payalsinghz/Predictive-Analytics-Insights-API

# 🚀 Predictive Analytics & Insights API ## 📌 Overview This project exposes ML-powered REST APIs that enable organizations to integrate predictive analytics directly into their applications. The system supports multiple machine learning workflows, automated feature engineering, and scalable model serving while maintaining low-latency responses. ### Key Highlights - Classification, Regression, and Anomaly Detection APIs - FastAPI-based high-performance REST architecture - Automated feature engineering pipelines - MongoDB aggregation optimization with compound indexing - Sub-50ms query response times - Role-Based Access Control (RBAC) - Multi-tenant architecture - Model versioning and lifecycle management - GitHub Actions CI/CD integration ## 🏗️ Tech Stack | Category | Technology | |-----------|------------| | Backend | FastAPI, Python | | Machine Learning | Scikit-learn | | Data Processing | Pandas, NumPy | | Database | MongoDB | | Authentication | JWT, RBAC | | DevOps | GitHub Actions | | API Docs | OpenAPI, Swagger UI | ## 📂 Project Structure Predictive-Analytics-Insights-API/ │ ├── app/ │ ├── api/ │ ├── services/ │ ├── models/ │ ├── schemas/ │ └── utils/ │ ├── ml/ │ ├── training/ │ ├── inference/ │ └── feature_engineering/ │ ├── database/ ├── tests/ ├── .github/workflows/ ├── requirements.txt └── README.md ## ✨ Features ### 📊 Machine Learning Inference - Classification Predictions - Regression Forecasting - Anomaly Detection & Scoring - Batch and Real-Time Predictions ### ⚡ Performance Optimization - MongoDB compound indexing - Optimized aggregation pipelines - Low-latency inference workflows - Efficient data preprocessing ### 🔒 Security - JWT Authentication - Role-Based Access Control (RBAC) - Multi-Tenant Data Isolation - Secure API Access ### 🚀 DevOps & MLOps - Model Versioning - Automated CI/CD Pipelines - GitHub Actions Integration - Automated Testing Workflows ## 🎯 API Endpoints ### Classification POST /predict/classification Predict categorical outcomes using trained classification models. ### Regression POST /predict/regression Generate numerical predictions and forecasts. ### Anomaly Detection POST /predict/anomaly Identify unusual patterns and outliers in incoming data. ## 🚀 Getting Started ### Clone the Repository git clone https://github.com/payalsinghz/Predictive-Analytics-Insights-API.git cd Predictive-Analytics-Insights-API ### Create Virtual Environment python -m venv venv ### Activate Environment #### Windows venv\Scripts\activate #### Linux / macOS source venv/bin/activate ### Install Dependencies pip install -r requirements.txt ### Run the Application uvicorn app.main:app --reload ## 📖 API Documentation After starting the server: ### Swagger UI http://localhost:8000/docs ### ReDoc http://localhost:8000/redoc ## 📈 Example Use Cases - Customer Churn Prediction - Fraud Detection - Demand Forecasting - Predictive Maintenance - Risk Assessment - Business Intelligence Platforms - Enterprise Analytics Solutions ## 🧪 Testing Run all tests: pytest ## 📊 Performance Metrics - Sub-50ms optimized query response times - FastAPI asynchronous request handling - Scalable ML inference architecture - Efficient MongoDB aggregation pipelines ## 🔮 Future Enhancements - Docker & Kubernetes Deployment - Real-Time Streaming Analytics - Explainable AI (XAI) - Automated Model Retraining - Monitoring & Observability Dashboard ## 👩‍💻 Author **Payal Singh** GitHub: https://github.com/payalsinghz