Project Title

A brief, one-line description of what this project does and why it matters.

What is this?

A short paragraph explaining the purpose of this project. Keep it focused and clear. For example: "This is a lightweight utility that converts CSV files to JSON format with automatic type detection. It's designed for quick data transformation tasks in Node.js environments."

Installation

# If it's a package
npm install your-package-name

# Or if it's a script
git clone https://github.com/username/repo.git
cd repo

Usage

Basic Example

# Command-line usage
node script.js input.txt

# Or as a module
npm run start

Example with Options

const myTool = require('./tool');

// Simple usage
myTool.process('input.txt');

// With configuration
myTool.process('input.txt', { format: 'json', pretty: true });

Configuration

If there are any simple settings, list them here:

• Option A: What this option controls (default: value)
• Option B: What this option controls (default: value)
• Option C: What this option controls (default: value)

Configuration File Example

{
  "optionA": true,
  "optionB": "value",
  "timeout": 5000
}

Common Use Cases

• Use Case 1: Brief description of when you'd use this
• Use Case 2: Another practical application
• Use Case 3: Yet another scenario

Troubleshooting

Problem: Common issue description Solution: How to fix it

Problem: Another common issue Solution: The fix for this one

Requirements

• Node.js 14+ / Python 3.8+ / etc.
• Any other dependencies

License

MIT - Feel free to use this however you'd like.

Project Name

A comprehensive description of the library that clearly explains what problem it solves. For example: "A type-safe HTTP client for TypeScript that provides automatic request/response validation, intelligent caching, and seamless error handling. Built for modern web applications that need reliability and developer experience."

Why Use This?

Explain the key value propositions:

• Problem it solves: Traditional HTTP clients require extensive boilerplate for type safety
• How it's different: Automatic TypeScript inference and runtime validation
• Who it's for: Teams building production TypeScript applications

Features

• 🚀 Fast: Optimized for performance with intelligent request batching
• 📦 Lightweight: Zero dependencies, only 4KB gzipped
• 🔧 Fully Typed: Written in TypeScript with comprehensive type definitions
• 🛡️ Safe: Runtime validation using Zod schemas
• 🔄 Smart Caching: Automatic response caching with configurable TTL
• 🎯 Intuitive API: Fluent interface design for better DX
• 📱 Universal: Works in Node.js, browsers, and edge environments

Installation

# npm
npm install my-cool-package

# yarn
yarn add my-cool-package

# pnpm
pnpm add my-cool-package

Quick Start

Here's a 30-second introduction to get you up and running:

import { createClient } from 'my-cool-package';

// Create a client instance
const client = createClient({
  baseURL: 'https://api.example.com',
  timeout: 5000
});

// Make a request
const data = await client.get('/users/123');
console.log(data);

Detailed Usage

Basic Requests

// GET request
const user = await client.get('/users/123');

// POST request
const newUser = await client.post('/users', {
  body: { name: 'John Doe', email: 'john@example.com' }
});

// PUT request
const updated = await client.put('/users/123', {
  body: { name: 'Jane Doe' }
});

// DELETE request
await client.delete('/users/123');

Advanced Configuration

const client = createClient({
  baseURL: 'https://api.example.com',
  timeout: 10000,
  headers: {
    'Authorization': 'Bearer token',
    'Custom-Header': 'value'
  },
  retry: {
    attempts: 3,
    delay: 1000
  },
  cache: {
    enabled: true,
    ttl: 60000 // 1 minute
  }
});

Type-Safe Requests with Validation

import { z } from 'zod';

// Define your schema
const UserSchema = z.object({
  id: z.number(),
  name: z.string(),
  email: z.string().email()
});

// Type-safe request
const user = await client.get('/users/123', {
  schema: UserSchema
});
// user is now typed as { id: number; name: string; email: string }

Error Handling

try {
  const data = await client.get('/users/123');
} catch (error) {
  if (error.isNetworkError) {
    console.error('Network failed:', error.message);
  } else if (error.isValidationError) {
    console.error('Response validation failed:', error.details);
  } else {
    console.error('Request failed:', error.status, error.message);
  }
}

API Reference

createClient(config: ClientConfig): Client

Creates a new HTTP client instance.

Parameters:

• config.baseURL (string): Base URL for all requests
• config.timeout (number, optional): Request timeout in milliseconds (default: 30000)
• config.headers (object, optional): Default headers for all requests
• config.retry (object, optional): Retry configuration
• config.cache (object, optional): Cache configuration

Returns: Client instance

client.get(path: string, options?: RequestOptions)

Performs a GET request.

Parameters:

• path (string): Request path (relative to baseURL)
• options.schema (Schema, optional): Validation schema
• options.headers (object, optional): Request-specific headers
• options.params (object, optional): Query parameters

Returns: Promise resolving to the response data

client.post(path: string, options?: RequestOptions)

Performs a POST request with the same options as GET, plus:

• options.body (any): Request body (automatically serialized)

Examples

Real-World Example: User Management API

import { createClient } from 'my-cool-package';
import { z } from 'zod';

// Define schemas
const UserSchema = z.object({
  id: z.number(),
  name: z.string(),
  email: z.string().email(),
  role: z.enum(['admin', 'user'])
});

const UsersListSchema = z.array(UserSchema);

// Create client
const api = createClient({
  baseURL: 'https://api.myapp.com',
  headers: {
    'Authorization': `Bearer ${process.env.API_TOKEN}`
  }
});

// Fetch all users
async function getUsers() {
  return await api.get('/users', {
    schema: UsersListSchema,
    params: { limit: 100, sort: 'name' }
  });
}

// Create a new user
async function createUser(name: string, email: string) {
  return await api.post('/users', {
    schema: UserSchema,
    body: { name, email, role: 'user' }
  });
}

// Update user
async function updateUser(id: number, updates: Partial<User>) {
  return await api.put(`/users/${id}`, {
    schema: UserSchema,
    body: updates
  });
}

Comparison with Alternatives

Feature	my-cool-package	axios	fetch	ky
TypeScript Support	✅ Built-in	⚠️ Via types	⚠️ Via types	✅ Built-in
Runtime Validation	✅ Yes	❌ No	❌ No	❌ No
Auto Retry	✅ Yes	⚠️ Via plugin	❌ No	✅ Yes
Bundle Size	4KB	14KB	0KB (native)	3KB
Browser Support	✅ Modern	✅ All	✅ Modern	✅ Modern

Performance

Benchmarks on M1 Mac, Node.js 20:

• Simple GET: ~2ms
• With validation: ~3ms
• Cached request: ~0.1ms

Browser Compatibility

• Chrome/Edge 90+
• Firefox 88+
• Safari 14+
• Node.js 14+

Migration Guide

From Axios

// Before (axios)
const response = await axios.get('https://api.example.com/users');
const data = response.data;

// After (my-cool-package)
const client = createClient({ baseURL: 'https://api.example.com' });
const data = await client.get('/users');

From Fetch

// Before (fetch)
const response = await fetch('https://api.example.com/users');
const data = await response.json();

// After (my-cool-package)
const client = createClient({ baseURL: 'https://api.example.com' });
const data = await client.get('/users');

Contributing

We welcome contributions! Here's how to get started:

Development Setup

1. Fork and clone the repository

   git clone https://github.com/yourusername/my-cool-package.git
   cd my-cool-package

2. Install dependencies

   npm install

3. Run tests

   npm test

4. Start development mode

   npm run dev

Pull Request Process

1. Create a feature branch (git checkout -b feature/amazing-feature)
2. Make your changes and add tests
3. Ensure all tests pass (npm test)
4. Update documentation if needed
5. Commit your changes (git commit -m 'Add amazing feature')
6. Push to your fork (git push origin feature/amazing-feature)
7. Open a Pull Request

Code Style

We use ESLint and Prettier. Run npm run lint to check your code.

FAQ

Q: Can I use this in a browser? A: Yes! It works in all modern browsers.

Q: Does it support authentication? A: Yes, through headers and interceptors.

Q: Is it production-ready? A: Yes, used by 500+ companies in production.

Changelog

See CHANGELOG.md for version history.

License

Distributed under the MIT License. See LICENSE for more information.

Support

• 📧 Email: support@example.com
• 💬 Discord: Join our community
• 🐛 Issues: GitHub Issues
• 📖 Docs: Full Documentation

Acknowledgments

• Inspired by axios and ky
• Built with Zod
• Thanks to all our contributors!

Backend API Service

A production-ready RESTful API service built with modern best practices. This service provides user authentication, data management, and integrates with third-party services. Designed for scalability, security, and maintainability.

🎯 Overview

This is a comprehensive backend API that handles:

• User Management: Registration, authentication, profile management
• Data Operations: CRUD operations with validation and authorization
• File Uploads: Secure file handling with S3 storage
• Real-time Updates: WebSocket support for live data
• Background Jobs: Queue-based task processing
• Monitoring: Health checks, metrics, and logging

Live API: https://api.example.com
Documentation: https://api.example.com/docs
Status Page: https://status.example.com

🏗 Architecture

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│   Client    │────▶│  API Server  │────▶│  Database   │
│  (React)    │     │  (Node.js)   │     │ (Postgres)  │
└─────────────┘     └──────────────┘     └─────────────┘
                            │
                            ├────▶ Redis Cache
                            ├────▶ S3 Storage
                            └────▶ Queue (Bull)

🛠 Tech Stack

Core

• Runtime: Node.js 20 LTS
• Framework: Express.js 4.18 / NestJS 10
• Language: TypeScript 5.0

Database & Caching

• Primary Database: PostgreSQL 15
• ORM: Prisma / TypeORM
• Caching: Redis 7
• Search: Elasticsearch (optional)

Infrastructure

• Containerization: Docker & Docker Compose
• Process Manager: PM2
• Reverse Proxy: Nginx
• Cloud: AWS / GCP / DigitalOcean

Development

• Testing: Jest, Supertest
• Linting: ESLint, Prettier
• CI/CD: GitHub Actions
• API Docs: Swagger/OpenAPI

📋 Prerequisites

Before you begin, ensure you have:

• Node.js: v20.0.0 or higher (Download)
• Docker: v24.0+ and Docker Compose v2.20+ (Install)
• PostgreSQL: v15+ (or use Docker)
• Redis: v7+ (or use Docker)

Optional:

• Make: For convenience commands
• AWS CLI: If deploying to AWS

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/yourorg/api-service.git
cd api-service

2. Environment Configuration

Copy the example environment file and configure it:

cp .env.example .env

Edit .env with your settings (see Environment Variables section).

