Flight-Price-Prediction-using-Linear-and-Ridge-Regression

This repository contains a Jupyter Notebook implementation of Linear regression and Ridge Regression from scratch for determining the Flight price prediction.

data: This folder contains the dataset files required for the project.

Flight_price_prediction.csv contains airline, flight, source_city, departure_time, stops, arrival_time, destination_city, class duration, days_left, price

Dependencies To run the Jupyter Notebook and reproduce the results, you will need the following dependencies: Python,Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn (for comparison and evaluation).

You can install the required packages using the following command:

 pip install jupyter numpy pandas matplotlib seaborn scikit-learn

Usage Clone this repository to your local machine using Git or download the file.

License This project is licensed under the MIT License.

Feel free to use, modify, and distribute the code as needed.

Feel free to customize the README to include any additional information or instructions specific to your project. Make sure to provide clear explanations and details so that users can easily understand and replicate your work.