Plane Ticket Price Prediction using ML

Keywords: Plane Ticket Price, prediction, ML Algorithms, Regression

Background

Predicting plane ticket prices accurately is a complex task due to the dynamic nature of the airline industry. This study aims to develop a model that utilizes machine learning techniques to predict ticket prices based on historical data and relevant influencing factors. The model analyses various features such as departure and arrival locations, travel dates, airline carriers, and flight durations. By training the model on historical ticket price data, it learns patterns and can make predictions on unseen data. To enhance prediction accuracy, the model incorporates real-time data sources such as fuel prices, currency exchange rates, and airline market trends. This ensures that the model adapts to current market conditions and captures sudden changes that may impact ticket prices

Aim & Objectives

Aim
The aim of plane ticket price prediction is to forecast future ticket prices accurately and help travellers make informed decisions
Objective
• Loading the dataset
• Cleaning and pre-processing
• Outlier imputation using required methods
• Data Visualization and Statistical testing’s
• Building the machine learning model

Research Methodology

Various regression Algorithms like KNNR, SVR, RF, DT and XGBoost

Software & Tools

Jupyter, Google Collab

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