https://github.com/gurramankit/flight_price_predection_ml_randomforestregression

https://github.com/gurramankit/flight_price_predection_ml_randomforestregression

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• Host: GitHub
• URL: https://github.com/gurramankit/flight_price_predection_ml_randomforestregression
• Owner: gurramankit
• Created: 2024-11-07T18:01:58.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-07T18:11:24.000Z (2 months ago)
• Last Synced: 2024-11-07T19:19:26.993Z (2 months ago)
• Language: Jupyter Notebook
• Size: 4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
#Flight Price Prediction using Random Forest Regression

Project Overview

This project is a Machine Learning model designed to predict flight ticket prices based on various factors, such as airline, departure time, arrival time, source, destination, and other relevant features. The primary aim is to provide accurate price predictions that can assist users or companies in understanding and forecasting ticket prices, optimizing booking times, or offering better price recommendations. The model is built using a Random Forest Regression algorithm due to its robustness and accuracy in handling complex data with nonlinear relationships.

Project Link

Link to Flight Price Prediction Project

Project Details

Data Preprocessing:

The dataset is first cleaned and preprocessed. This includes handling missing values, encoding categorical variables (like airlines and city names), and feature engineering to extract useful information, such as the duration of flights.

Feature Engineering:

Extracted features like departure time, arrival time, day of travel, and route patterns. These features are essential to capturing pricing trends.

Model Selection:

Used the Random Forest Regression model as it performs well on structured data, handling high dimensionality effectively and capturing complex relationships between features.

Evaluation Metrics:

The model’s performance was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), providing insight into its prediction accuracy and reliability.

Model Deployment (Optional):

The trained model can be integrated into applications where users input flight parameters, and the model returns a predicted price.

Key Results

The Random Forest Regression model demonstrated a solid ability to predict flight prices with reasonable accuracy, highlighting the correlation between various features and ticket prices.

Requirements

Python 3.x

Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn (for data visualization)