Customer Churn Prediction

A machine learning-based web application that predicts customer churn in the telecom industry using Logistic Regression.

Features

• Single Customer Prediction — Enter customer details manually and get instant churn probability
• Batch Prediction — Upload a CSV file to predict churn for multiple customers at once
• Risk Classification — Customers are categorized into Low, Medium, or High risk
• Model Analysis — View feature importance and model performance metrics

Tech Stack

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

Setup

1. Install dependencies:

   pip install -r requirements.txt
   

2. Run the app:

   streamlit run app.py
   

Model Details

• Algorithm: Logistic Regression
• Dataset: Telco Customer Churn (Kaggle) — 7043 records
• Features: 24 input features after preprocessing
• Accuracy: 79.28%