Flight Fare Prediction

The Flight Fare Prediction project leverages a Random Forest algorithm to predict flight fares based on several input features, including airline, date of journey, source, destination, and more. The model is deployed as a web application using Flask for the backend, with JavaScript used for client-side data validation to ensure data accuracy and integrity.

This project is designed to predict the price of flight tickets, making it an essential tool for users looking to estimate the cost of their travels. The model is trained on a comprehensive dataset containing flight details and fare information, ensuring accurate predictions for various flight parameters.

Features:

• Predict flight fares based on multiple input features like airline, date, source, and destination.
• Web interface built with Flask, providing an easy-to-use platform for fare prediction.
• Client-side data validation implemented with JavaScript to ensure that input data is correct before submitting.
• Accurate prediction using a Random Forest model trained on a large dataset of flight information.

How to Use:

1. Enter the required flight details such as airline, source, destination, and journey date in the provided web form.
2. Click the "Predict Fare" button to initiate the prediction process.
3. The predicted fare for your flight will be displayed on the screen.

Model Details:

• Algorithm: Random Forest
• Libraries: scikit-learn, pandas, numpy
• Training Data: The model is trained on a large dataset containing historical flight details and their corresponding fares.

Technologies Used:

• Random Forest Algorithm
• Python (Flask)
• JavaScript (for client-side validation)
• Libraries: scikit-learn, pandas, numpy