3. Start Dependencies with Docker

# Start PostgreSQL and Redis
docker-compose up -d postgres redis

# Verify services are running
docker-compose ps

4. Install Dependencies

npm install

5. Database Setup

# Run migrations
npm run migrate

# Seed database with sample data
npm run seed

6. Start Development Server

npm run dev

API should now be running at http://localhost:3000

Visit http://localhost:3000/docs for interactive API documentation.

🔐 Environment Variables

Create a .env file in the root directory:

Server Configuration

# Server
NODE_ENV=development
PORT=3000
API_VERSION=v1

# Logging
LOG_LEVEL=info
LOG_FORMAT=json

Database

# PostgreSQL
DATABASE_URL=postgresql://user:password@localhost:5432/mydb?schema=public
DB_HOST=localhost
DB_PORT=5432
DB_NAME=mydb
DB_USER=myuser
DB_PASSWORD=supersecretpassword

# Connection Pool
DB_POOL_MIN=2
DB_POOL_MAX=10

Authentication

# JWT Configuration
JWT_SECRET=your-super-secret-jwt-key-change-this-in-production
JWT_EXPIRY=7d
REFRESH_TOKEN_EXPIRY=30d

# OAuth (Optional)
GOOGLE_CLIENT_ID=your-google-client-id
GOOGLE_CLIENT_SECRET=your-google-client-secret
GITHUB_CLIENT_ID=your-github-client-id
GITHUB_CLIENT_SECRET=your-github-client-secret

Redis

REDIS_URL=redis://localhost:6379
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=
REDIS_TLS=false

External Services

# AWS (for file uploads)
AWS_REGION=us-east-1
AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key
AWS_S3_BUCKET=your-bucket-name

# Email Service (SendGrid)
SENDGRID_API_KEY=your-sendgrid-key
FROM_EMAIL=noreply@example.com

# Stripe (for payments)
STRIPE_SECRET_KEY=sk_test_...
STRIPE_WEBHOOK_SECRET=whsec_...

Security

# Rate Limiting
RATE_LIMIT_WINDOW=15m
RATE_LIMIT_MAX_REQUESTS=100

# CORS
CORS_ORIGIN=http://localhost:3001,https://app.example.com

📁 Project Structure

api-service/
├── src/
│   ├── controllers/       # Request handlers
│   ├── services/          # Business logic
│   ├── models/            # Database models
│   ├── routes/            # API routes
│   ├── middleware/        # Custom middleware
│   ├── utils/             # Utility functions
│   ├── config/            # Configuration files
│   ├── types/             # TypeScript types
│   └── app.ts             # Express app setup
├── tests/
│   ├── unit/              # Unit tests
│   ├── integration/       # Integration tests
│   └── e2e/               # End-to-end tests
├── prisma/
│   ├── schema.prisma      # Database schema
│   ├── migrations/        # Migration files
│   └── seeds/             # Seed data
├── docker/
│   ├── Dockerfile
│   └── docker-compose.yml
├── docs/                  # Additional documentation
├── scripts/               # Utility scripts
├── .env.example
├── package.json
└── tsconfig.json

🌐 API Endpoints

Authentication

Method	Endpoint	Description	Auth Required
POST	/api/v1/auth/register	Register new user	No
POST	/api/v1/auth/login	User login	No
POST	/api/v1/auth/logout	User logout	Yes
POST	/api/v1/auth/refresh	Refresh access token	No
POST	/api/v1/auth/forgot-password	Request password reset	No
POST	/api/v1/auth/reset-password	Reset password	No

Users

Method	Endpoint	Description	Auth Required
GET	/api/v1/users	List all users (paginated)	Yes (Admin)
GET	/api/v1/users/:id	Get user by ID	Yes
PUT	/api/v1/users/:id	Update user profile	Yes (Owner)
DELETE	/api/v1/users/:id	Delete user account	Yes (Owner)
GET	/api/v1/users/me	Get current user profile	Yes

Posts

Method	Endpoint	Description	Auth Required
GET	/api/v1/posts	List all posts	No
GET	/api/v1/posts/:id	Get post by ID	No
POST	/api/v1/posts	Create new post	Yes
PUT	/api/v1/posts/:id	Update post	Yes (Owner)
DELETE	/api/v1/posts/:id	Delete post	Yes (Owner)
POST	/api/v1/posts/:id/like	Like a post	Yes

Files

Method	Endpoint	Description	Auth Required
POST	/api/v1/files/upload	Upload file to S3	Yes
GET	/api/v1/files/:id	Get file metadata	Yes
DELETE	/api/v1/files/:id	Delete file	Yes (Owner)

Health & Monitoring

Method	Endpoint	Description	Auth Required
GET	/health	Basic health check	No
GET	/health/ready	Readiness probe	No
GET	/health/live	Liveness probe	No
GET	/metrics	Prometheus metrics	No

🔍 Example Requests

Register a New User

curl -X POST http://localhost:3000/api/v1/auth/register \
  -H "Content-Type: application/json" \
  -d '{
    "email": "user@example.com",
    "password": "SecurePass123!",
    "name": "John Doe"
  }'

Response:

{
  "success": true,
  "data": {
    "user": {
      "id": "user_abc123",
      "email": "user@example.com",
      "name": "John Doe"
    },
    "tokens": {
      "accessToken": "eyJhbGciOiJIUzI1NiIs...",
      "refreshToken": "eyJhbGciOiJIUzI1NiIs..."
    }
  }
}

Login

curl -X POST http://localhost:3000/api/v1/auth/login \
  -H "Content-Type: application/json" \
  -d '{
    "email": "user@example.com",
    "password": "SecurePass123!"
  }'

Create a Post (Authenticated)

curl -X POST http://localhost:3000/api/v1/posts \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
  -d '{
    "title": "My First Post",
    "content": "This is the content of my post",
    "tags": ["tutorial", "api"]
  }'

Get Posts with Pagination

curl -X GET "http://localhost:3000/api/v1/posts?page=1&limit=10&sort=createdAt:desc"

🧪 Testing

Run All Tests

npm test

Run Specific Test Suites

# Unit tests only
npm run test:unit

# Integration tests only
npm run test:integration

# E2E tests only
npm run test:e2e

# With coverage
npm run test:coverage

Watch Mode (Development)

npm run test:watch

Test Example

describe('POST /api/v1/auth/register', () => {
  it('should create a new user successfully', async () => {
    const response = await request(app)
      .post('/api/v1/auth/register')
      .send({
        email: 'test@example.com',
        password: 'SecurePass123!',
        name: 'Test User'
      });

    expect(response.status).toBe(201);
    expect(response.body.data.user.email).toBe('test@example.com');
    expect(response.body.data.tokens).toHaveProperty('accessToken');
  });
});

🐳 Docker Deployment

Build and Run with Docker Compose

# Build and start all services
docker-compose up -d

# View logs
docker-compose logs -f api

# Stop all services
docker-compose down

# Rebuild after changes
docker-compose up -d --build

Production Docker Build

# Build production image
docker build -t myapi:latest -f docker/Dockerfile .

# Run production container
docker run -d \
  --name myapi \
  -p 3000:3000 \
  --env-file .env.production \
  myapi:latest

🚀 Deployment

AWS Elastic Beanstalk

# Initialize EB
eb init -p node.js-20 my-api

# Create environment
eb create production

# Deploy
eb deploy

DigitalOcean App Platform

1. Connect your GitHub repository
2. Configure environment variables
3. Set build command: npm run build
4. Set run command: npm start

Heroku

# Create app
heroku create myapi

# Add PostgreSQL addon
heroku addons:create heroku-postgresql:hobby-dev

# Deploy
git push heroku main

# Run migrations
heroku run npm run migrate

VPS (Ubuntu 22.04)

# Install dependencies
sudo apt update
sudo apt install -y nodejs npm postgresql redis nginx

# Setup app
cd /var/www/myapi
npm install
npm run build

# Setup PM2
pm2 start dist/index.js --name myapi
pm2 startup
pm2 save

# Configure Nginx reverse proxy
sudo nano /etc/nginx/sites-available/myapi

📊 Monitoring & Logging

Health Checks

# Basic health
curl http://localhost:3000/health

# Database connectivity
curl http://localhost:3000/health/ready

# Liveness probe
curl http://localhost:3000/health/live

Logs

# Development logs
npm run dev

# Production logs with PM2
pm2 logs myapi

# Docker logs
docker-compose logs -f api

Metrics

Access Prometheus metrics at: http://localhost:3000/metrics

🔒 Security

Implemented Security Measures

• ✅ JWT-based authentication
• ✅ Password hashing with bcrypt
• ✅ Rate limiting (100 requests per 15 minutes)
• ✅ Helmet.js for security headers
• ✅ CORS configuration
• ✅ SQL injection prevention (ORM)
• ✅ XSS protection
• ✅ CSRF tokens
• ✅ Input validation with Joi/Zod
• ✅ File upload restrictions

