Customer Churn Prediction

Overview

This project predicts whether a customer will churn using machine learning. It is an end-to-end project with data preprocessing, model training, and a Streamlit web application.

Project Structure

Customer-Churn-Prediction/ ├── app.py ├── train_model.py ├── models/ │ ├── model.pkl │ ├── scaler.pkl │ └── encoder.pkl ├── data/ │ └── customer_churn.csv ├── requirements.txt ├── README.md ├── .gitignore

Dataset

The dataset used is the Telco Customer Churn dataset.

Model

Random Forest Classifier with SMOTE for class imbalance.

Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Streamlit
• Matplotlib
• Seaborn
• Imbalanced-learn (SMOTE)

How to Run the Project

1. Install dependencies:

pip install -r requirements.txt

2. Run the Streamlit app:

streamlit run app.py

Output

The application predicts whether a customer is likely to churn or not.

Author

Pranav Chauhan