D3 project for US flight delay visualization

Visualize departure and arrival delays for the USA by state

This project is a comprehensive Power BI dashboard analyzing airport operations, focusing on flight delays, time analysis, and detailed flight information to improve efficiency and passenger satisfaction.

Comparative analysis of flight delay predictions using naïve Bayes, trees, and logistic regression

ETL Pipeline to analyze Flight Departures (domestic) in the U.S. in 2022. Questions about flight delays, whether influence, airlines reliability.

This is a brief Insights on causes of flight delay based on Viz and implementation of Airline carrier ranking system based on data of flights to and from Texas ( Bureau of Transportation ) during Jan 2017. Using PandaSQL ( SQL+ Python)

“Using historical data, identify which model best predicts future flight delays and in turn identify top indicators of U.S. domestic flight delays.”

A powerful Streamlit-based web application for tracking real-time flight delays and aviation disruptions. It features predictive analytics, interactive data dashboards, and secure role-based portals for both travelers and administrators to monitor over 320+ US airports and 15+ airlines

Use Amazon SageMaker to forecast flight delays (regression model)

Predicts flight delays in Brazil using ANAC data and machine learning techniques, including exploratory data analysis and model evaluation.

This is an assignment to work on publicly available dataset called US commercial passenger carrier flights. This assignment is about predicting delays.

This project uses the RITA dataset by the U.S. Bureau of Transportation Statistics to analyse the different causes of airline delays across +1.9M of different flights.

Data-driven analysis of U.S. flight delays using Python and Random Forest

Analyzing United flight data that contains airline, airport, and weather information from 2019 to build a flight delay predictor, using various supervised learning methods.

End-to-end flight delay analytics pipeline analyzing 55M+ US domestic flights from 2016-2026 using Python, Snowflake, dbt Cloud, and Looker Studio

This project shows the amount of delays, on time and early flights Skywest airlines had in the year of 2009. Used Pandas, Tableau, SQLalchemy, machine learning model, and Postgres.

End-to-end demo project using Microsoft Fabric to predict flight delays with AI and ML. Includes data ingestion, feature engineering, model training, explainability, and Power BI insights — all built on Lakehouse architecture with Copilot support.

Interactive Power BI dashboard analyzing 5.8M US flight delays — airline performance, route analysis and cancellation patterns.

Exploratory data analysis and machine learning to forecast US flight delays, based on 3 million flights between 2019-2023.