Security Best Practices

1. Never commit .env files
2. Rotate secrets regularly
3. Use HTTPS in production
4. Enable 2FA for admin accounts
5. Regular dependency audits: npm audit

🛠 Development Commands

# Development
npm run dev              # Start dev server with hot reload
npm run dev:debug        # Start with Node debugger

# Building
npm run build            # Compile TypeScript to JavaScript
npm run build:watch      # Watch mode

# Database
npm run migrate          # Run migrations
npm run migrate:rollback # Rollback last migration
npm run seed             # Seed database

# Code Quality
npm run lint             # Run ESLint
npm run lint:fix         # Fix linting issues
npm run format           # Format code with Prettier
npm run type-check       # TypeScript type checking

# Testing
npm test                 # Run all tests
npm run test:watch       # Watch mode
npm run test:coverage    # Generate coverage report

# Production
npm start                # Start production server

📚 Additional Documentation

• API Documentation
• Database Schema
• Authentication Flow
• Deployment Guide
• Troubleshooting

🤝 Contributing

See CONTRIBUTING.md

📄 License

MIT License - see LICENSE

👥 Team

• Tech Lead: @username
• Backend: @developer1, @developer2
• DevOps: @devops1

📞 Support

• 📧 Email: dev-support@example.com
• 💬 Slack: #api-support
• 🐛 Issues: GitHub Issues

Customer Churn Prediction using Machine Learning

A comprehensive data science project that analyzes customer behavior patterns and predicts churn probability using ensemble methods (Random Forest, XGBoost, LightGBM). This project includes full exploratory data analysis, feature engineering, model comparison, and deployment-ready prediction pipeline.

Project Status: ✅ Completed | 📊 Accuracy: 86.3% | 🎯 AUC-ROC: 0.91

📋 Table of Contents

• Overview
• Business Problem
• Dataset
• Project Structure
• Installation
• Usage
• Methodology
• Results
• Model Deployment
• Future Work

🎯 Overview

What is This Project About?

Customer churn (the rate at which customers stop doing business with a company) is a critical metric for subscription-based businesses. This project builds a machine learning solution to:

1. Predict which customers are likely to churn in the next month
2. Identify key factors driving customer churn
3. Provide actionable insights for retention strategies
4. Enable proactive customer outreach programs

Key Achievements

• Achieved 86.3% accuracy in predicting customer churn
• Identified top 5 churn drivers: contract type, tenure, monthly charges, tech support, payment method
• Reduced false negatives by 35% compared to logistic regression baseline
• Built production-ready prediction pipeline with FastAPI

Technologies Used

• Languages: Python 3.10
• ML Libraries: scikit-learn, XGBoost, LightGBM, CatBoost
• Data Processing: pandas, NumPy
• Visualization: matplotlib, seaborn, plotly
• Notebooks: Jupyter Lab
• Model Tracking: MLflow
• Deployment: FastAPI, Docker

💼 Business Problem

Context

TelcoConnect (fictional company) is experiencing a 27% annual churn rate, which is costing the company $12M annually in lost revenue. The business needs to:

• Identify at-risk customers before they churn
• Understand why customers are leaving
• Implement targeted retention campaigns

Project Goals

1. Primary: Build a model with >85% accuracy for churn prediction
2. Secondary: Achieve precision >0.80 to minimize wasted retention efforts
3. Tertiary: Provide interpretable results for business stakeholders

Success Metrics

• Model Performance: Accuracy >85%, Precision >0.80, Recall >0.75
• Business Impact: Enable retention campaigns targeting top 10% at-risk customers
• ROI: Each correct prediction saves avg $1,200 in customer lifetime value

📊 Dataset

Source

Dataset: Telco Customer Churn
License: CC0: Public Domain
Size: 7,043 rows × 21 columns

Dataset Overview

The dataset contains customer information for a telecommunications company:

• Customers: 7,043 unique customer records
• Time Period: Q2 2020 to Q3 2020 (simulated)
• Churn Rate: 26.5% (1,869 churned customers)

Features

Demographic Features

• gender: Customer gender (Male, Female)
• SeniorCitizen: Whether customer is 65+ (0, 1)
• Partner: Whether customer has a partner (Yes, No)
• Dependents: Whether customer has dependents (Yes, No)

Service Features

• tenure: Number of months as customer (0-72)
• PhoneService: Phone service subscription (Yes, No)
• MultipleLines: Multiple phone lines (Yes, No, No phone service)
• InternetService: Type of internet (DSL, Fiber optic, No)
• OnlineSecurity: Online security add-on (Yes, No, No internet service)
• OnlineBackup: Online backup add-on (Yes, No, No internet service)
• DeviceProtection: Device protection add-on (Yes, No, No internet service)
• TechSupport: Tech support add-on (Yes, No, No internet service)
• StreamingTV: TV streaming service (Yes, No, No internet service)
• StreamingMovies: Movie streaming service (Yes, No, No internet service)

Account Features

• Contract: Contract type (Month-to-month, One year, Two year)
• PaperlessBilling: Paperless billing (Yes, No)
• PaymentMethod: Payment method (Electronic check, Mailed check, Bank transfer, Credit card)
• MonthlyCharges: Current monthly charge ($18.25 -$118.75)
• TotalCharges: Total amount charged to date

Target Variable

• Churn: Whether customer churned (Yes, No)

Data Quality

Issue	Count	Resolution
Missing Values	11 in TotalCharges	Imputed with median
Duplicates	0	None found
Outliers	3% in MonthlyCharges	Retained (legitimate high-spend customers)
Class Imbalance	73.5% / 26.5%	SMOTE applied

📁 Project Structure

customer-churn-prediction/
│
├── data/
│   ├── raw/                      # Original dataset
│   │   └── WA_Fn-UseC_-Telco-Customer-Churn.csv
│   ├── processed/                # Cleaned and engineered data
│   │   ├── train.csv
│   │   ├── test.csv
│   │   └── validation.csv
│   └── external/                 # External reference data
│
├── notebooks/
│   ├── 01_EDA.ipynb             # Exploratory Data Analysis
│   ├── 02_Data_Cleaning.ipynb   # Data cleaning and preprocessing
│   ├── 03_Feature_Engineering.ipynb  # Feature creation
│   ├── 04_Baseline_Models.ipynb      # Simple model benchmarks
│   ├── 05_Model_Training.ipynb       # Full model training
│   ├── 06_Model_Evaluation.ipynb     # Model comparison & selection
│   └── 07_Model_Interpretation.ipynb # SHAP analysis
│
├── src/
│   ├── __init__.py
│   ├── data/
│   │   ├── load_data.py         # Data loading utilities
│   │   ├── preprocess.py        # Preprocessing functions
│   │   └── feature_engineering.py
│   ├── models/
│   │   ├── train.py             # Training scripts
│   │   ├── predict.py           # Prediction scripts
│   │   └── evaluate.py          # Evaluation metrics
│   ├── visualization/
│   │   └── plots.py             # Plotting functions
│   └── utils/
│       ├── config.py            # Configuration
│       └── helpers.py           # Helper functions
│
├── models/
│   ├── random_forest_v1.pkl     # Saved model files
│   ├── xgboost_v1.pkl
│   ├── lightgbm_v1.pkl
│   └── final_model.pkl          # Best performing model
│
├── api/
│   ├── main.py                  # FastAPI application
│   ├── schemas.py               # Request/response models
│   └── requirements.txt
│
├── tests/
│   ├── test_preprocessing.py
│   ├── test_models.py
│   └── test_api.py
│
├── reports/
│   ├── figures/                 # Generated plots
│   ├── results.md               # Model results summary
│   └── business_insights.pdf    # Executive summary
│
├── deployment/
│   ├── Dockerfile
│   ├── docker-compose.yml
│   └── kubernetes/
│
├── .gitignore
├── environment.yml              # Conda environment
├── requirements.txt             # Python dependencies
├── README.md
└── LICENSE

🛠 Installation

Prerequisites

• Python 3.10 or higher
• Anaconda or Miniconda (recommended)
• Jupyter Lab
• Git

Option 1: Conda Environment (Recommended)

# Clone the repository
git clone https://github.com/username/customer-churn-prediction.git
cd customer-churn-prediction

# Create conda environment
conda env create -f environment.yml

# Activate environment
conda activate churn-analysis

# Verify installation
python -c "import sklearn, xgboost, lightgbm; print('All packages installed!')"

Option 2: Virtual Environment (venv)

# Clone the repository
git clone https://github.com/username/customer-churn-prediction.git
cd customer-churn-prediction

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Dependencies

Key packages (full list in requirements.txt):

pandas==2.0.3
numpy==1.24.3
scikit-learn==1.3.0
xgboost==2.0.0
lightgbm==4.0.0
catboost==1.2
matplotlib==3.7.2
seaborn==0.12.2
plotly==5.15.0
jupyter==1.0.0
shap==0.42.1
imbalanced-learn==0.11.0
mlflow==2.5.0
fastapi==0.100.0
uvicorn==0.23.0

🚀 Usage

1. Download the Dataset

# Create data directory
mkdir -p data/raw

# Download from Kaggle (requires kaggle API setup)
kaggle datasets download -d blastchar/telco-customer-churn -p data/raw

# Unzip
unzip data/raw/telco-customer-churn.zip -d data/raw

2. Run Notebooks in Order

# Start Jupyter Lab
jupyter lab

# Navigate to notebooks/ folder and run in sequence:
# 01_EDA.ipynb → 02_Data_Cleaning.ipynb → ... → 07_Model_Interpretation.ipynb

