✈️ Flight Delay Intelligence System

📌 Project Overview

This project analyzes U.S. flight delays and cancellations using PySpark and explores patterns across airlines, airports, routes, months, and departure times.

The project combines Big Data Analytics, Data Visualization, and an Interactive Dashboard to uncover operational insights and identify major factors contributing to flight delays.

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🎯 Business Objective

The objective of this project is to:

• Identify major causes of flight delays and cancellations
• Analyze delay trends across different time periods
• Discover the most delayed routes and airports
• Visualize flight performance through interactive dashboards
• Generate actionable insights for airline operations

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🛠️ Tools & Technologies

Category	Technologies
Programming	Python
Big Data	PySpark
Data Analysis	Pandas
Visualization	Matplotlib, Seaborn, Plotly
Dashboard	Dash
Environment	Jupyter Notebook

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📂 Dataset Information

The analysis uses the following datasets:

Dataset	Description
flights.csv	Flight-level operational data
airlines.csv	Airline information
airports.csv	Airport information

⚠️ Note: The original flights.csv dataset is not included in this repository due to GitHub file size limitations.

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🔄 Project Workflow

1️⃣ Data Collection

• Imported flight, airline, and airport datasets

2️⃣ Data Cleaning

• Removed missing values
• Processed delay and cancellation records

3️⃣ Exploratory Data Analysis

• Delay trends
• Cancellation analysis
• Route performance analysis

4️⃣ Visualization

• Bar charts
• Pie charts
• Trend analysis
• Airport geo-mapping

5️⃣ Dashboard Development

• Interactive Dash dashboard using Plotly

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📊 Analysis Performed

✈️ Flight Operations Analysis

• Flight cancellation analysis
• Cancellation reason distribution
• Airport delay analysis

📈 Delay Trend Analysis

• Average arrival delay by month
• Average arrival delay by departure hour
• Delay cause breakdown

🛣️ Route Analysis

• Most delayed flight routes
• Route performance comparison

🌎 Geographic Analysis

• Airport delay geo-mapping

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📊 Dashboard Features

The interactive dashboard provides:

✅ Monthly Delay Trends

✅ Cancellation Reason Analysis

✅ Most Delayed Routes

✅ Airport Delay Insights

✅ Interactive Visual Exploration

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📸 Project Visualizations

✈️ Flight Cancellation Count

❌ Cancellation Reasons Distribution

📅 Average Arrival Delay by Month

🕒 Average Arrival Delay by Departure Hour

🛣️ Top 5 Most Delayed Routes

🚨 Total Delay Minutes by Reason

🌎 Airport Delay Geo Mapping

🔍 Key Findings

🚨 Major Delay Cause

Late aircraft delays were the largest contributor to overall delays.

🌅 Best Time to Fly

Early morning departures generally experienced fewer delays.

🛣️ High-Risk Routes

Certain routes consistently showed significantly higher delays.

🌎 Delay Hotspots

Airport geo-mapping helped identify major delay-prone regions.

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📁 Repository Structure

Flight-Delay-Intelligence-System
│
├── big_data_analysis/
├── images/
│   ├── cancellation_count.png
│   ├── cancellation_reasons.png
│   ├── delay_by_month.png
│   ├── delay_by_hour.png
│   ├── top_delayed_routes.png
│   ├── delay_causes.png
│   └── airport_delay_map.png
│
├── app.py
├── main.py
├── report.ipynb
└── README.md

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🚀 Future Improvements

• Build a real-time flight delay prediction model
• Integrate weather data
• Add airline performance scorecards
• Deploy dashboard online

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👩‍💻 Author

Unnati Patil

Aspiring Data Analyst | Business Analyst | Data Science Enthusiast

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⭐ Project Highlights

✔ Big Data Analytics using PySpark

✔ Interactive Dashboard Development

✔ Data Visualization & Storytelling

✔ Business Insight Generation

✔ End-to-End Analytics Project

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