Flight Price Prediction

An application for predicting flight prices using machine learning. The structured branch contains the data pipeline, model, and API setup necessary to support predictions.

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🚀 Table of Contents

• Overview
• Features
• Repo Structure
• Getting Started
  • Prerequisites
  • Setup and Installation
  • Running the API
• Usage
• Testing
• Tech Stack
• License

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Overview

This project uses historical flight data and statistical / machine learning methods to predict flight ticket prices. The prediction model is exposed via a REST API so that it can be used by frontends or other services.

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Features

• Data pipeline for cleaning, feature engineering, model training.
• Predictive model serialized (.pkl) for inference.
• REST API endpoints to get price predictions.
• Logging configuration for debugging / monitoring.
• Structured layout, tests, and notebooks for experimentation.

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Repo Structure

Here’s a quick rundown of the folders & files:

Flight-Price-Prediction/
├── README.md
├── .gitignore
├── requirements.txt
├── requirements-dev.txt
├── pytest.ini
├── main.py
├── main-base.py
├── api.py
├── pipeline.py
├── mlflow_config.py
├── logging_config.py
├── data/
│   ├── raw_data.csv
│   └── processed_data.csv
├── dist/
│   ├── favicon.ico
│   ├── index.html
│   ├── placeholder.svg
│   ├── robots.txt
│   └── assets/
├── logs/
│   └── project.log
├── notebooks/
│   ├── flight_price.ipynb
├── src/
│   ├── __init__.py
│   ├── data.py
│   ├── model.py
│   ├── predict.py
│   └── utils.py
└── tests/
    ├── test_data.py
    ├── test_geoapi.py
    └── test_models.py

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Getting Started

Prerequisites

• Python 3.8+
• pip for installing dependencies
• (Optional) virtual environment tool like venv or conda
• (Optional) MLflow if you're using tracking

Setup and Installation

# Clone the repo
git clone https://github.com/10mudassir007/Flight-Price-Prediction.git
cd Flight-Price-Prediction

# Create and activate virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install requirements
pip install -r requirements.txt
pip install -r requirements-dev.txt  # optional, for testing and dev tools

Run the tests

pytest tests/

Run the pipeline

python pipeline.py

Running the API

uvicorn main:app