3. Train Models via Scripts

# Preprocess data
python src/data/preprocess.py --input data/raw/WA_Fn-UseC_-Telco-Customer-Churn.csv --output data/processed/

# Train models
python src/models/train.py --model random_forest --data data/processed/train.csv

# Train all models with hyperparameter tuning
python src/models/train.py --model all --tune --cv 5

4. Make Predictions

# Predict on new data
python src/models/predict.py --model models/final_model.pkl --input data/new_customers.csv --output predictions.csv

5. Run API Server (for production predictions)

cd api
uvicorn main:app --reload --host 0.0.0.0 --port 8000

# Test the API
curl -X POST "http://localhost:8000/predict" \
  -H "Content-Type: application/json" \
  -d '{
    "gender": "Female",
    "SeniorCitizen": 0,
    "Partner": "Yes",
    "Dependents": "No",
    "tenure": 12,
    "PhoneService": "Yes",
    "InternetService": "Fiber optic",
    "Contract": "Month-to-month",
    "MonthlyCharges": 70.5
  }'

🔬 Methodology

1. Exploratory Data Analysis (EDA)

Key Findings:

• Customers with month-to-month contracts churn 42% vs 11% for long-term contracts
• Fiber optic customers churn 41% vs 19% for DSL customers
• Customers without tech support churn 41% vs 15% with tech support
• Churn rate inversely correlates with tenure (strong relationship)

Visualizations Created:

• Churn distribution by contract type
• Correlation heatmap of numerical features
• Box plots for monthly charges by churn status
• Tenure distribution for churned vs retained

2. Data Preprocessing

Steps Taken:

1. Missing Value Treatment:

   • Imputed 11 missing TotalCharges with median grouped by tenure

2. Feature Encoding:

   • Binary features (Yes/No) → 0/1
   • Multi-class features → One-Hot Encoding
   • Ordinal features preserved order

3. Feature Scaling:

   • StandardScaler for tree-based models (optional)
   • MinMaxScaler for neural networks (future work)

4. Class Imbalance:

   • Applied SMOTE (Synthetic Minority Over-sampling)
   • Balanced to 60/40 split

3. Feature Engineering

New Features Created:

1. tenure_months_binned: Categorized tenure (0-12, 13-24, 25-48, 49+)
2. avg_monthly_spend: TotalCharges / tenure
3. service_count: Total number of services subscribed
4. has_premium_services: Binary flag for any premium add-ons
5. charge_tenure_ratio: MonthlyCharges * tenure interaction
6. contract_value: Indicator combining contract and payment method

Feature Importance (Top 10):

Rank	Feature	Importance	Description
1	tenure	0.185	Months as customer
2	MonthlyCharges	0.162	Current monthly bill
3	Contract_Month-to-month	0.143	Short-term contract
4	TotalCharges	0.109	Lifetime spending
5	InternetService_Fiber	0.087	Fiber internet
6	PaymentMethod_Electronic	0.076	Auto-pay method
7	TechSupport_No	0.068	No tech support
8	OnlineSecurity_No	0.054	No online security
9	service_count	0.049	Total services
10	PaperlessBilling_Yes	0.041	Paperless billing

4. Model Training & Selection

Models Evaluated:

Model	Description	Use Case
Logistic Regression	Baseline linear model	Interpretable baseline
Decision Tree	Simple tree-based model	Feature importance
Random Forest	Ensemble of trees	Robust predictions
XGBoost	Gradient boosting	High performance
LightGBM	Fast gradient boosting	Large datasets
CatBoost	Handles categorical data	Categorical-heavy data

Hyperparameter Tuning:

• Method: Randomized Search CV (5-fold)
• Metric: ROC-AUC Score
• Iterations: 100 per model
• Computing: 12-core CPU, 4 hours

5. Model Evaluation

Cross-Validation Strategy:

• 5-Fold Stratified Cross-Validation
• Train/Val/Test Split: 60% / 20% / 20%
• Stratified to maintain class balance

Evaluation Metrics:

• Accuracy, Precision, Recall, F1-Score
• ROC-AUC, PR-AUC
• Confusion Matrix
• Classification Report

📈 Results

Model Performance Comparison

Model	Accuracy	Precision	Recall	F1-Score	ROC-AUC	PR-AUC	Training Time
Logistic Regression	80.1%	0.68	0.62	0.65	0.82	0.71	2s
Decision Tree	78.5%	0.63	0.71	0.67	0.79	0.68	5s
Random Forest	85.3%	0.82	0.71	0.76	0.91	0.84	45s
XGBoost	86.3%	0.84	0.73	0.78	0.90	0.83	38s
LightGBM	84.7%	0.80	0.75	0.77	0.90	0.82	12s
CatBoost	85.1%	0.81	0.72	0.76	0.90	0.83	95s

🏆 Best Model: XGBoost

• Why: Highest accuracy (86.3%) and precision (0.84) with competitive training time
• Runner-up: Random Forest (more interpretable, nearly as good)

Detailed XGBoost Results

Classification Report:
              precision    recall  f1-score   support

  Not Churned       0.88      0.95      0.91      1038
      Churned       0.84      0.73      0.78       371

     accuracy                           0.86      1409
    macro avg       0.86      0.84      0.85      1409
weighted avg       0.87      0.86      0.86      1409

ROC-AUC Score: 0.9037
PR-AUC Score: 0.8291

Confusion Matrix (Test Set)

                 Predicted
                 Not Churn  Churn
Actual Not Churn    987      51
       Churn        100     271

Interpretation:

• True Negatives: 987 (correctly identified non-churners)
• False Positives: 51 (incorrectly flagged as churners)
• False Negatives: 100 (missed churners - business cost)
• True Positives: 271 (correctly identified churners)

Feature Importance (SHAP Analysis)

Top Drivers of Churn:

1. Contract Type: Month-to-month contracts increase churn probability by 42%
2. Tenure: Each additional year reduces churn risk by 15%
3. Tech Support: Lack of tech support increases churn by 28%
4. Monthly Charges: Charges >$70 increase churn by 22%
5. Internet Type: Fiber optic increases churn by 18% (likely due to price)

Business Impact Simulation

Based on model predictions on 1,000 customers:

Metric	Value	Financial Impact
At-risk customers identified	268	-
True churners caught	195 (73% recall)	$234,000 saved
False alarms	73	$14,600 wasted effort
Net benefit	-	$219,400

Assumption: Retention campaign costs $200 per customer, saves$1,200 if successful

🚀 Model Deployment

Local Prediction API

A FastAPI service for real-time churn predictions:

# Start the API server
cd api
uvicorn main:app --reload --port 8000

# Server runs at: http://localhost:8000
# Docs available at: http://localhost:8000/docs

API Endpoints:

1. POST /predict - Single customer prediction
2. POST /predict/batch - Batch predictions (up to 1000)
3. GET /health - Health check
4. GET /model/info - Model metadata

Example Request:

import requests

customer_data = {
    "gender": "Female",
    "SeniorCitizen": 0,
    "Partner": "Yes",
    "Dependents": "No",
    "tenure": 12,
    "PhoneService": "Yes",
    "MultipleLines": "No",
    "InternetService": "Fiber optic",
    "OnlineSecurity": "No",
    "OnlineBackup": "No",
    "DeviceProtection": "No",
    "TechSupport": "No",
    "StreamingTV": "No",
    "StreamingMovies": "No",
    "Contract": "Month-to-month",
    "PaperlessBilling": "Yes",
    "PaymentMethod": "Electronic check",
    "MonthlyCharges": 70.35,
    "TotalCharges": 844.2
}

response = requests.post(
    "http://localhost:8000/predict",
    json=customer_data
)

print(response.json())
# Output: {"churn_probability": 0.78, "prediction": "Churn", "risk_level": "High"}

Docker Deployment

# Build image
docker build -t churn-prediction-api:v1 .

# Run container
docker run -d -p 8000:8000 churn-prediction-api:v1

# Test
curl http://localhost:8000/health

Production Considerations

For production deployment:

1. Model Versioning: Use MLflow for model registry
2. Monitoring: Track prediction drift with Evidently
3. A/B Testing: Compare model versions in production
4. Logging: Log all predictions for audit trail
5. Rate Limiting: Implement rate limits for API
6. Authentication: Add API key authentication

📊 Notebooks Overview

01_EDA.ipynb - Exploratory Data Analysis

Duration: ~30 minutes
Output: 15 visualizations, statistical summaries

Key Sections:

• Data loading and inspection
• Missing value analysis
• Distribution plots for all features
• Churn rate by demographic segments
• Correlation analysis
• Statistical tests (Chi-square, t-tests)

Key Insights:

• Month-to-month contracts have 3.8x higher churn
• Fiber optic customers churn 2.1x more than DSL
• Tech support reduces churn by 63%

02_Data_Cleaning.ipynb - Data Preprocessing

Duration: ~20 minutes
Output: Cleaned dataset saved to data/processed/

Cleaning Steps:

1. Handle missing TotalCharges (11 rows)
2. Fix data type inconsistencies
3. Remove duplicates (none found)
4. Validate ranges and constraints
5. Create train/test split (80/20)

03_Feature_Engineering.ipynb - Feature Creation

Duration: ~25 minutes
Output: 6 new engineered features

Features Created:

• tenure_group: Binned tenure categories
• avg_monthly_charges: TotalCharges / tenure
• service_count: Number of subscribed services
• has_premium: Flag for premium services
• contract_payment: Interaction feature
• charge_ratio: MonthlyCharges / median ratio

04_Baseline_Models.ipynb - Initial Models

Duration: ~15 minutes
Output: Baseline performance benchmarks

Establishes performance baselines:

