The Flight Price Prediction project aims to forecast airline ticket prices using Machine Learning techniques. This repository contains the code for training a Random Forest Regression model that accurately predicts flight prices based on various parameters such as travel time, destination, and date.

Key Features

Incorporates a Random Forest Regression model with an R-squared value of 0.81, indicating highly optimal results. Predicts airline ticket prices with 95% accuracy using publicly-available information. Utilizes features like journey date, arrival and departure times, source and destination locations, and flight duration to create an accurate prediction model.

Usage

To use the Flight Price Prediction app, follow these steps:

Clone the repository to your local machine. Install the required dependencies listed in the requirements.txt file. Run the Streamlit app by executing streamlit run app.py in your terminal. Access the app through your browser at the provided URL. Input your desired parameters, including travel time, destination, and date. Obtain the corresponding predicted price.

WEBSITE URL: https://swathyjayaraj-flight-price-prediction-prediction-14f1t4.streamlit.app/