A comprehensive aviation analytics platform analysing 6.2M+ flight records across 14 US airlines and 628 airports — uncovering delay patterns, seasonal trends, airline performance, and root-cause intelligence to drive operational improvements.
A dynamic, data-driven flight operations dashboard built to explore delay patterns across US domestic aviation — covering monthly seasonality, day-of-week peaks, airline on-time performance, airport delay rankings, and root-cause analysis across Weather, Carrier, NAS, Security, and Late Aircraft delay categories.
The Airline Flight Delay Analysis Dashboard is an analytically rich Python-based intelligence platform designed to help aviation operations teams, analysts, and passengers understand the who, when, why, and how much of flight delays across the US domestic network.
This tool is intended for aviation analysts, airline operations managers, airport authorities, travel researchers, and data analysts who seek to understand delay patterns and identify operational improvement opportunities.
- 🐍 Python 3.10+ — Core data processing and analysis language
- 📊 Matplotlib & Seaborn — Professional aviation-themed dark dashboard suite
- 🧮 Pandas & NumPy — Data wrangling, aggregation, and statistical analysis on 6M+ records
- 🗃️ SQL (conceptual) — Data cleaning & transformation logic
- 📁 CSV — Structured flight dataset (BTS-style format)
- 🖥️ Jupyter Notebook — Exploratory development environment
Dataset: Synthetically engineered flight dataset modeled on Bureau of Transportation Statistics (BTS) patterns
The dataset captures 6.2M flight records across 2022–2023 with:
- 14 US Airlines with realistic on-time performance profiles
- 628 Airports across the US domestic network
- 5 Delay Causes: Weather, Carrier, NAS, Security, Late Aircraft
- Full feature set: Carrier, Origin, Dest, Distance, Delay minutes, Cancellations
💡 For real flight delay data, visit: BTS On-Time Performance | Kaggle Flight Delay Dataset
Flight delays cost the US aviation industry $33 billion annually — $16 billion borne directly by passengers. Airlines, airports, and operations teams struggle to answer:
- Which months and days consistently produce the worst delays?
- Are delays driven by weather, carrier failures, or system-wide congestion?
- Which airlines have the best vs worst on-time reliability?
- Do busier airports necessarily delay more flights?
- How do delay patterns shift year-over-year?
To deliver an interactive flight operations intelligence platform that:
- Enables exploration of 6.2M+ flight records through visual aggregations
- Identifies peak delay months (Dec/Jan) and peak days (Friday)
- Surfaces airline delay risk matrices (frequency vs severity)
- Benchmarks 628 airports on delay performance
- Supports operations teams with a complete KPI scorecard
📊 Executive KPIs (Top Row)
- Total Flights Analysed: 6.2M+
- Overall On-Time Rate: 83.3%
- Avg Delay (when delayed): 40 minutes
- Cancellation Rate: ~1.2%
🏆 On-Time Performance by Airline (Bar Chart) Ranks all 14 airlines by OTP %. Hawaiian Airlines leads at 88%+ while Spirit and Frontier lag — driven by tighter scheduling buffers and hub congestion.
📅 Monthly Delay Pattern (Dual-Axis Line+Bar) Clear seasonal peaks in December–January (1.40× avg delay) driven by weather, and a secondary peak in June–July driven by thunderstorm season. September–October is the best travel window.
🌦️ Delay Cause Analysis (Horizontal Bar) Carrier delays are the most frequent (31%) while Late Aircraft causes the longest average delays (35 min). Weather accounts for 22% but spikes dramatically in winter months.
📊 Delay Duration Distribution (Histogram) Confirms that majority of delays are short-duration (under 30 minutes), with a long right tail of extreme cases. The distribution validates the industry insight that most disruption is manageable.
⏱️ Day-of-Week Delay Pattern (Bar Chart) Friday shows the highest delays (1.15× average) due to peak leisure travel demand. Wednesday is the most reliable day to fly.
📍 Airline Risk Matrix (Scatter) Plots all 14 airlines by Delay Frequency vs Severity — airlines in the top-right quadrant (high frequency + high severity) are flagged as highest operational risk.
🏢 Airport Delay Rankings (Horizontal Bar) Top 30 busiest airports ranked by average departure delay. Reveals that volume alone doesn't drive delays — operational efficiency and hub complexity matter more.
📋 KPI Scorecard Table Complete operations summary covering all key metrics, benchmarks, and contextual notes — formatted for executive reporting.
| Insight | Business Action |
|---|---|
| December has 1.40× avg delays | Airlines should pad schedules & add buffer capacity in Nov-Dec |
| Carrier delays are #1 cause (31%) | Operational review of turnaround procedures & crew scheduling |
| Friday peak delays | Suggest travellers shift to Wednesday/Thursday when possible |
| Hawaiian Airlines leads OTP at 88%+ | Study their scheduling & ops model as industry benchmark |
| Short delays dominate (>60% under 30 min) | Focus on eliminating first delays of the day (cascading effect) |
| Airport volume ≠ more delays | Efficiency metrics more predictive than size |
| Late Aircraft causes longest avg delay (35 min) | First flight of the day buffer is critical to prevent cascade |
| September-October: lowest delays | Optimal travel window for fare & reliability |
git clone https://github.com/Munishx01/airline-flight-delay-analysis.git
cd airline-flight-delay-analysis
pip install -r requirements.txt
python src/generate_data.py # Generate 6.2M flight records
python src/dashboard_viz.py # Create all 5 dashboardsairline-flight-delay-analysis/
├── 📁 data/
│ └── flight_delays.csv # 6.2M flight records, 17 features
├── 📁 src/
│ ├── generate_data.py # Realistic BTS-style data generator
│ └── dashboard_viz.py # 5 professional dashboard panels
├── 📁 outputs/figures/
│ ├── 01_executive_flight_dashboard.png
│ ├── 02_temporal_pattern_analysis.png
│ ├── 03_airline_performance.png
│ ├── 04_airport_analysis.png
│ └── 05_kpi_scorecard.png
├── requirements.txt
└── README.md
Munish Kumar — Data Analyst | Python | SQL | Power BI
📧 mk611453@gmail.com | 📍 Palampur, Himachal Pradesh
"Every delayed flight tells a story — data helps us rewrite the ending."