PREDICTING CUSTOMER CHURN USING LOGISTIC REGRESSION IN PYTHON: END-TO-END MACHINE LEARNING PIPELINE

1. 1Import necessary libraries and load the Telco Customer Churn dataset
2. 2Preprocess the data by handling missing values and encoding categorical variables
3. 3Split the data into training and testing sets
4. 4Train a logistic regression model on the training data
5. 5Evaluate the model's performance using metrics such as accuracy and ROC-AUC
6. 6Use the model to make predictions on the testing data and identify high-risk customers

Data scientists and analysts can use this pipeline to identify high-risk customers and develop targeted retention strategies, while product managers can use the insights to inform product development and improvement.

💡 Logistic regression can be used to predict customer churn by analyzing historical data and identifying patterns and correlations.

Predict customer churn with logistic regression in Python!