Compare logistic regression with decision tree along with a case study in Python

First we import the data

# Importing the libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

# Importing the dataset

dataset = pd.read_csv('Social_Network_Ads.csv')

X = dataset.iloc[:, [2, 3]].values

y = dataset.iloc[:, 4].values

Now we split and scale the data

# Splitting the dataset into the Training set and Test set

from sklearn.cross_validation import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

# Feature Scaling

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

Now we fit to the logistic classification and decision tree classification

# Fitting Logistic Regression and Decision tree to the Training set

from sklearn.linear_model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

classifier1 = LogisticRegression(random_state = 0)

classifier 1.fit(X_train, y_train)

classifier2=DecisionTreeClassifier()

classifier 2.fit(X_train, y_train)

Now we predict both the models

# Predicting the Test set results

y_pred1 = classifier1.predict(X_test)

y_pred2 = classifier2.predict(X_test)

 Now we evaluate both the model

# Making the Confusion Matrix

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test, y_pred1)

cm = confusion_matrix(y_test, y_pred2)

Compared to the confusion matrix, Decision tree performed better than logistic regression