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‎Random Forest Classification.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"#import libabry\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn import metrics\n",
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"from sklearn.ensemble import RandomForestClassifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"#import dataset\n",
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"Diabetes = pd.read_csv('~/Downloads/Data Science/data set/Diabetes.csv') "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Pregnancies</th>\n",
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" <th>Glucose</th>\n",
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" <th>BloodPressure</th>\n",
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" <th>SkinThickness</th>\n",
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" <th>Insulin</th>\n",
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" <th>BMI</th>\n",
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" <th>DiabetesPedigreeFunction</th>\n",
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" <th>Age</th>\n",
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" <th>Outcome</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <td>35</td>\n",
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" <td>0</td>\n",
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" <td>33.6</td>\n",
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" <td>0.627</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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"</table>\n",
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"<p>768 rows ×ばつ 9 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
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"0 6 148 72 35 0 33.6 \n",
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"1 1 85 66 29 0 26.6 \n",
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"2 8 183 64 0 0 23.3 \n",
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"3 1 89 66 23 94 28.1 \n",
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"4 0 137 40 35 168 43.1 \n",
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".. ... ... ... ... ... ... \n",
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"763 10 101 76 48 180 32.9 \n",
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"764 2 122 70 27 0 36.8 \n",
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"765 5 121 72 23 112 26.2 \n",
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"766 1 126 60 0 0 30.1 \n",
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"767 1 93 70 31 0 30.4 \n",
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"\n",
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" DiabetesPedigreeFunction Age Outcome \n",
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"0 0.627 50 1 \n",
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"1 0.351 31 0 \n",
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"2 0.672 32 1 \n",
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"3 0.167 21 0 \n",
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"4 2.288 33 1 \n",
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".. ... ... ... \n",
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"763 0.171 63 0 \n",
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"764 0.340 27 0 \n",
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"765 0.245 30 0 \n",
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"766 0.349 47 1 \n",
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"767 0.315 23 0 \n",
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"\n",
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"[768 rows x 9 columns]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Diabetes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"colnames = list(Diabetes.columns)\n",
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"predictors = colnames[:8]\n",
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"target = colnames[8]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Split dataset\n",
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"X_train, X_test, y_train, y_test = train_test_split(Diabetes[predictors],Diabetes[target],test_size=0.3, random_state=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"#Import Random Forest Model\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"\n",
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"#Create a Gaussian Classifier\n",
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"clf=RandomForestClassifier(n_estimators=100)\n",
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"\n",
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"#Train the model using the training\n",
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"clf.fit(X_train,y_train)\n",
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"\n",
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"y_pred=clf.predict(X_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy: 0.7835497835497836\n"
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]
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}
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],
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"source": [
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"# Model Accuracy\n",
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"print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"version": "3.6.8"
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}
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"nbformat": 4,
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"nbformat_minor": 4
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}

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