David-Bamidele/Expresso-Customer-Churn-Project

This is a project that predicts the customer churn of an African telecommunications company-Expresso --- # Expresso Churn Prediction Project This project focuses on predicting customer churn for Expresso, a fictional telecommunications company. By analyzing customer data, the model aims to identify which customers are likely to stop using the service, allowing the company to implement retention strategies proactively. ## Project Overview The **Expresso Churn Prediction App** is built using Python and leverages **Streamlit** to create an interactive and user-friendly interface. The project includes data preprocessing, model training, and a web app for deployment. This solution is designed to help Expresso make data-driven decisions to improve customer retention. ## Features - **Data Preprocessing**: Cleans and prepares raw customer data for model training. - **Machine Learning Model**: A predictive model trained on historical data to identify potential churn. - **Interactive Web Application**: Built with Streamlit, allowing users to interact with the model and view churn predictions. ## Usage - **Load Customer Data**: The app allows users to upload customer data for churn prediction. - **View Predictions**: The model predicts the likelihood of churn for each customer. - **Analyze Results**: Use the insights provided by the app to strategize retention efforts. ## Technologies Used - **Python** - **Streamlit** - **Joblib** for model serialization - **scikit-learn** for model development ## Project Structure - `Expresso_Churn_Prediction_Streamlit_App.py`: The main application file for Streamlit. - `requirements.txt`: Lists the dependencies required to run the app. - `Expresso_Churn_main.csv`: The dataset that was used for this project ## License This project is licensed under the MIT License. ---