "           1       0.52      0.49      0.50        78\n",
      "\n",
      "    accuracy                           0.70       254\n",
      "   macro avg       0.65      0.64      0.65       254\n",
      "weighted avg       0.70      0.70      0.70       254\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
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       " Text(0.125, 0.375, 'X[5] <= 31.05\\ngini = 0.199\\nsamples = 223\\nvalue = [198, 25]'),\n",
       " Text(0.0625, 0.125, 'gini = 0.047\\nsamples = 125\\nvalue = [122, 3]'),\n",
       " Text(0.1875, 0.125, 'gini = 0.348\\nsamples = 98\\nvalue = [76, 22]'),\n",
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     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"--------------Implement the Random Forest and Decision Tree classifier--------------\")\n",
    "print(\"Name: Farhana Khatoon Abdul Rashid\")\n",
    "print(\"Roll No.21\")\n",
    "print(\"College Name: Vivek College of Commerce\")\n",
    "print(\"M.Sc(I.T.)[Sem 3]\")\n",
    "from datetime import datetime\n",
    "now=datetime.now()\n",
    "#dd/mm/yy H:M:S\n",
    "dt_string=now.strftime(\"%d/%m/%Y %H:%M:%S\")\n",
    "print(\"Date and Time:\", dt_string)\n",
    "#Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import f1_score\n",
    "#Import dataset\n",
    "data=pd.read_csv('diabetes.csv')\n",
    "data.head() #If you can to see the data you can print data\n",
    "# Select feature data\n",
    "x=data.drop('Outcome', axis=1)\n",
    "print(\"======Features=======\")\n",
    "print(x.head())\n",
    "print(\"\\n\")\n",
    "# Select target data\n",
    "y=data['Outcome']\n",
    "print(\"=====Target=====\")\n",
    "print(y.head())\n",
    "print(\"\\n\")\n",
    "\n",
    "# Use train-test-split to split the data into training data and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "# implementing train-test-split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=66)\n",
    "\n",
    "# Import cross_val_score, classification_report, and confusion_matrix\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score,recall_score, precision_score\n",
    "\n",
    "print('\\n===================Creating a Random Forest Model===================\\n')\n",
    "# random forest model creation\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=100, \n",
    "                            max_depth=3,\n",
    "                            max_features='auto', \n",
    "                            min_samples_leaf=4,\n",
    "                            bootstrap=True, \n",
    "                            n_jobs=-1, \n",
    "                            random_state=0)\n",
    "\n",
    "rfc.fit(X_train,y_train)\n",
    "# predictions\n",
    "rfc_pred_train = rfc.predict(X_train) #for training prediction\n",
    "print('Training Set Evaluation F1-Score=>',f1_score(y_train,rfc_pred_train))\n",
    "\n",
    "# generating predictions on the test set\n",
    "rfc_pred_test = rfc.predict(X_test)  #for testing prediction\n",
    "print('Testing Set Evaluation F1-Score=>',f1_score(y_test,rfc_pred_test))\n",
    "print(\"\\nTest Score(Accuracy):-\\n\", accuracy_score(y_test,rfc_pred_test))\n",
    "print(\"\\nRecall:-\\n\", recall_score(y_test,rfc_pred_test))\n",
    "print(\"\\nPrecision:-\\n\", precision_score(y_test,rfc_pred_test))\n",
    "\n",
    "# Confusion matrix \n",
    "cm=np.array(confusion_matrix(y_test,rfc_pred_test))\n",
    "print(\"\\n=======Confusion Matrix======\")\n",
    "print(cm)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Cross-validation\n",
    "rfc_cv_score = cross_val_score(rfc, x, y, cv=10, scoring='roc_auc')\n",
    "print(\"======Cross-validation for Random Forest======\")\n",
    "print( rfc_cv_score)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Classification report\n",
