🚨 Churn Prediction System
🔍 About the Task
In this hands-on internship project, you’ll develop a Churn Prediction System that identifies which customers are likely to stop using a service. This kind of model is crucial in industries like telecom, SaaS, and banking where retaining customers is more profitable than acquiring new ones.
You’ll use real customer data to build, train, and evaluate a machine learning model—and then present your findings with an analytics dashboard or a report that a business decision-maker could act on.
✅ What You’ll Do
- Explore and clean a customer dataset (e.g., telecom, banking, or subscription data).
- Engineer relevant features like contract type, payment method, or service usage.
- Train and test classification models (Logistic Regression, Random Forest, XGBoost).
- Evaluate model accuracy, precision, recall, and churn probabilities.
- Visualize key churn drivers and insights using Power BI or Matplotlib.
- Present your results in a business-focused dashboard or PDF report.
🎯 Skills You’ll Gain
🧠 Classification & predictive modeling
🧹 Data wrangling and feature engineering
📊 Confusion matrix, ROC-AUC, precision-recall analysis
📈 Business storytelling with charts and dashboards
📉 Risk segmentation & churn probability analysis
🛠️ Tools You’ll Use
- Python – Primary language for data analysis and modeling
- Pandas & Scikit-learn – For data prep and ML
- XGBoost – Advanced classification model
- Power BI or Matplotlib – For dashboard or report visuals
- Optional: Streamlit – Turn your model into a live web app
📁 Sample Datasets to Use
- 📱 Telco Customer Churn Dataset (Kaggle)
- 🏦 Bank Customer Churn Dataset (Kaggle)
- 🎧 Spotify User Churn Simulation – Simulated data for music platforms
📺 YouTube Tutorial to Get Started
🔗 Video: Customer Churn Prediction using Machine Learning
This video explains the complete flow from data cleaning to model building and churn visualization.
🔑 Key Features to Include
✔ Model that predicts churn probability per customer
✔ Feature importance chart (which factors drive churn)
✔ Confusion matrix and evaluation metrics
✔ Dashboard or PDF report summarizing business insights
✔ (Optional) Interactive UI with Streamlit to demo your system