flight-price-prediction

Predicting flight ticket prices using a random forest regression model based on scraped data from Kayak. A Kayak scraper is also provided.

Data Science & Machine Learning Internship at Flip Robo Technologies

This project aims to provide users with a tool to predict flight fares based on various parameters, allowing them to make informed decisions when booking air travel. The app utilizes machine learning algorithms trained on historical flight data to estimate future fares.

Data Science Projects done at Data Trained Education during PG Data Science & ML Course

REST API for airline information including IATA/ICAO codes, fleet data, and home airports. Search 1,000+ airlines worldwide.

Flight Fare Prediction UI built with React + Vite. Select From/To cities and a future travel date to get an estimated fare (USD) from a backend prediction API.

Analyze and Predict the Flight Price Using Machine Learning Models and Plotly Library

The main goal is to predict the fares of the flights based on different factors available in the provided dataset.

A flight price prediction website that works on the Random Forest model for predicting flight fares. The model is then hosted using Flask API.

A Machine Learning Web Application built with Flask to predict flight fares based on user-selected options like airline, city, departure time, number of stops, class, etc.

The primary goal is to develop a predictive model that leverages historical data, machine learning algorithms, and real-time market trends to empower users with insights for informed decision-making in air travel planning.

Predictive model to forecast flight prices.

Built flight fare prediction and deploy using flask on Heroku platform

A Flight price prediction application which predicts fares of flight for a particular date based on various parameters like Source, Destination, Stops & Airline.

A Flight Price Prediction System, which is a machine learning-based web application. Users can predict the cost of a flight based on their desired travel details. The project has been implemented using the Streamlit framework for hosting the application and providing a user interface for flight price predictions.

ML system predicting Indian domestic airfares (R2 0.9165, 94.1% accuracy) and recommending cost-effective bookings, with validated ranking reliability

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A comprehensive project predicting flight prices and customer satisfaction using machine learning models, deployed through interactive Streamlit apps.