    "cr=classification_report(y_test,rfc_pred_test)\n",
    "print(\"======Classification report======\")\n",
    "print(cr)\n",
    "print(\"\\n\")\n",
    "\n",
    "print('\\n===================Creating a Decision Tree Model===================\\n')\n",
    "# decision tree model creation\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier(criterion=\"gini\", max_depth=3,#Max height of tree\n",
    "                             min_samples_leaf=3, #Maximum leafsamples\n",
    "                             random_state=100)\n",
    "dtc.fit(X_train,y_train)\n",
    "\n",
    "# predictions\n",
    "dtc_pred_train = dtc.predict(X_train)\n",
    "print('Training Set Evaluation F1-Score=>',f1_score(y_train,dtc_pred_train))\n",
    "\n",
    "# generating predictions on the test set\n",
    "dtc_pred_test = dtc.predict(X_test)\n",
    "print('Testing Set Evaluation F1-Score=>',f1_score(y_test,dtc_pred_test))\n",
    "print(\"\\nTest Score(Accuracy):-\\n\", accuracy_score(y_test,dtc_pred_test))\n",
    "print(\"\\nRecall:-\\n\", recall_score(y_test,dtc_pred_test))\n",
    "print(\"\\nPrecision:-\\n\", precision_score(y_test,dtc_pred_test))\n",
    "\n",
    "# Confusion matrix \n",
    "cm=confusion_matrix(y_test,dtc_pred_test)\n",
    "print(\"\\n=======Confusion Matrix======\")\n",
    "print(cm)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Cross-validation\n",
    "dtc_cv_score = cross_val_score(dtc, x, y, cv=10, scoring='roc_auc')\n",
    "print(\"======Cross-validation for Decision Tree======\")\n",
    "print( dtc_cv_score)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Classification report\n",
    "cr=classification_report(y_test,dtc_pred_test)\n",
    "print(\"======Classification report======\")\n",
    "print(cr)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Visualizing decision tree\n",
    "from sklearn import tree\n",
    "tree.plot_tree(dtc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "475772d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.875, 'Insulin <= 126.5\\ngini = 0.479\\nsamples = 323\\nvalue = [310, 204]'),\n",
       " Text(0.25, 0.625, 'Insulin <= 32.5\\ngini = 0.443\\nsamples = 238\\nvalue = [254, 126]'),\n",
       " Text(0.125, 0.375, 'Pregnancies <= 3.5\\ngini = 0.493\\nsamples = 160\\nvalue = [143, 113]'),\n",
       " Text(0.0625, 0.125, 'gini = 0.398\\nsamples = 66\\nvalue = [74, 28]'),\n",
       " Text(0.1875, 0.125, 'gini = 0.495\\nsamples = 94\\nvalue = [69, 85]'),\n",
       " Text(0.375, 0.375, 'BMI <= 45.35\\ngini = 0.188\\nsamples = 78\\nvalue = [111, 13]'),\n",
       " Text(0.3125, 0.125, 'gini = 0.129\\nsamples = 74\\nvalue = [107, 8]'),\n",
       " Text(0.4375, 0.125, 'gini = 0.494\\nsamples = 4\\nvalue = [4, 5]'),\n",
       " Text(0.75, 0.625, 'Glucose <= 153.5\\ngini = 0.487\\nsamples = 85\\nvalue = [56, 78]'),\n",
       " Text(0.625, 0.375, 'DiabetesPedigreeFunction <= 0.528\\ngini = 0.479\\nsamples = 59\\nvalue = [53, 35]'),\n",
       " Text(0.5625, 0.125, 'gini = 0.331\\nsamples = 30\\nvalue = [34, 9]'),\n",
       " Text(0.6875, 0.125, 'gini = 0.488\\nsamples = 29\\nvalue = [19, 26]'),\n",
       " Text(0.875, 0.375, 'SkinThickness <= 39.0\\ngini = 0.122\\nsamples = 26\\nvalue = [3, 43]'),\n",
       " Text(0.8125, 0.125, 'gini = 0.049\\nsamples = 22\\nvalue = [1, 39]'),\n",
       " Text(0.9375, 0.125, 'gini = 0.444\\nsamples = 4\\nvalue = [2, 4]')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import plot_tree\n",
    "rfc.estimators_[0]\n",
    "fig = plt.figure(figsize=(15, 10))\n",
    "plot_tree(rfc.estimators_[0], \n",
    "         feature_names=x.columns,\n",
    "         filled=True, impurity=True, \n",
    "         rounded=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7d2f531",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}