This project aims to perform exploratory data analysis (EDA) and develop machine learning models for predicting flight prices between cities. The goal is to analyze historical flight data, identify patterns, and build regression models to accurately predict flight prices.
The dataset used in this project contains historical flight information, including details such as departure city, destination city, departure time, arrival time, airline, and ticket price.
• data/: Directory containing the dataset files.
• notebooks/: Jupyter notebooks for exploratory data analysis and model development.
• data_cleaning_visualization.ipynb: Notebook for EDA.
• price_prediction.ipynb: Notebook for model development and evaluation.
• README.md: Project overview and instructions.
The EDA notebook explores the dataset to gain insights into the features, distributions, correlations, and any other relevant patterns. Visualizations and statistical summaries are used to understand the data better.
The model development notebook focuses on building machine learning models for predicting flight prices. Various regression algorithms such as Linear Regression, Ridge Regression, KNN are explored and evaluated for their performance.
The project requires Python 3.x and the following libraries:
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• geopandas
• jupyter