README.md

---
title: ML Demo - Churn & Recommendations
emoji: 📈
colorFrom: pink
colorTo: yellow
sdk: streamlit
sdk_version: 1.55.0
python_version: '3.10'
app_file: app.py
pinned: true
short_description: ml demo
---

# ML Demo - Customer Churn & Product Recommendations

A comprehensive machine learning demo showcasing:

## Features

### Customer Churn Prediction
- Exploratory Data Analysis (EDA)
- Multiple ML models: Logistic Regression, Random Forest, XGBoost, Naive Bayes
- SHAP explanations for model interpretability
- Live model updates with streaming data

### Product Recommendation System
- Multiple recommendation algorithms: SVD, ALS, SGD (Funk SVD), NMF, Item-Based CF
- Evaluation metrics: Precision@K, Recall@K, Hit Rate, RMSE, R²
- Interactive product recommendations
- Live recommendation updates

## Tech Stack
- Streamlit for the web interface
- scikit-learn, XGBoost for ML models
- implicit library for ALS recommendations
- SHAP for model explanations
- Pandas, NumPy for data processing

## Dataset
- Telco Customer Churn dataset
- Online Retail dataset (UCI ML Repository)