📌 Project Overview

IndiGo Airlines is expanding into the U.S. domestic market. This project analyzes 5.8M+ flight records from 2015 to identify key factors affecting On-Time Arrival Rate (OTAR) and recommends operational strategies to improve punctuality from 62.5% to 80%.

❓ Problem Statement

IndiGo Airlines is entering the U.S. domestic market where on-time performance is critical for building brand trust. The industry standard OTAR is ~80%, but analysis of 2015 U.S. flight data shows the current benchmark sits at 62.5% under strict definition (flights arriving on or before scheduled time). The goal is to identify root causes of delays and recommend data-backed strategies to close this 17.5% gap.

🛠️ Tools & Technologies

Python | Pandas | Matplotlib | Seaborn | Google Colab | Tableau

🗂️ Dataset Overview

• Source: U.S. Bureau of Transportation Statistics (BTS)
• Time Period: January 2015 — December 2015
• Total Records: 5,819,079 flight records
• Total Columns: 31 columns

Files Used:

• flights.csv — Core flight operations data (delays, cancellations, arrival/departure times)
• airports.csv — 322 airports mapped with IATA codes, cities and coordinates
• airlines.csv — 14 U.S. carriers mapped to full names

Key Variables Analyzed:

• Departure & Arrival Delays
• Taxi-Out & Taxi-In Times