Flight Price Prediction

This project predicts flight prices using machine learning techniques in Python. The workflow is implemented in a Jupyter Notebook and uses a cleaned dataset (Clean_Dataset.csv).

Features

• Data exploration and visualization
• Data preprocessing and feature engineering
• One-hot encoding for categorical variables
• Regression model training (Random Forest)
• Model evaluation (R2, MAE, MSE, RMSE)
• Feature importance analysis
• Visualization of results

Requirements

• Python 3.11+
• Jupyter Notebook
• pandas
• numpy
• matplotlib
• scikit-learn

A virtual environment (env/) is included. Activate it before running the notebook:

source env/bin/activate

Usage

1. Activate the virtual environment:

   source env/bin/activate

2. Start Jupyter Notebook:

   jupyter notebook

3. Open main.ipynb and run the cells sequentially.

Workflow Overview

1. Load Data: Read and explore the cleaned flight dataset.
2. Preprocessing: Drop unnecessary columns, encode categorical features, and transform data for modeling.
3. Model Training: Split data, train a Random Forest regressor, and evaluate performance.
4. Analysis: Visualize actual vs. predicted prices and analyze feature importances.

File Structure

• main.ipynb: Main notebook with all code and analysis steps.
• Clean_Dataset.csv: Cleaned flight data for modeling.
• env/: Python virtual environment with required packages.

License

This project is for educational purposes.