https://github.com/yash22222/predicting-customer-churn-for-subscription-based-services
A Flask-based web app that predicts customer churn using ML models and suggests retention strategies.
https://github.com/yash22222/predicting-customer-churn-for-subscription-based-services
churn-analysis churn-modelling churn-prediction flask generative-ai hackathon machine-learning
Last synced: 8 months ago
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A Flask-based web app that predicts customer churn using ML models and suggests retention strategies.
- Host: GitHub
- URL: https://github.com/yash22222/predicting-customer-churn-for-subscription-based-services
- Owner: Yash22222
- License: apache-2.0
- Created: 2025-02-02T05:52:36.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-02T06:24:07.000Z (8 months ago)
- Last Synced: 2025-02-02T06:25:31.656Z (8 months ago)
- Topics: churn-analysis, churn-modelling, churn-prediction, flask, generative-ai, hackathon, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📊 Predicting Customer Churn for Subscription-Based Services
## 🚀 Project Overview
This project aims to **predict customer churn** based on various features like customer tenure, monthly charges, payment methods, and contract types. The system not only predicts churn but also **automates customer retention strategies** by sending promotional SMS and emails to at-risk customers.
### 🔥 **Why This Project?**
- **Business Impact:** Helps businesses **reduce churn** by taking proactive retention actions.
- **Automation:** Auto-generates **personalized retention offers** for high-risk customers.
- **Data-Driven:** Uses machine learning models to predict churn probabilities.
---
## 🛠️ **Tech Stack**
- **Backend:** Flask, SQLAlchemy (Database ORM)
- **Frontend:** HTML, CSS, JavaScript
- **Machine Learning:** Scikit-learn, XGBoost
- **Database:** SQLite (Can be extended to PostgreSQL, MySQL)
- **Third-Party APIs:** Twilio (for SMS notifications), Google Generative AI (for personalized retention messages)
---
## 📌 **Features**
### ✅ Customer Management
- Add new customers with **name, mobile, email, location**.
- Store customer **subscription & service details** in a structured database.
### 🔮 **Churn Prediction & Retention Strategy**
- Uses **Logistic Regression, Random Forest, and XGBoost** to predict churn.
- Identifies **high-risk customers** based on churn probability.
- Generates **personalized retention offers** using **Generative AI**.
- **Automated SMS & Email notifications** for retention campaigns.
### 📡 **Model Training & Evaluation**
- Trains machine learning models on the **Telco Customer Churn dataset**.
- Performs **feature engineering, encoding, and scaling** for better performance.
- Evaluates models using **AUC-ROC, precision-recall, and confusion matrices**.
---
## 🏗️ **Project Structure**
```
📂 customer-churn-prediction
│-- 📁 static/ # CSS & JS files for frontend
│-- 📁 templates/ # HTML templates for frontend
│-- 📁 models/ # Machine learning models
│-- 📁 database/ # SQLite database files
│-- app.py # Main Flask application
│-- db_setup.py # Database schema & table creation
│-- requirements.txt # Required Python packages
│-- README.md # Project documentation
```
---
## 🛠️ **Setup & Installation**
### 1️⃣ **Clone the repository**
```bash
git clone https://github.com/YOUR_USERNAME/customer-churn-prediction.git
cd customer-churn-prediction
```
### 2️⃣ **Install dependencies**
```bash
pip install -r requirements.txt
```
### 3️⃣ **Set up the database**
```bash
python db_setup.py
```
### 4️⃣ **Run the Flask application**
```bash
python app.py
```
Open in browser: **http://127.0.0.1:5000/**
---
## 📊 **Churn Prediction Workflow**
1️⃣ **User selects a customer** from the database.
2️⃣ **System fetches their service history** and applies the trained model.
3️⃣ **Churn probability is calculated**, and risk level (High, Medium, Low) is assigned.
4️⃣ **Retention strategy is suggested** using AI-generated offers.
5️⃣ If churn probability is **above 70%**, an **automated SMS & email** is sent to the customer.
---
## 📡 **Deployment**
Can be deployed on:
- **Heroku** (`heroku create && git push heroku main`)
- **AWS (EC2, Lambda)**
- **Render.com**
- **Google Cloud (App Engine, Cloud Run)**
---
## 📧 **Notifications & Retention Strategy**
| Risk Level | Churn Probability | Suggested Action | Automated Response |
|-------------|------------------|------------------|--------------------|
| 🚨 **High** | > 70% | Special discount offers | **SMS & Email notification** |
| 🤔 **Medium** | 50-70% | Targeted engagement | **Email notification only** |
| ✅ **Low** | < 50% | Retain customer normally | **No action needed** |
---
## 🏆 **Key Achievements**
✅ Successfully trained **3 ML models** for churn prediction.
✅ **Automated retention strategy** based on prediction results.
✅ Integrated **Twilio for SMS** and **Google AI for personalized offers**.
✅ **Web-based UI** to manage customers, records, and predictions.
---
## 💡 **Future Enhancements**
🔹 **Advanced NLP Sentiment Analysis** (Analyze customer feedback for better predictions).
🔹 **Dynamic Learning** (Update model based on new data).
🔹 **Hybrid AI Models** (Combine rule-based and deep learning approaches).
---
## 🤝 **Contributors**
👨💻 **Yash Shirsath** - Data Scientist & ML Engineer
👩💻 **Yash Chaudhary** - Gen AI & Backend Developer
👨💻 **Vedant Bhosale** - Security Analyst, DB & API Specialist
---
## 📜 **License**
This project is licensed under the **Apache License 2.0**.
---
## ⭐ **Support & Feedback**
Found this project useful? **Give it a star ⭐ on GitHub!**
For feedback & issues, open a **GitHub Issue**.
---