Perform Logistic Regression in Python

Perform Logistic Regression in Python

The following tutorial demonstrates how to perform logistic regression on Python.

Let us download a sample dataset to get started with. We will use a user dataset containing information about the user’s gender, age, and salary and predict if a user will eventually buy the product.

Take a look at our dataset.

data set

We will now start creating our model by importing pertinent libraries such as pandas, numpy and matplotlib.

Perform Logistic Regression in Python

Importing pertinent libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Let us import our dataset using pandas.

Reading dataset:

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

We will now select the Age and Estimated salary features from our dataset to train our model to predict if a user purchases a product or not. Here, gender and user id won’t play a significant role in predicting; we ignore them in the training process.

x = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

Let’s split the dataset into training and testing data. We divide them into 75% for training the model and the rest 25% for testing the model’s performance.

We do this using train_test_split function in sklearn library.

from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(
		x, y, test_size = 0.25, random_state = 0)

We perform the feature scaling process since the Age and Salary features lie in a different range. This is essential since one feature can dominate the other while the training process is avoided.

from sklearn.preprocessing import StandardScaler
sc_x = StandardScaler()
xtrain = sc_x.fit_transform(xtrain)
xtest = sc_x.transform(xtest)

Both the features lie in the range from -1 to 1, which will ensure both features contribute equally to decision-making (i.e., predicting process). Let us take a look at updated features.

print (xtrain[0:10, :])
[[ 0.58164944 -0.88670699]
 [-0.60673761  1.46173768]
 [-0.01254409 -0.5677824 ]
 [-0.60673761  1.89663484]
 [ 1.37390747 -1.40858358]
 [ 1.47293972  0.99784738]
 [ 0.08648817 -0.79972756]
 [-0.01254409 -0.24885782]
 [-0.21060859 -0.5677824 ]
 [-0.21060859 -0.19087153]]

Let us finally train our model; in our case, we will use the logistic regression model, which we will import from the sklearn library.

from sklearn.linear_model import LogisticRegression
classifier1 = LogisticRegression(random_state = 0)
classifier1.fit(xtrain, ytrain)

Since we have now trained our model, let us do the prediction on our testing data to evaluate our model.

y_pred = classifier1.predict(xtest)

Let us now create a confusion matrix based on our testing data and the predictions we obtained in the last procedure.

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(ytest, y_pred)

print ("Confusion Matrix : \n", cm)
Confusion Matrix :
 [[65  3]
 [ 8 24]]

Let us calculate the accuracy of our model using the sklearn library.

from sklearn.metrics import accuracy_score
print ("Accuracy score : ", accuracy_score(ytest, y_pred))
Accuracy score :  0.89

We got a satisfying accuracy score of 0.89 from our model, which signifies that our model can very well predict if a user will buy a product or not.

Thus, we can successfully perform logistic regression using Python with the above method.

Preet Sanghavi avatar Preet Sanghavi avatar

Preet writes his thoughts about programming in a simplified manner to help others learn better. With thorough research, his articles offer descriptive and easy to understand solutions.

LinkedIn GitHub

Related Article - Python Math

  • Calculate Factorial in Python
  • Calculate Inverse of Cosine in Python
  • Calculate Modular Multiplicative Inverse in Python
  • Fit Poisson Distribution to Different Datasets in Python
  • Reduce Fractions in Python
  • Define an Infinite Value in Python