https://github.com/samjoesilvano/airline_ticket_fare_prediction

Airline Fare Prediction using Machine Learning focuses on developing a Random Forest model to predict flight prices, achieving an R² score of 0.804. The project includes hyperparameter tuning using RandomizedSearchCV, alongside extensive data preprocessing and feature engineering to ensure robust model performance.
https://github.com/samjoesilvano/airline_ticket_fare_prediction

airline-fare-prediction data-preprocessing data-visualization feature-engineering feature-selection hyperparameter-tuning machine-learning pandas python random-forest randomizedsearchcv regression-analysis scikit-learn

Last synced: 24 days ago
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Airline Fare Prediction using Machine Learning focuses on developing a Random Forest model to predict flight prices, achieving an R² score of 0.804. The project includes hyperparameter tuning using RandomizedSearchCV, alongside extensive data preprocessing and feature engineering to ensure robust model performance.

• Host: GitHub
• URL: https://github.com/samjoesilvano/airline_ticket_fare_prediction
• Owner: SamJoeSilvano
• Created: 2024-08-25T07:58:59.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-10-16T19:07:24.000Z (4 months ago)
• Last Synced: 2024-11-27T11:44:48.328Z (3 months ago)
• Topics: airline-fare-prediction, data-preprocessing, data-visualization, feature-engineering, feature-selection, hyperparameter-tuning, machine-learning, pandas, python, random-forest, randomizedsearchcv, regression-analysis, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 9.07 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Airline Fare Prediction using Machine Learning

## Project Overview

This project aims to predict airline fares using a Random Forest machine learning model. The model demonstrates strong predictive accuracy, achieving an R² score of **0.804**. This predictive capability can assist travelers and businesses in making informed decisions regarding flight pricing.

## Table of Contents

- [Installation](#installation)

- [Usage](#usage)

- [Data Preprocessing](#data-preprocessing)

- [Model Development](#model-development)

- [Results](#results)

- [Technologies Used](#technologies-used)

- [License](#license)

## Installation

To run this project, you need to have Python 3.6 or later installed. You can set up your environment by installing the required packages using pip:

pip install pandas numpy scikit-learn

## Usage

1. Clone the repository:

   git clone https://github.com/SamJoeSilvano/Airline_Ticket_Fare_Prediction.git

2. Navigate to the project directory:

   cd airline-fare-prediction

3. Run the main script:

   python main.py

## Data Preprocessing

The dataset is preprocessed to ensure high quality and accuracy in predictions. Key steps include:

- Data cleaning to remove any missing or inconsistent entries.

- Feature engineering, including feature selection and encoding of categorical variables.

- Splitting the data into training and testing sets.

## Model Development

The model development involves:

- Using the **Random Forest** algorithm for regression.

- Hyperparameter tuning with **RandomizedSearchCV** to optimize model performance.

- Evaluating the model using metrics such as R² score and Mean Absolute Error (MAE).

## Results

The Random Forest model achieved an R² score of **0.804**, indicating a strong predictive capability for airline fare predictions.

## Technologies Used

- Python

- Scikit-learn

- Pandas

- NumPy

- Jupyter Notebook (for exploratory analysis and visualization)

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.