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Author: omairaasim

project_16_random_forest_classifier/random_forest_classifier.py

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+# -*- coding: utf-8 -*-
+"""
+Spyder Editor
+
+This is a temporary script file.
+"""
+
+
+# Step 1 - Load Data
+import pandas as pd
+dataset = pd.read_csv("iphone_purchase_records.csv")
+X = dataset.iloc[:,:-1].values
+y = dataset.iloc[:, 3].values
+
+# Step 2 - Convert Gender to number
+from sklearn.preprocessing import LabelEncoder
+labelEncoder_gender =  LabelEncoder()
+X[:,0] = labelEncoder_gender.fit_transform(X[:,0])
+
+# Optional - if you want to convert X to float data type
+import numpy as np
+X = np.vstack(X[:, :]).astype(np.float)
+
+# Step 3 - Split Data
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
+
+
+# Step 4 - Fit Classifier
+from sklearn.ensemble import RandomForestClassifier
+classifier = RandomForestClassifier(n_estimators=100, criterion="entropy", random_state=0)
+classifier.fit(X_train, y_train)
+
+# Step 5 - Predict
+y_pred = classifier.predict(X_test)
+
+# Step 6 - Metrics
+#from sklearn import metrics
+#cm = metrics.confusion_matrix(y_test, y_pred) ## 5,3 errors
+#accuracy = metrics.accuracy_score(y_test, y_pred)  ## 0.92
+#precision = metrics.precision_score(y_test, y_pred)  ## 0.85
+#recall = metrics.recall_score(y_test, y_pred)  ## 0.90
+
+# Step 6 - Evaluate the model performance
+from sklearn import metrics
+cm = metrics.confusion_matrix(y_test, y_pred) 
+print(cm)
+accuracy = metrics.accuracy_score(y_test, y_pred) 
+print("Accuracy score:",accuracy)
+precision = metrics.precision_score(y_test, y_pred) 
+print("Precision score:",precision)
+recall = metrics.recall_score(y_test, y_pred) 
+print("Recall score:",recall)
+