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Predict flight prices using machine learning. This project involves data preprocessing, exploratory analysis, feature engineering, and model training with various regression algorithms to accurately predict flight prices based on factors such as airline, source, destination, duration, stops, and additional info

Developeda machine learning model to predict flight ticket prices, aiming to improve fare estimation accuracy by analyzing historical flight data and patterns

Claude Code skill for searching Google Flights and generating structured flight price comparison reports. Supports flexible round-trip search, LCC baggage cost calculation, and leave day estimation. 機票比價 | 便宜機票 | 航班搜尋

Problem Statement: Airlines implement dynamic pricing for their tickets, and base their pricing decisions on demand estimation models. The reason for such a complicated system is that each flight only has a set number of seats to sell, so airlines have to regulate demand. In the case where demand is expected to exceed capacity, the airline may increase prices, to decrease the rate at which seats fill. On the other hand, a seat that goes unsold represents a loss of revenue, and selling that seat for any price above the service cost for a single passenger would have been a more preferable scenario. Anyone who has booked a flight ticket knows how unexpectedly the prices vary. The cheapest available ticket on a given flight gets more and less expensive over time. This usually happens as an attempt to maximize revenue based on - 1. Time of purchase patterns (making sure last-minute purchases are expensive) 2. Keeping the flight as full as they want it (raising prices on a flight which is filling up in order to reduce sales and hold back inventory for those expensive last-minute expensive purchases) So, you have to work on a project where you collect data of flight fares with other features and work to make a model to predict fares of flights. This project contains three phase- 1. Data Collection: I have done web scraping to collect the data of flight ticket prices from the well known website https://www.yatra.com/ where I found more features of flights compared to other websites and I fetch data for different locations. As per the requirement we need to build the model to predict the prices of different flight tickets. 2. Data Analysis: After cleaning the data, we have to do some analysis on the data. Do airfares change frequently? Do they move in small increments or in large jumps? Do they tend to go up or down over time? What is the best time to buy so that the consumer can save the most by taking the least risk? Does price increase as we get near to departure date? Is Indigo cheaper than Jet Airways? Are morning flights expensive? 3. Model Building: After collecting the data, we need to build a machine learning model. Before model building do all data pre-processing steps. Try different models with different hyper parameters and select the bestmodel. Follow the complete life cycle of data science. Include all the steps like 1. Data Cleaning 2. Exploratory Data Analysis 3. Data Pre-processing 4. Model Building 5. Model Evaluation 6. Selecting the best model

Building a ML project to predict price of flight when information such as Airline, Source, Destination, Date of Journey, Departure Time, Arrival Time, Duration, Number of Stops, Route, Additional_Info are provided

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Data Science Project

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The project is about flight price prediction

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Flight Price Estimation — A practical, end-to-end project that predicts airline ticket prices. I cleaned and engineered features from journey data, built preprocessing pipelines, and trained models (Random Forest, XGBoost). Includes EDA, tuning, evaluation (MAE/RMSE) and a ready-to-run inference notebook.

Production-ready flight price prediction using ensemble models (RF + XGBoost + LightGBM) with AWS ECS deployment

This model predict price for various airlines on different routes

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AI-powered flight price prediction system using Machine Learning (RandomForestRegressor) with real-time fare estimation, confidence scoring, and price sentiment analysis.

Build a machine learning model to predict flight prices based on features such as airline, journey details, and flight duration. The aim is to provide actionable insights for travelers and assist in price estimation.

AI-powered flight price prediction using Random Forest algorithm with 81% accuracy. Interactive Flask web app covering major Indian airlines and routes, featuring real-time price estimation with 29 engineered features.

Flight Price Prediction & Analysis ✈️💰 A web app that predicts flight ticket prices based on duration, days left, departure & arrival hours using Machine Learning. Built with Flask, Python, and LightGBM, it provides real-time price estimation. 🔧 Tech Stack: Flask, Python, Scikit-learn, LightGBM, Pandas, NumPy, HTML, CSS

This project focuses on predicting flight ticket prices using machine learning techniques. It uses a structured flight dataset containing journey, airline, route, duration, stop, and timing information to build predictive models for airfare estimation.

✈️ Leaderboard (Top 8%): The project focuses on building a robust regression model capable of accurately estimating flight prices for unseen test data, which can assist in travel cost estimation and pricing strategies.

A Flask-based web app deployed on Render that predicts flight ticket prices using a machine learning model. It processes user inputs, applies feature engineering, and utilizes a trained regression model for real-time price estimation.

A smart ML-powered tool that predicts flight fares based on airline, departure time, stops, and other key features. Fast, accurate, and easy-to-use for real-time price estimation.

Flight Fare Prediction ✈️ A machine learning project that predicts flight ticket prices based on factors like airline, departure time, duration, and stops. Uses regression models (Linear Regression, Random Forest) for accurate fare estimation. Built with Python, Pandas, Scikit-Learn, and Flask for deployment. 🚀

This project predicts flight ticket fares using advanced Machine Learning techniques and Artificial Neural Networks (ANNs). It involves thorough data preprocessing, feature engineering, and model tuning to deliver accurate price predictions, helping in real-world fare estimation.

Utilized Python, MySQL, and JavaScript to collect, clean, process, and visualize flight data. Achieved 94% accuracy in ticket price estimation and trip planning. Enhanced user analytics with NumPy, Pandas, SciPy, and optimized performance through SQL queries. Improved data visualization and user experience, increasing activity by 30%.