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project_17_compare_classification_algorithms/compare_classification_algos.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Dec 19 17:30:09 2018
+
+@author: omairaasim
+"""
+
+# 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])
+
+
+# Step 3 - Feature Scaling
+from sklearn.preprocessing import StandardScaler
+sc = StandardScaler()
+X = sc.fit_transform(X)
+
+# Step 4 - Compare Classification Algorithms
+from sklearn.model_selection import KFold
+from sklearn.model_selection import cross_val_score
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.svm import SVC
+
+classification_models = []
+classification_models.append(('Logistic Regression', LogisticRegression(solver="liblinear")))
+classification_models.append(('K Nearest Neighbor', KNeighborsClassifier(n_neighbors=5, metric="minkowski",p=2)))
+classification_models.append(('Kernel SVM', SVC(kernel = 'rbf',gamma='scale')))
+classification_models.append(('Naive Bayes', GaussianNB()))
+classification_models.append(('Decision Tree', DecisionTreeClassifier(criterion = "entropy")))
+classification_models.append(('Random Forest', RandomForestClassifier(n_estimators=100, criterion="entropy")))
+
+for name, model in classification_models:
+  kfold = KFold(n_splits=10, random_state=7)
+  result = cross_val_score(model, X, y, cv=kfold, scoring='accuracy')
+  print("%s: Mean Accuracy = %.2f%% - SD Accuracy = %.2f%%" % (name, result.mean()*100, result.std()*100))