• Majority class baseline: 73.5% accuracy
• Logistic Regression: 80.1% accuracy
• Simple Decision Tree: 78.5% accuracy

05_Model_Training.ipynb - Advanced Models

Duration: ~2 hours (with hyperparameter tuning)
Output: 6 trained models with optimized hyperparameters

Trains and tunes:

• Random Forest (100 trees)
• XGBoost (200 rounds)
• LightGBM (fast training)
• CatBoost (categorical handling)

06_Model_Evaluation.ipynb - Model Comparison

Duration: ~30 minutes
Output: Comprehensive evaluation metrics and plots

Evaluation Includes:

• Performance metrics table
• ROC curves (all models)
• Precision-Recall curves
• Confusion matrices
• Cross-validation results
• Model selection justification

07_Model_Interpretation.ipynb - Explainability

Duration: ~40 minutes
Output: SHAP plots, feature importance, partial dependence plots

Analysis Includes:

• SHAP summary plot (global feature importance)
• SHAP waterfall plots (individual predictions)
• Partial dependence plots
• Feature interaction analysis
• Business recommendations

💡 Business Insights & Recommendations

Top 3 Actionable Insights

1. Contract Strategy 🎯

Finding: Month-to-month customers churn at 42% vs 11% for contract customers

Recommendations:

• Offer 10-15% discount for 1-year contracts
• Implement "contract transition" campaigns at months 8-10
• Provide loyalty bonuses for contract renewals
• Create hybrid "flex contracts" with lower early termination fees

Expected Impact: 25% reduction in month-to-month churn

2. Tech Support Matters 🛠️

Finding: Customers without tech support churn 63% more often

Recommendations:

• Include 3 months free tech support for new customers
• Proactive outreach to customers who call support >2 times
• Create self-service knowledge base to reduce friction
• Train support staff in retention techniques

Expected Impact: 18% reduction in overall churn

3. Fiber Optic Pricing 💰

Finding: Fiber customers churn 2.1x more despite premium service

Recommendations:

• Review fiber optic pricing strategy (currently 40% more than DSL)
• Bundle fiber with premium features (security, backup)
• Implement "price lock" guarantees for fiber customers
• Create competitive matching program

Expected Impact: 15% reduction in fiber optic churn

Retention Campaign Strategy

Target Segment: Top 10% churn risk (based on model scores)

Campaign Elements:

1. Personalized Outreach: Email + phone call within 48 hours
2. Custom Offers: Based on customer's service usage
3. Save Desk: Dedicated retention specialists
4. Win-back: Re-engagement campaign for recent churners

Budget: $200 per customer in campaign **Expected ROI**: 3.5x (save$1,200 per successful retention)

🔮 Future Work

Short-term (Next 3 Months)

• Implement A/B testing framework for retention campaigns
• Add demographic data (location, income) if available
• Build customer lifetime value (CLV) prediction model
• Create interactive dashboard for stakeholders (Streamlit)
• Set up automated model retraining pipeline

Medium-term (3-6 Months)

• Deep learning model (LSTM for temporal patterns)
• Causal inference analysis (why do interventions work?)
• Survival analysis for time-to-churn predictions
• Multi-model ensemble with stacking
• Real-time prediction streaming with Kafka

Long-term (6-12 Months)

• Prescriptive analytics (optimal intervention timing)
• Customer segmentation with clustering
• Integration with CRM system (Salesforce)
• AutoML pipeline for continuous improvement
• Cost-sensitive learning (different costs for FP vs FN)

🐛 Troubleshooting

Common Issues

Issue: "TotalCharges cannot be converted to float"

# Solution: Convert with error handling
df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')
df['TotalCharges'].fillna(df['TotalCharges'].median(), inplace=True)

Issue: ImportError with XGBoost

# Solution: Install with conda instead of pip
conda install -c conda-forge xgboost

Issue: Kernel dying during model training

# Solution: Reduce hyperparameter search space
param_grid = {
    'n_estimators': [50, 100],  # Reduced from [50, 100, 200]
    'max_depth': [3, 5],        # Reduced from [3, 5, 7, 10]
}

Issue: API returns 422 validation error

# Solution: Check that all required fields are included
# See api/schemas.py for complete field list

📚 Additional Resources

Learning Materials

• Scikit-learn Documentation
• XGBoost Documentation
• SHAP Library Guide
• Kaggle Churn Prediction Tutorial

Research Papers

• Chen & Guestrin (2016): "XGBoost: A Scalable Tree Boosting System"
• Lundberg & Lee (2017): "A Unified Approach to Interpreting Model Predictions"

Related Projects

• Customer Segmentation with K-Means
• Time Series Churn Prediction with LSTM

🤝 Contributing

Contributions welcome! Areas for improvement:

1. Feature Engineering: Propose new features
2. Model Experimentation: Try neural networks, ensemble methods
3. Visualization: Create new plots for insights
4. Documentation: Improve explanations
5. Testing: Add unit tests for preprocessing pipeline

See CONTRIBUTING.md for guidelines.

📄 Citation

If you use this project in your research or work, please cite:

@misc{churn_prediction_2024,
  author = {Your Name},
  title = {Customer Churn Prediction using Machine Learning},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/username/customer-churn-prediction}
}

📧 Contact

Author: Your Name
Email: your.email@example.com
LinkedIn: Your Profile
GitHub: @yourusername

📜 License

This project is licensed under the MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Dataset provided by IBM Watson Analytics
• Kaggle community for inspiration and notebooks
• Open-source ML community for amazing tools
• My university advisor for project guidance

---

Last Updated: December 2024
Project Status: ✅ Complete and Deployed
Maintained: Yes, active development

MegaFramework Documentation

The Modern Web Framework for Building Scalable Enterprise Applications

Welcome to MegaFramework - a next-generation full-stack framework that combines the best of React, server-side rendering, and edge computing. Built for teams that need to ship fast without sacrificing performance, security, or developer experience.

Version: 3.0.0 | License: MIT | Status: Stable ✅

🚀 Quick Links

• 📖 Full Documentation
• 🎓 Interactive Tutorial
• 💬 Community Discord
• 📺 Video Courses
• 🐛 Issue Tracker

📑 Table of Contents

Getting Started

• What is MegaFramework?
• Why MegaFramework?
• Installation
• Your First App
• Project Structure

Core Concepts

• Routing
• Data Fetching
• State Management
• Styling
• API Routes

Advanced Topics

• Server-Side Rendering (SSR)
• Static Site Generation (SSG)
• Edge Functions
• Authentication
• Middleware
• Caching Strategies
• Internationalization (i18n)

API Reference

• CLI Commands
• Configuration
• Core APIs
• Plugins

Deployment

• Vercel
• Netlify
• AWS
• Docker

Additional Resources

• FAQ
• Troubleshooting
• Migration Guides
• Community Plugins

---

What is MegaFramework?

MegaFramework is a full-stack React framework that provides everything you need to build production-ready web applications:

Key Features

✅ Hybrid Rendering: Mix SSR, SSG, and CSR in one app
✅ File-Based Routing: Routes from your file system
✅ API Routes: Build your backend alongside your frontend
✅ Edge Functions: Run code at the edge for lowest latency
✅ TypeScript First: Full type safety from database to UI
✅ Zero Config: Sensible defaults, configure only what you need
✅ Optimized Builds: Automatic code splitting and lazy loading
✅ Developer Experience: Hot reload, error overlay, time-travel debugging

What Can You Build?

• 🛍️ E-commerce Sites: High-performance storefronts with SEO
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• 📰 Content Sites: Blogs, documentation, marketing pages
• 🎮 Interactive Apps: Dashboards, tools, games
• 🌐 Multi-tenant Platforms: Enterprise apps with customization

Why MegaFramework?

vs Next.js

• ✅ Simpler data fetching APIs
• ✅ Built-in state management
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• ✅ Native TypeScript (no compilation needed)

vs Remix

• ✅ More flexible rendering options
• ✅ Established ecosystem (5+ years)
• ✅ Better static site generation

vs SvelteKit

• ✅ Larger React ecosystem
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• ✅ Richer tooling and IDE support

The Numbers

• 1.2M weekly downloads
• 45K GitHub stars
• 2,500+ companies in production
• 99.9% uptime on framework CDN
• <100ms p50 response time

Installation

Requirements

• Node.js 18.17 or later
• macOS, Windows, or Linux

Create a New App

The fastest way to get started:

# Using npx (npm 5.2+)
npx create-mega-app my-app

# Using yarn
yarn create mega-app my-app

# Using pnpm
pnpm create mega-app my-app

# With TypeScript (recommended)
npx create-mega-app my-app --typescript

# With a template
npx create-mega-app my-app --template blog
# Available templates: blog, e-commerce, dashboard, docs, landing

Manual Installation

Add to an existing project:

npm install megaframework react react-dom

# Peer dependencies
npm install --save-dev @types/react @types/react-dom typescript

Create mega.config.ts:

import { defineConfig } from 'megaframework';

export default defineConfig({
  // Your config here
});

Add scripts to package.json:

{
  "scripts": {
    "dev": "mega dev",
    "build": "mega build",
    "start": "mega start",
    "lint": "mega lint"
  }
}

Your First App

1. Create a Page

Create pages/index.tsx:

export default function Home() {
  return (
    <div>
      <h1>Welcome to MegaFramework!</h1>
      <p>Build something amazing.</p>
    </div>
  );
}

2. Add Styling

MegaFramework supports CSS Modules, Tailwind, Styled Components, and more.

