Mini project · Semester 5 · Computer Science and Engineering

Customer Churn Prediction Using Machine Learning

Python, Flask, Machine Learning, Scikit-learn, Pandas, NumPy, SQLite, HTML, CSS, Bootstrap, Joblib

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THE IDEA

About this project

Customer Churn Prediction Using Machine Learning is a web-based ML application designed to predict whether a customer is likely to leave a service or continue as an existing customer. The system collects customer-related information such as tenure, contract type, internet service, monthly charges and payment method, processes the information using a trained Logistic Regression model, and displays the predicted churn status along with churn probability. The application also maintains prediction history and provides a simple dashboard for analyzing previous predictions.

What this project does

  • Customer churn prediction, Churn probability prediction, Customer details input form, Machine Learning model integration, Automatic data preprocessing, Churn/No Churn result, Prediction history, SQLite database, Prediction dashboard, Responsive web interface.

What you need

Python 3.10 or above, pip, Flask, Pandas, NumPy, Scikit-learn, Joblib, SQLite, Modern web browser. VS Code is recommended for running and editing the project.

How to run this project

Extract the project ZIP and open the project folder in VS Code. Open Terminal and run: python -m venv venv venv\Scripts\activate pip install -r requirements.txt python train_model.py python app.py Open the application in your browser at: http://127.0.0.1:5000/ Run python train_model.py before starting the Flask application for the first time.

Learn, build and make it yours

Read the code, try the features and change the project to match your idea. Make sure you understand how it works before using it for your coursework.

DOWNLOAD & START BUILDING

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