Flight Price Prediction Model
This project involves building a machine learning model to predict flight prices based on various features such as airline, flight details, departure and arrival times, duration, stops, and more. It leverages regression algorithms to provide price predictions, helping travelers and businesses forecast flight costs efficiently.
Project Objective
The objective of this project is to predict flight prices based on different factors using machine learning techniques. The model is designed to take various flight attributes like airline, class, departure time, and more to predict an accurate price for a given flight.
Steps Involved:
Data Preparation:
- Clean the flight data by handling missing values and removing any inconsistencies.
- Preprocess categorical features (e.g., airline, class, source city) using Label Encoding.
- Scale numerical features (e.g., duration, days left) using MinMaxScaler for model optimization.
Model Selection:
- Trained a regression model (e.g., Extra Trees Regressor) to predict flight prices based on the features.
- Evaluated the model’s performance and fine-tuned it to improve accuracy.
Model Training:
- Fitted the regression model to the training data and tested it on unseen data.
- Predicted flight prices for the test data and compared the predicted results with actual values.
Evaluation:
- Assessed the model's performance using metrics like Mean Absolute Error (MAE) or R-squared.
- Visualized the results to understand the factors influencing the flight price predictions.
Flight Price Prediction Results:
After training the model, we were able to predict flight prices based on the given features. The model's predictions can now be used to help travelers and businesses estimate flight costs with a good degree of accuracy.
Example Flight Predictions:
For example, given a flight from Delhi to Mumbai with a duration of 2.17 hours, economy class, and an early morning departure, the predicted price for the flight was approximately ₹5953.
Results:
By applying machine learning models to predict flight prices, businesses can improve pricing strategies and offer more accurate cost estimates to customers. This model provides valuable insights into flight pricing dynamics, helping to enhance customer satisfaction and operational efficiency.
Images from the Project
