The aviation industry significantly influences the social and economic advancement of a nation. However, airline delays cause significant losses for the industry, impacting airports, airlines, and passengers.
Punctual flight performance is crucial for happier customers, increased profitability, and improved efficiency and safety. This project utilizes data visualization and analysis to explore distribution features and understand their effect on the occurrence of delays.
The dataset contains information on 539,383 rows and 9 columns, detailing whether flights operated by different airlines were delayed. The data was sourced from Kaggle.
- Target Variable: Delay (0 or 1)
- Features: Airline, Flight Number, Airport From, Airport To, DayOfWeek, Time, Length
| id | Airline | Flight | Airport From | Airport To | DayOfWeek | Length | Delay |
|---|---|---|---|---|---|---|---|
| 1 | CO | 269 | SFO | IAH | 3 | 205 | 1 |
| 2 | US | 1558 | PHX | CLT | 3 | 222 | 1 |
| 3 | AA | 2400 | LAX | DFW | 3 | 165 | 1 |
| 4 | AA | 2466 | SFO | DFW | 3 | 195 | 1 |
| 5 | AS | 108 | ANC | SEA | 3 | 202 | 0 |
Worldwide airline delays are a major issue causing enormous losses. Cutting down on flight delays can lessen aviation's carbon footprint and benefit the environment. This study aims to analyze factors contributing to flight delays to help create appropriate plans for smooth operational functioning.
We analyzed the distribution of features using univariate and multivariate plots. The analysis revealed that approximately 45% of flights in the dataset are delayed.
| Category | Finding | Metric/Note |
|---|---|---|
| Most Delayed Airline | WN Airlines | Highest frequency of delays |
| Least Delayed Airline | HA Airlines | Best performance |
| Worst Days | Midweek (Days 3 & 4) | 17% delay proportion each |
| Best Day | Day 6 | Only 11% delay observed |
| Most Popular Route | LAX - SFO | 2,156 combined flights |
As shown above, WN Airlines operated the most delayed flights, and delays were most frequent during midweek operations.