Gradient Boosting Classifier Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e8ff957",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the required packages\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "plt.rcParams['figure.figsize'] = [30,15]\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.tree import plot_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68b026a0",
   "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>Pregnant</th>\n",
       "      <th>Plasma glucose</th>\n",
       "      <th>blood pressure</th>\n",
       "      <th>Triceps</th>\n",
       "      <th>serum insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes pedigree function</th>\n",
       "      <th>Age (years)</th>\n",
       "      <th>Class</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",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnant  Plasma glucose  blood pressure  Triceps  serum 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",
       "   Diabetes pedigree function  Age (years)  Class  \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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "names = ['Pregnant','Plasma glucose','blood pressure' ,'Triceps','serum insulin','BMI','Diabetes pedigree function','Age (years)','Class']\n",
    "df = pd.read_csv(\"pima-indians-diabetes.csv\", names=names, index_col=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8df00358",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the columns required and splitting into feature columns & target column\n",
    "model_names = ['Pregnant','Plasma glucose','blood pressure' ,'Triceps','serum insulin','BMI','Diabetes pedigree function','Age (years)']\n",
    "X = df[model_names]\n",
    "y = df['Class']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "476a1b42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.82\n"
     ]
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier()\n",
    "gbc.fit(X_train, y_train)\n",
    "y_pred = gbc.predict(X_test)\n",
    "print('Accuracy: {:.2f}'.format(gbc.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9a2d523",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/prabhatm/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example of the output using input\n",
    "gbc.predict([[5,150, 66, 0,0,33.6,0.165,40]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e118e05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y, gbc.predict(X))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.imshow(cm)\n",
    "ax.grid(False)\n",
    "ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\n",
    "ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\n",
    "ax.set_ylim(1.5, -0.5)\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax.text(j, i, cm[i, j], ha='center', va='center', color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab5c362a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.87      0.87       107\n",
      "           1       0.70      0.70      0.70        47\n",
      "\n",
      "    accuracy                           0.82       154\n",
      "   macro avg       0.79      0.79      0.79       154\n",
      "weighted avg       0.82      0.82      0.82       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Displaying the classification report\n",
    "from sklearn.metrics import classification_report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c02b5005",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}