Air Flight Analysis

OVERVIEW

The Flight Analysis Project addressed critical challenges faced by airlines, including flight delays and cancellations, and the need to optimize air traffic management. By analyzing a comprehensive dataset covering multiple airlines, airports, and flight statuses, the project aimed to provide a clear picture of operational bottlenecks and passenger experience pain points. The insights generated are crucial for data-driven decision-making in airline scheduling, resource allocation, and customer communication. 

OBJECTIVES

The primary objectives of this project were to:

• Flight Status Analysis:

  • Evaluate monthly flight status to analyze on-time, delayed, and cancelled flights.

  • Investigate average delay times for each airline and month, focusing on identifying patterns of delays.

• Air Traffic Analysis:

  • Identify optimal times to book flights by analyzing air traffic data.

  • Determine peak hours for flight availability and understand flight patterns throughout the week.

• Actionable Insights:

  • Identify key days or times when delays are more to help airlines improve scheduling and resource allocation.

  • Provide actionable insights to passengers regarding the best chances for on-time flights.

KEY COLUMNS

The analysis utilized an airline dataset that included data from multiple airlines and airports. Key data points analyzed would have included:

• Flight Status: (e.g., On-time, Delayed, Cancelled)

• Airline: (e.g., Delta Air Lines Inc.)

• Airport: (Specific airport identifiers)

• Date/Time: (e.g., Monthly, Day of the week, Departure Hour, Arrival Hour, Year)

• Delay Times: (Average delay time in minutes)

• Number of Flights: (Air traffic volume)

TOOLS

The primary tools utilized for this data analysis and visualization project were:

• Data Source: Airline dataset in Excel.

• Data Wrangling: Microsoft Excel (for initial data cleaning and formatting, specifically for the 'Departure Hour' column).

• Data Analysis & Visualization: Power BI.

APPROACH

The project followed a structured analytical and visualization approach:

Phase 1: Data Acquisition & Initial Wrangling

• Loaded the airline dataset into Power BI.

• Challenge & Solution: Encountered a data formatting issue in the 'Departure Hour' column (e.g., "0:6:00" instead of "00:06"). This was resolved by fixing the column in Excel, replacing incorrect time values, and splitting the 'Departure Hour' column into "Departure Time" and "Arrival Hour" before re-uploading the corrected dataset into Power BI.

Phase 2: Flight Status Analysis

• Evaluated monthly flight status trends for on-time, delayed, and cancelled flights.

• Analyzed average delay times, breaking them down by airline and month to identify specific patterns.

Phase 3: Air Traffic Pattern Analysis

• Investigated air traffic data to identify peak flight periods and overall flight patterns throughout the week.

• Determined the number of flights over time to visualize trends in air traffic and potential congestion points.

Phase 4: Dashboard Development & Visualization

• Created interactive dashboards in Power BI.

• Implemented slicers for 'Month' and 'Airline' to allow dynamic filtering of data.

• Designed charts to showcase flight statuses by the day of the week, display on-time, delayed, and cancelled flight counts, and visualize average delay times broken down by airline and month.

• Visualized air traffic trends to highlight peak flight periods and delay trends.

Phase 5: Insight Extraction & Recommendation Development

• Derived actionable insights from the visualizations, identifying key days or times prone to delays.

• Formulated specific recommendations for various airline management roles (Operations, Flight Operations, Customer Service, Airline Scheduling, Airline Coordination) to improve performance and customer experience.

KEY INSIGHTS

The analysis yielded several critical insights for airlines:

• On-Time Performance by Day: Tuesday consistently had the highest on-time flight rate, showing an 18.73% significant difference compared to Saturday, which had the lowest on-time rate. Tuesday accounted for 15.07% of all on-time flights.

• Flight Status Variation: Flight status varied significantly across days, with on-time flights ranging from 37,982 to 45,095, cancellations from 1,448 to 2,112, and delays from 17,949 to 28,178.

• Average Delay Time Trends: The year 2009 recorded the highest total average delay (600,648 minutes), followed by 2010 (583,667 minutes), with a significant drop in 2011 (45,083 minutes). Delta Air Lines Inc. in 2010 contributed 7.64% of the total average delay time.

• Air Traffic Surges: A notable 261.45% increase in flights occurred on Saturday, December 30, 1899, reaching 45,693 flights. Conversely, a sharp 80.64% decline in flights was observed over 5 hours on the same Saturday, dropping from 36,040 to 8,653 flights.

IMPACTS

This project delivered significant impacts for airlines:

• Improved Operational Efficiency: Data-driven insights enable airlines to optimize flight schedules and resource allocation, leading to smoother operations.

• Reduced Delays & Cancellations: By identifying high-risk periods and factors, airlines can proactively address issues, minimizing disruptions.

• Enhanced Customer Satisfaction: Providing insights into on-time performance and potential delays allows airlines to set realistic expectations and improve customer communication through real-time alerts.

• Strategic Planning: The analysis supports strategic decision-making for flight operations, gate management, ground crew allocation, and seasonal staffing.

• Proactive Issue Resolution: Understanding flight status trends allows airlines to anticipate and address issues before they escalate, ensuring a better passenger experience.

DELIVERABLES

The key deliverables for this project included:

• Interactive Dashboards (Power BI): Dashboards with slicers for Month and Airline, dynamic charts showcasing flight statuses by day of the week, on-time, delayed, and cancelled flight counts, and average delay times broken down by airline and month.

• Flight Status Visualizations: Visual representations of on-time, delayed, and cancelled flights, along with average delay times, segmented by airline and month.

• Air Traffic Trends Visualizations: Charts illustrating the number of flights over time to identify peak periods and delay trends.

• Comprehensive Insights Report: A detailed report outlining the project's findings, challenges encountered and their solutions, and actionable recommendations for various airline management roles.