✈️ Flight Time Prediction Using Machine Learning

📖 Project Overview

This project focuses on predicting flight times to classify them as Early, Late, or On Time based on a comprehensive set of influencing factors. Utilizing various machine learning algorithms, our model achieved an accuracy of 80%, the highest in our class!

🚀 Key Features

• Data Sources: Utilizes a rich dataset comprising:

  • Weather data
  • Airline data
  • Historical flight data
  • Current weather data
  • Airport congestion data
  • Flight capacity and wind flow
  • Average airport security congestion

• Machine Learning Models: Implemented multiple combinations of machine learning models to enhance prediction capabilities.

• Classification Accuracy: Achieved 80% accuracy in predicting flight statuses.

📊 Data Overview

The dataset includes various features that impact flight delays, such as:

• Weather conditions (temperature, humidity, etc.)
• Airline-specific data (flight schedules, operational history)
• Historical delays
• Real-time airport conditions (congestion, security checks)

⚙️ Installation

To run this project locally, ensure you have Python installed. Then clone the repository and install the required packages:

git clone https://github.com/hemilshah99316/FLIGHT_PREDICTION_ANALYSIS_USING_ML.git

OR

Download ZIP File

🎯 Future Work

• Feature Enhancements: Incorporate more features like advanced weather forecasting and additional flight metrics.
• Model Optimization: Experiment with other machine learning techniques to further improve accuracy.
• Deployment: Create a web application for real-time flight status predictions.