Flight Price Prediction using Different Regression Techniques Description This GitHub repository contains a flight price prediction project implemented in Python, focusing on various regression techniques. The project aims to predict flight prices based on historical data, enabling travelers to make informed decisions and plan their trips more efficiently.
Dataset Source The dataset used for this project is obtained from ['/kaggle/input/flight-price-prediction/Clean_Dataset.csv']. It includes features such as departure and arrival locations, date of travel, airline information, flight duration, and historical flight prices. The dataset is preprocessed and cleaned to ensure the quality and reliability of the predictions.
Regression Techniques Explored The project explores several regression techniques to predict flight prices, including but not limited to:
Linear Regression
Cat Boosting Regression
Each technique is implemented and evaluated, providing insights into their performance in predicting flight prices.
How to Use the Project Clone or download this GitHub repository to your local machine.
Ensure you have Python 3.x and the required libraries installed on your system. You can install the necessary packages using pip:
Copy code pip install numpy pandas scikit-learn matplotlib seaborn Obtain the flight price dataset from ['/kaggle/input/flight-price-prediction/Clean_Dataset.csv'] and place it in the project directory.
Open the Jupyter Notebook or Python scripts provided in the repository using Jupyter Notebook or your preferred Python environment.
Run the code to preprocess the data, split it into training and testing sets, and implement different regression techniques.
Evaluate the performance of each regression model using metrics such as mean absolute error, mean squared error, and R-squared.
Experiment with different hyperparameters, feature engineering techniques, or other regression algorithms to further improve prediction accuracy.
Contribution and Feedback Contributions to this project are welcome! If you have any suggestions, enhancements, or additional regression techniques to add, feel free to create a pull request. Your feedback and contributions will help enhance the accuracy and effectiveness of flight price predictions.
Let's collaborate to create a robust flight price prediction model that benefits travelers and travel industries alike.
Impact By sharing this project on GitHub, we hope to contribute to the field of data science and predictive analytics. Accurate flight price predictions can assist travelers in making budget-friendly travel plans and help airlines and travel agencies optimize pricing strategies.
Predictive models for flight prices can also aid in understanding the factors affecting pricing fluctuations, assisting airlines in better revenue management and resource allocation.
We believe that the fusion of machine learning and travel industry data can pave the way for more efficient and seamless travel experiences for people worldwide. Together, let's harness the power of data to improve the way we travel. Happy predicting!