Merge pull request #1576 from Stuti333/feature/Stuti

Adding Customer Churn Prediction Model

Machine Learning and Data Science/Intermediate/Customer Churn Prediction/README.md

@@ -0,0 +1,90 @@
+# Customer Churn Prediction
+
+## Overview
+This project implements a machine learning solution to predict customer churn using three different algorithms: Random Forest, XGBoost, and Logistic Regression. The model helps identify customers who are likely to discontinue services, enabling proactive retention strategies.
+
+## Table of Contents
+- [Features](#features)
+- [Requirements](#requirements)
+- [Project Structure](#project-structure)
+- [Installation](#installation)
+- [Model Comparison](#model-comparison)
+
+## Features
+- Data preprocessing and feature engineering
+- Implementation of three machine learning algorithms:
+  - Random Forest Classifier
+  - XGBoost Classifier
+  - Logistic Regression
+- Model performance comparison and evaluation
+- Feature importance analysis
+- Cross-validation for robust model validation
+
+## Requirements
+```
+python>=3.8
+pandas
+numpy
+scikit-learn
+xgboost
+matplotlib
+seaborn
+```
+
+## Project Structure
+```
+customer-churn-prediction/
+│
+├── data/
+│   ├── Churn_Modelling.csv
+│  
+├── saved models/
+│   ├── Gradient_Boosting_Classifier.joblib
+│   ├── scaler.joblib
+│  
+├── notebooks/
+│   └── Model.ipynb
+│
+└── README.md
+```
+
+## Installation
+1. Clone the repository:
+```bash
+git clone https://github.com/yourusername/customer-churn-prediction.git
+cd customer-churn-prediction
+```
+
+2. Create a virtual environment and activate it:
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install required packages:
+```bash
+pip install -r requirements.txt
+```
+
+## Model Comparison
+
+### Performance Metrics
+
+| Model               | Accuracy | Precision | 
+|--------------------|----------|-----------|
+| Random Forest      | 0.87     | 0.83      | 
+| XGBoost            | 0.88     | 0.86      | 
+| Logistic Regression| 0.82     | 0.77      | 
+
+### Key Findings
+- XGBoost performed best overall with highest accuracy and AUC-ROC scores
+- Random Forest showed comparable performance with slightly lower metrics
+- Logistic Regression provided a good baseline but was outperformed by both ensemble methods
+
+Threshold vs Recall and Threshold vs Precision graph (XGBoost)
+![image](https://github.com/user-attachments/assets/42be4ba5-052d-4e7c-8c16-be57bc929d80)
+
+ROC Curve
+![{05BAA722-2B5D-466B-94C4-5ECB09D9A904}](https://github.com/user-attachments/assets/3a3cacb5-15e2-4876-bf49-94d3d3515866)
+
+