Using CSS Modules (index.module.css):

.container {
  min-height: 100vh;
  padding: 4rem;
  text-align: center;
}

.title {
  font-size: 3rem;
  color: #0070f3;
}

import styles from './index.module.css';

export default function Home() {
  return (
    <div className={styles.container}>
      <h1 className={styles.title}>Welcome!</h1>
    </div>
  );
}

3. Fetch Data

import { getServerData } from 'megaframework';

export default function Blog({ posts }) {
  return (
    <div>
      <h1>Blog Posts</h1>
      {posts.map(post => (
        <article key={post.id}>
          <h2>{post.title}</h2>
          <p>{post.excerpt}</p>
        </article>
      ))}
    </div>
  );
}

// Runs on the server
export const getData = getServerData(async () => {
  const res = await fetch('https://api.example.com/posts');
  const posts = await res.json();
  
  return { props: { posts } };
});

4. Add Dynamic Routes

Create pages/blog/[slug].tsx:

import { useRouter } from 'megaframework/router';
import { getServerData } from 'megaframework';

export default function BlogPost({ post }) {
  const router = useRouter();
  
  if (router.isFallback) {
    return <div>Loading...</div>;
  }
  
  return (
    <article>
      <h1>{post.title}</h1>
      <div dangerouslySetInnerHTML={{ __html: post.content }} />
    </article>
  );
}

export const getData = getServerData(async ({ params }) => {
  const post = await fetch('https://api.example.com/posts/params.slug');
  
  return {
    props: { post: await post.json() },
    revalidate: 60 // ISR: Revalidate every 60 seconds
  };
});

// Generate static paths at build time
export const getPaths = getServerData(async () => {
  const res = await fetch('https://api.example.com/posts');
  const posts = await res.json();
  
  return {
    paths: posts.map(post => ({ params: { slug: post.slug } })),
    fallback: true // Enable ISR for new paths
  };
});

5. Create an API Route

Create pages/api/hello.ts:

import { NextApiRequest, NextApiResponse } from 'megaframework';

export default function handler(req: NextApiRequest, res: NextApiResponse) {
  res.status(200).json({ 
    message: 'Hello from MegaFramework!',
    timestamp: new Date().toISOString()
  });
}

Advanced API route with database:

import { db } from '@/lib/database';
import { withAuth } from '@/middleware/auth';

async function handler(req, res) {
  if (req.method === 'GET') {
    const users = await db.user.findMany();
    return res.status(200).json(users);
  }
  
  if (req.method === 'POST') {
    const user = await db.user.create({
      data: req.body
    });
    return res.status(201).json(user);
  }
  
  res.status(405).json({ error: 'Method not allowed' });
}

export default withAuth(handler); // Protected route

6. Run Development Server

npm run dev

Open http://localhost:3000 🎉

Project Structure

Recommended structure for a MegaFramework app:

my-mega-app/
├── pages/              # File-based routes
│   ├── index.tsx       # / route
│   ├── about.tsx       # /about route
│   ├── blog/
│   │   ├── index.tsx   # /blog route
│   │   └── [slug].tsx  # /blog/:slug route
│   ├── api/            # API routes
│   │   └── users.ts    # /api/users endpoint
│   ├── _app.tsx        # Custom App component
│   └── _document.tsx   # Custom Document
│
├── components/         # Reusable components
│   ├── Button.tsx
│   ├── Header.tsx
│   └── Layout.tsx
│
├── lib/                # Utility functions
│   ├── database.ts
│   ├── auth.ts
│   └── api.ts
│
├── styles/             # Global styles
│   ├── globals.css
│   └── variables.css
│
├── public/             # Static files
│   ├── images/
│   └── favicon.ico
│
├── middleware/         # Custom middleware
│   └── auth.ts
│
├── types/              # TypeScript types
│   └── index.ts
│
├── mega.config.ts      # Framework configuration
├── tsconfig.json       # TypeScript configuration
├── package.json
└── .env.local          # Environment variables

Routing

File-Based Routing

MegaFramework uses your file system as the API for routes:

File Path	URL
pages/index.tsx	/
pages/about.tsx	/about
pages/blog/index.tsx	/blog
pages/blog/first-post.tsx	/blog/first-post
pages/blog/[slug].tsx	/blog/:slug
pages/posts/[...all].tsx	/posts/* (catch-all)
pages/[[...slug]].tsx	/* (optional catch-all)

Dynamic Routes

// pages/posts/[id].tsx
import { useRouter } from 'megaframework/router';

export default function Post() {
  const router = useRouter();
  const { id } = router.query;
  
  return <div>Post: {id}</div>;
}

Nested Dynamic Routes

// pages/shop/[category]/[product].tsx
export default function Product() {
  const router = useRouter();
  const { category, product } = router.query;
  
  return <div>{category} - {product}</div>;
}
// URL: /shop/electronics/laptop

Catch-All Routes

// pages/docs/[...slug].tsx
export default function Docs() {
  const router = useRouter();
  const { slug } = router.query; // slug is an array
  
  return <div>Docs: {slug?.join(' / ')}</div>;
}
// URL: /docs/getting-started/installation
// slug: ['getting-started', 'installation']

Programmatic Navigation

import { useRouter } from 'megaframework/router';
import Link from 'megaframework/link';

export default function Navigation() {
  const router = useRouter();
  
  const handleClick = () => {
    router.push('/about');
    // Or with query params
    router.push({
      pathname: '/blog',
      query: { page: '2' }
    });
  };
  
  return (
    <div>
      <Link href="/about">About</Link>
      <button onClick={handleClick}>Go to About</button>
    </div>
  );
}

Data Fetching

MegaFramework offers multiple data fetching strategies:

Server-Side Rendering (SSR)

Fetch data on every request:

import { getServerData } from 'megaframework';

export const getData = getServerData(async (context) => {
  const { req, res, params, query } = context;
  
  // Access request headers
  const userAgent = req.headers['user-agent'];
  
  // Fetch data
  const data = await fetch('https://api.example.com/data');
  
  return {
    props: {
      data: await data.json(),
      userAgent
    }
  };
});

export default function Page({ data, userAgent }) {
  return <div>{data.title}</div>;
}

Static Site Generation (SSG)

Generate pages at build time:

import { getStaticData } from 'megaframework';

export const getData = getStaticData(async () => {
  const posts = await fetchPosts();
  
  return {
    props: { posts },
    revalidate: 3600 // ISR: Regenerate every hour
  };
});

Incremental Static Regeneration (ISR)

Update static pages after deployment:

export const getData = getStaticData(async () => {
  return {
    props: { data: await fetchData() },
    revalidate: 10 // Regenerate every 10 seconds
  };
});

Client-Side Fetching

For dynamic data that doesn't need SEO:

import { useState, useEffect } from 'react';
import { useSWR } from 'megaframework/data';

// With SWR (recommended)
export default function Profile() {
  const { data, error, isLoading } = useSWR('/api/user', fetcher);
  
  if (isLoading) return <div>Loading...</div>;
  if (error) return <div>Error loading data</div>;
  
  return <div>Hello {data.name}!</div>;
}

// With useEffect
export default function Dashboard() {
  const [data, setData] = useState(null);
  
  useEffect(() => {
    fetch('/api/dashboard')
      .then(res => res.json())
      .then(setData);
  }, []);
  
  return data ? <div>{data.stats}</div> : <div>Loading...</div>;
}

State Management

MegaFramework includes built-in state management:

Local Component State

import { useState } from 'react';

export default function Counter() {
  const [count, setCount] = useState(0);
  
  return (
    <div>
      <p>Count: {count}</p>
      <button onClick={() => setCount(count + 1)}>Increment</button>
    </div>
  );
}

Global State (Atoms)

// lib/store.ts
import { atom, useAtom } from 'megaframework/state';

export const userAtom = atom({
  key: 'user',
  default: null
});

export const themeAtom = atom({
  key: 'theme',
  default: 'light'
});

// components/Profile.tsx
import { useAtom } from 'megaframework/state';
import { userAtom } from '@/lib/store';

export default function Profile() {
  const [user, setUser] = useAtom(userAtom);
  
  return (
    <div>
      {user ? 'Welcome user.name!' : 'Please log in'}
    </div>
  );
}

Derived State (Selectors)

import { selector, useValue } from 'megaframework/state';
import { userAtom } from '@/lib/store';

const userNameSelector = selector({
  key: 'userName',
  get: ({ get }) => {
    const user = get(userAtom);
    return user?.name ?? 'Guest';
  }
});

export default function Greeting() {
  const userName = useValue(userNameSelector);
  return <h1>Hello, {userName}!</h1>;
}

API Reference

CLI Commands

# Development
mega dev              # Start dev server (hot reload)
mega dev --port 4000  # Custom port
mega dev --turbo      # Enable turbopack (faster)

# Production
mega build            # Build for production
mega start            # Start production server
mega export           # Export static HTML

# Utilities
mega lint             # Run ESLint
mega lint --fix       # Auto-fix issues
mega analyze          # Analyze bundle size
mega info             # Show system info

Configuration API

// mega.config.ts
import { defineConfig } from 'megaframework';

export default defineConfig({
  // Server
  port: 3000,
  hostname: 'localhost',
  
  // Build
  outDir: 'dist',
  typescript: {
    strict: true,
    paths: {
      '@/*': ['./src/*']
    }
  },
  
  // Optimization
  experimental: {
    turbo: true,
    serverComponents: true
  },
  
  // SEO
  meta: {
    title: 'My App',
    description: 'My awesome app'
  },
  
  // Plugins
  plugins: [
    'mega-plugin-mdx',
    ['mega-plugin-sitemap', { domain: 'https://example.com' }]
  ]
});

For the complete API reference, visit: https://docs.megaframework.com/api

FAQ

Q: How does MegaFramework compare to Next.js?

A: MegaFramework is similar to Next.js but focuses on simplicity and developer experience. Key differences:

• Simpler data fetching API
• Built-in state management
• Native TypeScript support (no build step)
• More flexible rendering options

Q: Can I use MegaFramework with my existing React app?

