airlines tickets regression

project — a Machine Learning Web Application that predicts flight ticket prices based on multiple factors like airline, flight duration, stops, travel class, and more.

Key Highlights:

End-to-end ML pipeline: Data preprocessing, feature engineering, model training, and evaluation.

Tried multiple algorithms (Linear Regression, Random Forest, Gradient Boosting, XGBoost) and selected the best-performing one.

Built a Flask-based web app for real-time predictions.

Designed a user-friendly interface to make predictions easy and accessible.

Tech Stack: Python, Pandas, Scikit-learn, XGBoost, Flask, HTML/CSS.

The app predicts flight prices with impressive accuracy — a step forward in making air travel cost prediction more transparent and data-driven.