Which algorithms are used in this customer churn prediction project?

The system implements and compares two algorithms: Logistic Regression (interpretable) and Random Forest (high-performance). Random Forest achieved superior performance with 80.2% accuracy and 0.659 F1-score.

What is the accuracy of the churn prediction model?

The Random Forest model achieves 80.2% accuracy with an F1-score of 0.659 and AUC-ROC of 0.842, demonstrating strong predictive capability for customer churn detection.

Who can benefit from this churn prediction system?

Telecommunications companies, customer retention teams, marketing professionals, and business analysts can use this system to identify at-risk customers and implement proactive retention strategies.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and Matplotlib for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Customer Churn Prediction and Retention Analytics Using Machine Learning

The Customer Churn Prediction and Retention Analytics Using Machine Learning system is a comprehensive machine learning solution that predicts customer churn in telecommunications. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Logistic Regression and Random Forest with class balancing, achieving 80.2% accuracy and 0.842 AUC-ROC.

The system utilizes the Telco Customer Churn dataset containing 7,043 customer records with 21 features including demographic information, account details, and service subscriptions. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time churn prediction with probability scores, enabling telecom companies to implement proactive retention strategies.

Python 3.8+ Machine Learning Random Forest Logistic Regression Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Churn Prediction
Random Forest (80.2% Accuracy)
Logistic Regression (79.5% Accuracy)
Feature Engineering (tenure groups)
Interactive Visualizations
Real-time Churn Predictions
Model Performance Comparison
Feature Importance Analysis
Retention Analytics Dashboard
Flask Web Application

Algorithms Used

📊 Logistic Regression

Interpretable linear model with class_weight='balanced', max_iter=1000

🎯 Accuracy: 79.5%

🌲 Random Forest

Ensemble learning with n_estimators=100, max_depth=10, class_weight='balanced'

🎯 Accuracy: 80.2%

🔧 Feature Engineering

Tenure groups, binary encoding, label encoding for categorical features

📊 Top Feature: tenure

📊 Cross-Validation

5-fold stratified CV with balanced class weights

📊 CV Mean: 80.0%

Methodology & Workflow

1 Data Collection
7,043 customer records with 21 features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of churn patterns

4 Model Training
Logistic Regression and Random Forest with cross-validation

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, AUC-ROC

6 Web Deployment
Flask web app with real-time churn prediction

Model Performance Comparison

Metric	Logistic Regression	Random Forest	Best
Test Accuracy	79.5%	80.2%	Random Forest
Test Precision	66.0%	66.0%	Tie
Test Recall	57.3%	65.9%	Random Forest
Test F1-Score	61.3%	65.9%	Random Forest
Test AUC-ROC	84.3%	84.2%	Logistic Regression
CV Mean Accuracy	79.2%	80.0%	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Telco Churn Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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