A: Yes! MegaFramework can be adopted incrementally. Start by migrating one route at a time.

Q: Does MegaFramework support TypeScript?

A: Yes, TypeScript is a first-class citizen. All APIs are fully typed, and .tsx files work out of the box.

Q: How do I deploy a MegaFramework app?

A: Deploy anywhere Node.js runs: Vercel, Netlify, AWS, DigitalOcean, etc. See our deployment guide.

Q: Is MegaFramework production-ready?

A: Absolutely. It's used by 2,500+ companies including Fortune 500 enterprises.

Q: Can I use my preferred CSS solution?

A: Yes. MegaFramework supports CSS Modules, Tailwind, styled-components, Emotion, and vanilla CSS.

---

Need more help?

• 📖 Full Documentation
• 💬 Discord Community
• 🐦 Twitter Updates
• 📧 Email: support@megaframework.com

ACME Corp Monorepo

A high-performance monorepo containing all ACME Corp's web properties, shared libraries, and internal tools. Built with Turborepo, pnpm workspaces, and modern tooling for maximum developer productivity.

🚀 Apps: 3 production apps | 📦 Packages: 8 shared libraries | 👥 Team: 45 developers

🎯 What is This Monorepo?

This repository is the single source of truth for ACME Corp's web ecosystem. It contains:

• Customer-Facing Apps: Marketing site, product dashboard, mobile app API
• Internal Tools: Admin dashboard, analytics platform
• Shared Libraries: UI components, utilities, configs, API clients
• Documentation: Technical docs, API references, style guides

Why a Monorepo?

✅ Single Source of Truth: One repo, one version, one deploy
✅ Code Sharing: Share components, utils, and configs effortlessly
✅ Atomic Changes: Update shared code and all consumers in one PR
✅ Unified Tooling: One linting config, one testing setup
✅ Better Collaboration: See the whole picture, review across apps
✅ Faster CI/CD: Smart caching with Turborepo (up to 85% faster builds)

📊 Repository Statistics

• Total Lines of Code: ~850,000
• Active Contributors: 45 developers
• Monthly Deploys: ~280
• Test Coverage: 87%
• Build Time (with cache): 3.2 minutes
• Build Time (cold): 18 minutes

🏗 Monorepo Structure

acme-monorepo/
│
├── apps/                        # Production applications
│   ├── web/                     # Next.js marketing website
│   │   ├── src/
│   │   ├── public/
│   │   ├── package.json
│   │   └── next.config.js
│   │
│   ├── dashboard/               # React SaaS dashboard
│   │   ├── src/
│   │   ├── package.json
│   │   └── vite.config.ts
│   │
│   ├── docs/                    # Documentation site (Docusaurus)
│   │   ├── docs/
│   │   ├── blog/
│   │   └── docusaurus.config.js
│   │
│   ├── mobile-api/              # Node.js API for mobile apps
│   │   ├── src/
│   │   ├── package.json
│   │   └── tsconfig.json
│   │
│   └── admin/                   # Internal admin panel
│       ├── src/
│       └── package.json
│
├── packages/                    # Shared packages
│   ├── ui/                      # Shared React component library
│   │   ├── src/
│   │   │   ├── Button.tsx
│   │   │   ├── Card.tsx
│   │   │   └── index.ts
│   │   ├── package.json
│   │   ├── tsconfig.json
│   │   └── README.md
│   │
│   ├── utils/                   # Shared utility functions
│   │   ├── src/
│   │   │   ├── date.ts
│   │   │   ├── string.ts
│   │   │   └── validation.ts
│   │   └── package.json
│   │
│   ├── api-client/              # Shared API client
│   │   ├── src/
│   │   └── package.json
│   │
│   ├── eslint-config/           # Shared ESLint configuration
│   │   ├── index.js
│   │   ├── react.js
│   │   └── package.json
│   │
│   ├── tsconfig/                # Shared TypeScript configs
│   │   ├── base.json
│   │   ├── react.json
│   │   ├── node.json
│   │   └── package.json
│   │
│   ├── tailwind-config/         # Shared Tailwind config
│   │   ├── index.js
│   │   └── package.json
│   │
│   ├── database/                # Prisma database client
│   │   ├── prisma/
│   │   │   └── schema.prisma
│   │   ├── src/
│   │   └── package.json
│   │
│   └── auth/                    # Authentication library
│       ├── src/
│       └── package.json
│
├── tooling/                     # Development tools
│   ├── scripts/                 # Automation scripts
│   │   ├── setup.sh
│   │   └── deploy.sh
│   └── github/                  # GitHub actions
│       └── workflows/
│
├── .github/                     # GitHub configuration
│   ├── workflows/
│   │   ├── ci.yml
│   │   ├── deploy.yml
│   │   └── release.yml
│   └── CODEOWNERS
│
├── turbo.json                   # Turborepo configuration
├── package.json                 # Root package.json
├── pnpm-workspace.yaml          # pnpm workspaces config
├── .npmrc                       # npm configuration
├── tsconfig.json                # Base TypeScript config
├── .eslintrc.js                 # Root ESLint config
├── .prettierrc                  # Prettier config
└── README.md

🚀 Quick Start

Prerequisites

Ensure you have the following installed:

• Node.js: v20.0.0 or higher (Download)
• pnpm: v8.0.0 or higher (npm install -g pnpm)
• Git: Latest version

Initial Setup

# 1. Clone the repository
git clone https://github.com/acme/monorepo.git
cd monorepo

# 2. Install all dependencies (for all apps and packages)
pnpm install

# 3. Build all packages
pnpm build

# 4. Run development servers for all apps
pnpm dev

That's it! All apps should now be running:

• 🌐 Marketing Site: http://localhost:3000
• 📊 Dashboard: http://localhost:3001
• 📖 Docs: http://localhost:3002
• 🔧 Admin: http://localhost:3003
• 🚀 API: http://localhost:4000

Environment Setup

Copy environment files for each app:

# Marketing site
cp apps/web/.env.example apps/web/.env.local

# Dashboard
cp apps/dashboard/.env.example apps/dashboard/.env.local

# API
cp apps/mobile-api/.env.example apps/mobile-api/.env

Fill in your environment variables (see individual app READMEs for details).

📦 Applications

1. Marketing Website (apps/web)

Tech Stack: Next.js 14, React 18, Tailwind CSS
URL: https://acme.com
Purpose: Public-facing marketing and SEO-optimized content

Key Features:

• Static generation for landing pages (99 Lighthouse score)
• Blog with MDX support
• Multi-language support (English, Spanish, French)
• Contact forms with Resend integration
• Analytics with Vercel Analytics

Local Development:

cd apps/web
pnpm dev

📖 Full README

2. Dashboard (apps/dashboard)

Tech Stack: React 18, Vite, TanStack Query, Zustand
URL: https://app.acme.com
Purpose: Customer-facing SaaS dashboard

Key Features:

• Real-time data updates with WebSockets
• Complex data tables with virtualization
• Charts and analytics (Recharts)
• Multi-tenant architecture
• Role-based access control (RBAC)

Local Development:

cd apps/dashboard
pnpm dev

📖 Full README

3. Documentation Site (apps/docs)

Tech Stack: Docusaurus 3, React, Algolia
URL: https://docs.acme.com
Purpose: Developer documentation and API references

Key Features:

• Interactive code examples
• API reference auto-generated from OpenAPI
• Versioned documentation
• Full-text search with Algolia
• Dark mode support

Local Development:

cd apps/docs
pnpm dev

📖 Full README

4. Mobile API (apps/mobile-api)

Tech Stack: Node.js, Express, PostgreSQL, Redis
URL: https://api.acme.com
Purpose: REST API for mobile applications

Key Features:

• RESTful API with OpenAPI spec
• JWT authentication
• Rate limiting and caching
• Background jobs with Bull
• Monitoring with Sentry

Local Development:

cd apps/mobile-api
pnpm dev

📖 Full README

5. Admin Panel (apps/admin)

Tech Stack: React, Material-UI, React Admin
URL: https://admin.acme.com (internal)
Purpose: Internal admin tools for customer support and ops

Key Features:

• User management
• Analytics dashboards
• Feature flags management
• System health monitoring

Local Development:

cd apps/admin
pnpm dev

📖 Full README

📦 Shared Packages

UI Component Library (packages/ui)

Shared React components used across all applications.

Components (50+):

• Buttons, Cards, Modals, Tooltips
• Forms (Input, Select, Checkbox, Radio)
• Data Display (Tables, Lists, Badge)
• Feedback (Alert, Toast, Progress)
• Navigation (Tabs, Breadcrumb, Pagination)

Usage:

import { Button, Card, Modal } from '@acme/ui';

export default function MyComponent() {
  return (
    <Card>
      <Button variant="primary" size="lg">
        Click Me
      </Button>
    </Card>
  );
}

Storybook: http://localhost:6006 (pnpm storybook)

📖 Full Docs | 🎨 Storybook

Utilities (packages/utils)

Shared utility functions and helpers.

Categories:

• Date/Time: formatDate, parseDate, timeAgo
• String: truncate, slugify, capitalize
• Validation: isEmail, isURL, isPhoneNumber
• Array: groupBy, unique, sortBy
• Object: deepMerge, pick, omit
• Number: formatCurrency, formatNumber, clamp

Usage:

import { formatDate, truncate, isEmail } from '@acme/utils';

const date = formatDate(new Date(), 'MMM DD, YYYY');
const excerpt = truncate(longText, 100);
const valid = isEmail('user@example.com');

📖 Full Docs

API Client (packages/api-client)

Type-safe API client for communicating with backend services.

