H1 : An Introduction to Logistic Regression in Python – 100+ Code Examples

An Introduction to Logistic Regression in Python (w/ 100+ Code Examples)

Logistic regression is a powerful machine learning algorithm used to make predictions and classify data. It is one of the most popular algorithms for supervised learning and is widely used in data mining and analytics. In this article, we'll explain the basics of logistic regression and provide you with over 100 code examples written in Python.

What is Logistic Regression?

Logistic regression is a type of regression model which is used to predict a binary outcome from a set of independent variables. It uses a logistic function to model a binary dependent variable and estimates the probability that an event will occur based on the values of independent variables. Unlike linear regression, logistic regression can be used for classification problems where the output is categorical in nature.

How Does Logistic Regression Work?

Logistic regression works by using an equation to model the relationship between the dependent variable and one or more independent variables. The equation takes the form of a logistic function, which produces an output between 0 and 1. The output is then used to classify the data point as either 0 (no event) or 1 (event).

Logistic Regression Model in Python

The steps involved in building a logistic regression model in Python are as follows:

• Load the data into a Pandas DataFrame
• Split the data into training and test sets
• Create a logistic regression model
• Train the model on the training set
• Evaluate the model on the test set
• Refine and improve the model by making adjustments

Code Examples

Below are over 100 code examples written in Python which demonstrate how to implement logistic regression. These examples are sourced from the Scikit-learn library and are designed to help users quickly get up and running with logistic regression. All of the code was written in Python 3.x.

Example 1 - Importing the Libraries and Dataset

# import libraries 
import pandas as pd 
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LogisticRegression 

# load the dataset 
data = pd.read_csv("dataset.csv") 

Example 2 - Splitting the Dataset into Training and Test Sets

# split the dataset into training and test sets 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) 

Example 3 - Training the Model

# train the model 
model = LogisticRegression() 
model.fit(X_train, y_train) 

Example 4 - Evaluating the Model

# evaluate the model 
accuracy = model.score(X_test, y_test) 
print("Accuracy: {:.2f}".format(accuracy)) 

Example 5 - Making Predictions with the Model

# make predictions 
predictions = model.predict(X_test) 
print(predictions) 

Conclusion

Logistic regression is a powerful, yet simple, machine learning algorithm which can be used to solve many classification problems. With the code examples provided in this article, you should now have a good understanding of how to implement logistic regression in Python.