Saimoukthika25

Saimoukthika25 / Flight-delay-and-cancellation-analysis

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Data Science project analyzing flight delays and cancellations using EDA and Machine Learning on real-world aviation data.

10
0
100% credibility
Found Apr 15, 2026 at 9 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

This GitHub repository documents a data science project analyzing flight delays and cancellations through exploratory data analysis, statistical tests, and basic machine learning models on U.S. aviation data.

How It Works

1
🔍 Discover the project

You search online for why flights are often late and find this helpful analysis shared by a student.

2
📖 Read the overview

You learn it's a study of over a million real flights, looking at delays and cancellations.

3
💡 Uncover key insights

You see eye-opening facts like which airlines delay most, busiest delay months, and how delays spread.

4
📊 Explore the charts

You enjoy looking at pictures like pie charts of on-time flights and heatmaps of connections.

5
🤖 See smart predictions

You discover how simple guesses predict arrival delays super accurately from takeoff waits.

6
💭 Reflect on findings

You think about weather, crowds, and airlines next time you're at the airport.

🎉 Feel informed

Now you understand flight delays better and can share cool facts with friends.

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AI-Generated Review

What is Flight-delay-and-cancellation-analysis?

This Jupyter Notebook project analyzes over 1 million real-world flights from the Bureau of Transportation Statistics, using EDA and machine learning to uncover delay patterns, airline performance, and cancellation causes. It generates visualizations like heatmaps and boxplots, plus predictive models such as linear regression (0.916 R² on arrival delays) and logistic regression for binary delay classification. Python users get actionable insights on factors like weather and departure delays without needing to source data themselves.

Why is it gaining traction?

It stands out as a flight delay analysis github repo with concrete stats—79% on-time flights, Frontier's 23-minute average delays—making data science bachelor projects or data science studium demos instantly relatable. The hook is its focus on aviation pain points like delay propagation (0.94 correlation), with SciPy tests and Scikit-learn models that benchmark against real BTS data. Developers grab it for quick wins in data science jobs portfolios over generic tutorials.

Who should use this?

Data science master students prototyping ML on time-series data, aviation analysts validating seasonal trends, or data science weiterbildung learners needing a flight delay dataset example. Ideal for data science und künstliche intelligenz explorers building dashboards from EDA insights.

Verdict

Skip for production—1.0% credibility score, 10 stars, and no full dataset or notebooks limit usability—but it's a solid starter for data science institute homework or github data table experiments. Fork and expand with XGBoost for real value.

(178 words)

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