Features:

• Automatic TypeScript types from OpenAPI
• Built-in authentication
• Request/response interceptors
• Retry logic and error handling

Usage:

import { createClient } from '@acme/api-client';

const client = createClient({
  baseURL: process.env.API_URL,
  token: session.accessToken
});

// Fully typed requests
const user = await client.users.get('123');
const posts = await client.posts.list({ page: 1, limit: 10 });

📖 Full Docs

Database (packages/database)

Prisma database client shared across backend services.

Schema: PostgreSQL with Prisma ORM
Models: Users, Organizations, Posts, Comments, etc.

Usage:

import { prisma } from '@acme/database';

const users = await prisma.user.findMany({
  where: { active: true },
  include: { posts: true }
});

Migrations:

cd packages/database
pnpm prisma migrate dev
pnpm prisma generate

📖 Full Docs

Configuration Packages

ESLint Config (packages/eslint-config)

{
  "extends": ["@acme/eslint-config/react"]
}

TypeScript Config (packages/tsconfig)

{
  "extends": "@acme/tsconfig/react.json"
}

Tailwind Config (packages/tailwind-config)

module.exports = {
  presets: [require('@acme/tailwind-config')],
}

🛠 Development Workflows

Working on a Single App

# Run only the dashboard
pnpm --filter dashboard dev

# Run dashboard with dependencies
pnpm --filter dashboard... dev

# Build only the marketing site
pnpm --filter web build

Working on Multiple Apps

# Run specific apps
pnpm --filter "{web,dashboard}" dev

# Run all apps except docs
pnpm --filter "!docs" dev

Working on Shared Packages

# Watch mode for UI package
cd packages/ui
pnpm dev  # Rebuilds on file changes

# Apps importing @acme/ui will auto-reload

Adding Dependencies

# Add to a specific app
pnpm --filter web add lodash

# Add to a package
pnpm --filter @acme/utils add date-fns

# Add to root (dev dependencies)
pnpm add -D -w prettier

Creating a New App

# Use the generator
pnpm generate:app

# Or manually
mkdir apps/new-app
cd apps/new-app
pnpm init
# Add to pnpm-workspace.yaml

Creating a New Package

# Use the generator
pnpm generate:package

# Or manually
mkdir packages/new-package
cd packages/new-package
pnpm init

🏗 Build System (Turborepo)

How Turborepo Works

Turborepo intelligently caches build outputs and only rebuilds what changed.

Benefits:

• ⚡ 85% faster builds with caching
• 🎯 Smart scheduling of parallel tasks
• 🔄 Remote caching shared across team
• 📊 Build visualization to debug slowness

Turbo Configuration

// turbo.json
{
  "pipeline": {
    "build": {
      "dependsOn": ["^build"],
      "outputs": ["dist/**", ".next/**"]
    },
    "dev": {
      "cache": false,
      "persistent": true
    },
    "lint": {
      "outputs": []
    },
    "test": {
      "outputs": ["coverage/**"]
    }
  }
}

Common Turbo Commands

# Build everything
pnpm build

# Build with remote caching
pnpm build --remote-cache

# Force rebuild (ignore cache)
pnpm build --force

# Dry run (see what would run)
pnpm build --dry-run

# See dependency graph
pnpm turbo run build --graph

# Clear cache
pnpm turbo prune

Pipeline Visualization

# Generate visual graph
pnpm turbo run build --graph=graph.html

Opens an interactive graph showing:

• Build dependencies
• Parallel vs sequential tasks
• Cache hits/misses
• Execution time per task

🧪 Testing

Test Strategy

Type	Tool	Coverage
Unit Tests	Vitest	87%
Component Tests	React Testing Library	82%
E2E Tests	Playwright	Critical paths
Visual Tests	Chromatic	UI components
API Tests	Supertest	91%

Running Tests

# Run all tests
pnpm test

# Run tests for specific app
pnpm --filter dashboard test

# Run tests in watch mode
pnpm test:watch

# Run E2E tests
pnpm test:e2e

# Generate coverage report
pnpm test:coverage

Writing Tests

// packages/ui/src/Button.test.tsx
import { render, screen } from '@testing-library/react';
import { Button } from './Button';

describe('Button', () => {
  it('renders with correct text', () => {
    render(<Button>Click me</Button>);
    expect(screen.getByText('Click me')).toBeInTheDocument();
  });
  
  it('handles click events', () => {
    const handleClick = vi.fn();
    render(<Button onClick={handleClick}>Click</Button>);
    screen.getByText('Click').click();
    expect(handleClick).toHaveBeenCalledOnce();
  });
});

🚀 Deployment

CI/CD Pipeline

GitHub Actions workflow runs on every push:

1. ✅ Lint - ESLint, Prettier
2. ✅ Type Check - TypeScript compilation
3. ✅ Test - Unit, integration, E2E
4. ✅ Build - All apps and packages
5. ✅ Deploy - Vercel (preview) or Production

Deployment Targets

App	Platform	URL
Marketing	Vercel	https://acme.com
Dashboard	Vercel	https://app.acme.com
Docs	Vercel	https://docs.acme.com
API	Railway	https://api.acme.com
Admin	Vercel	https://admin.acme.com

Manual Deployment

# Deploy all apps
pnpm deploy

# Deploy specific app
pnpm --filter web deploy

# Deploy to staging
pnpm deploy:staging

# Deploy to production (requires approval)
pnpm deploy:production

Environment Variables

Each app has its own environment variables:

# Marketing site
apps/web/.env.local

# Dashboard
apps/dashboard/.env.local

# API
apps/mobile-api/.env

Never commit .env files! Use Vercel/Railway secret management.

📊 Monitoring & Observability

Tools Used

• Error Tracking: Sentry
• Performance: Vercel Analytics
• Uptime: Better Uptime
• Logs: Datadog
• APM: New Relic (API only)

Health Checks

# Check all services
pnpm health:check

# Output:
# ✅ web: https://acme.com (200 OK)
# ✅ dashboard: https://app.acme.com (200 OK)
# ✅ api: https://api.acme.com/health (200 OK)

🔧 Troubleshooting

Common Issues

Problem: "Cannot find module '@acme/ui'"

# Solution: Build packages first
pnpm build
# Or run in watch mode
cd packages/ui && pnpm dev

Problem: "Port 3000 already in use"

# Solution: Kill process or use different port
pnpm --filter web dev -- --port 3001

Problem: "Out of memory during build"

# Solution: Increase Node memory
export NODE_OPTIONS="--max-old-space-size=4096"
pnpm build

Problem: "Changes in package not reflecting in app"

# Solution: Clear Turborepo cache
pnpm turbo prune
pnpm build

Getting Help

1. Check the internal wiki
2. Ask in #eng-help Slack channel
3. Open an issue with [HELP] prefix
4. Ping @platform-team for urgent issues

📚 Documentation

• Architecture: docs/ARCHITECTURE.md
• Contributing: CONTRIBUTING.md
• Security: SECURITY.md
• Code of Conduct: CODE_OF_CONDUCT.md
• Release Process: docs/RELEASES.md

🤝 Contributing

Development Process

1. Create a branch: git checkout -b feature/amazing-feature
2. Make changes: Follow our coding standards
3. Write tests: Maintain or improve coverage
4. Run checks: pnpm lint && pnpm test && pnpm build
5. Commit: Use conventional commits
6. Push: git push origin feature/amazing-feature
7. Open PR: Fill out the PR template
8. Get reviews: At least 2 approvals required
9. Merge: Squash and merge to main

Commit Convention

# Format: type(scope): message

feat(dashboard): add user analytics chart
fix(ui): resolve button hover state bug
docs(readme): update installation steps
chore(deps): upgrade react to v18.3
test(utils): add date formatting tests

Code Review Guidelines

• ✅ Keep PRs small (<500 lines)
• ✅ Write descriptive PR titles
• ✅ Add screenshots for UI changes
• ✅ Update relevant documentation
• ✅ Ensure CI passes before requesting review

📊 Repository Metrics

Code Quality

Dependencies

# Check for outdated packages
pnpm outdated

# Update all dependencies
pnpm update -r

# Check for vulnerabilities
pnpm audit

Bundle Sizes

App	Initial JS	Total Size	Lighthouse
Marketing	85 KB	320 KB	99/100
Dashboard	210 KB	890 KB	94/100
Docs	120 KB	450 KB	98/100

🛡 Security

• Dependency Scanning: Dependabot (weekly)
• Secret Scanning: GitHub Advanced Security
• SAST: SonarCloud
• Penetration Testing: Quarterly audits
• Security Policy: SECURITY.md

📄 License

This monorepo is proprietary and confidential.
© 2024 ACME Corporation. All rights reserved.

Unauthorized copying, distribution, or use is strictly prohibited.

👥 Team & Ownership

Platform Team (@platform-team)

• Lead: @alice (alice@acme.com)
• Members: @bob, @carol, @dave
• Responsibilities: Infrastructure, CI/CD, monorepo tooling

Product Teams

Marketing (@marketing-team)

• Owner: @emily
• Apps: web, docs

Product (@product-team)

• Owner: @frank
• Apps: dashboard

API (@api-team)

• Owner: @grace
• Apps: mobile-api

Internal Tools (@tools-team)

• Owner: @henry
• Apps: admin

🔗 Useful Links

• Internal Wiki: https://wiki.acme.com
• Design System: https://design.acme.com
• Component Storybook: https://storybook.acme.com
• Status Page: https://status.acme.com
• Metrics Dashboard: https://metrics.acme.com
• Slack: #engineering channel

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Questions? Ask in #eng-help or email platform@acme.com

Last Updated: December 2024
Maintainers: @platform-team