Customer Churn Prediction for a Telecom Provider

Machine Learning project to predict customer churn using Logistic Regression, Decision Tree, Random Forest, and SVM.

Project Overview

Customer churn prediction helps telecom providers identify customers who are likely to leave their service. This project uses machine learning models to classify whether a customer will churn or stay based on different factors like contract type, monthly charges, and tenure.

Dataset

• Source: IBM Telco Customer Churn Dataset
• Size: 7,000+ customer records
• Target Variable: Churn (1 = Churned, 0 = Not Churned)

Tech Stack

• Programming Language: Python 🐍
• Libraries Used: pandas, numpy, sklearn, joblib, matplotlib
• Models: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM)

Exploratory Data Analysis (EDA)

✔ Data visualization of customer behavior 📊
✔ Feature correlation analysis 🔗
✔ Data preprocessing: missing values handling, encoding categorical features

Model Performance

Model	Accuracy
Logistic Regression	81.76%
Decision Tree	79.56%
Random Forest	80.06%
Support Vector Machine	81.76%

Best Models: Logistic Regression & SVM
Next Steps: Implement XGBoost and Hyperparameter Tuning.

---