Machine Learning-Based Flight Delay Predictor

Project: End-to-End Flight Delay Prediction Web App
Project Summary
This project is a complete, full-stack machine learning application that predicts flight delays in real-time. I managed the entire development lifecycle, from processing raw data and training an advanced predictive model to deploying it within a secure, professional, and user-friendly web interface. The final product is a robust, multi-page web app where authenticated users can input flight details via interactive dropdowns and receive an instant, accurate forecast of potential delays.

Key Features
Secure User Authentication: A complete login/logout system protects the predictor tool, ensuring only authorized users can access it.

Real-Time Prediction Engine: Utilizes a high-performance LightGBM model, delivered via a Flask backend, to provide instant predictions.

Professional, Multi-Page UI/UX: A sleek, dark-themed, and fully responsive user interface that includes a Home page, an About page, and a secure Predictor dashboard.

Interactive & Error-Proof Data Input: User-friendly dropdown menus for categorical data (Airlines, Airports) prevent user errors and ensure only valid data is sent to the model.

Dynamic Prediction Feedback: The interface provides clear, color-coded results, indicating if a flight is predicted to be DELAYED, ON-TIME, or EARLY.

My Technical Process
Machine Learning Pipeline (The "Brain"):

Developed a professional training script using Object-Oriented Python for clean, reusable code.

Engineered a robust Scikit-learn Pipeline to bundle all preprocessing steps (like one-hot encoding) with the final model. This industry-standard practice prevents data leakage and makes the model production-ready.

Trained and evaluated a state-of-the-art LightGBM Regressor, a model renowned for its high performance, using K-Fold Cross-Validation to ensure reliable and accurate performance metrics.

Backend Development (The "Engine"):

Built the web server and application logic using the Flask framework in Python.

Implemented RESTful routes to handle all pages (/home, /about, /predictor, etc.).

Managed secure user sessions for the login/logout functionality.

Created the /predict API endpoint that takes raw user input, transforms it into the format required by the model, and returns a real-time prediction.

Frontend Development (The "Face"):

Designed and coded a clean, modern, and fully responsive user interface using professional HTML5 and CSS3.

Ensured a seamless and consistent user experience across the login form, informational pages, and the main predictor dashboard.

Technology Stack
Python | Flask

Machine Learning: Scikit-learn | LightGBM | Pandas

Frontend: HTML5 | CSS3