Customer Churn Prediction using Machine Learning Techniques

This project focuses on predicting customer churn using advanced machine learning algorithms. By analyzing historical customer data, we aim to identify patterns that indicate potential churn and help businesses retain valuable customers.

Features

• Predicts which customers are likely to churn based on historical data.
• Uses machine learning models such as Random Forest, Gradient Boosting, and Neural Networks.
• Visualizes key factors influencing customer churn.
• Provides actionable insights to reduce churn and improve customer retention.

Installation

To set up the customer churn prediction system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/customer-churn-prediction.git
   cd customer-churn-prediction

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To start the churn prediction analysis, run the main script with your dataset:

python churn_prediction.py --input