Airlines Delay Prediction Using Machine Learning and Deep Learning

Keywords: Airline Delay Prediction, Flight Delay Prediction, Machine Learning, Deep Learning, Artificial Intelligence, Python, Decision Tree, Naive Bayes, Random Forest, Logistic Regression, KNN, SGD, Neural Ne

Project Synopsis & Overview

This project aims to develop a predictive system for forecasting airline flight delays using machine learning and deep learning techniques. The system analyzes historical flight information and relevant factors such as departure and arrival times, flight distance, airline, airport traffic, weather conditions, mechanical issues, and crew-related factors. Data preprocessing, feature engineering, visualization, and exploratory data analysis are performed before model training. Machine learning models such as Decision Tree, Naive Bayes, Random Forest, Logistic Regression, KNN, and SGD, along with deep learning models such as Neural Networks, LSTM, RNN, and CNN, can be used for prediction. The models are evaluated using accuracy, precision, recall, F1-score, and confusion matrix, and their performances are compared.

Complete Technical Specifications

Project ID	1CP0631 (DB ID: 631)
Project Title	Airlines Delay Prediction Using Machine Learning and Deep Learning
Domain Division	Python
Sub-Domain / Tech	AI
IEEE Year	2026
Package Price	₹6500
Created Date	Sep 17, 2026