User Retention Analytics Project

Project Overview

This project analyzes user retention and churn behavior for a simulated online learning platform.
Using Python and data analytics techniques, the project explores engagement patterns and builds a predictive churn model.

Objectives

• Simulate user activity data
• Analyze cohort-based retention
• Explore engagement metrics
• Build a churn prediction model

Dataset Features

• user_id
• signup_date
• lessons_completed
• weekly_sessions
• churn_probability
• churned

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

Analysis Performed

Visualizations

Cohort Retention Analysis

Retention rates were calculated based on signup month cohorts.

Engagement vs Churn

User engagement metrics were compared with churn outcomes.

Churn Prediction Model

A logistic regression model was trained using:

• Lessons completed
• Weekly sessions

Model Accuracy: 0.71

Example prediction:

User behavior:

• Lessons completed: 3
• Weekly sessions: 1

Predicted churn probability:54.45%

Project Structure

Retention_Analytics_Project

1. project2.py
2. user_dataset.csv
3. README.md
4. visuals 4.1 cohort_retention.png 4.2engagement_scatter.png
5. report

Key Insights

• Higher weekly engagement reduces churn risk
• Cohort retention varies across signup periods
• Logistic regression provides a baseline churn prediction model