Flight_data_analysis_project2

✈️ Airline Flight Data – Exploratory Data Analysis (EDA)

📌 Project Overview

This project explores an airline flight dataset to uncover insights into flight durations, pricing patterns, route popularity, and airline performance.
It answers 10 business questions using Python, Pandas, Matplotlib/Seaborn, and SQL queries.

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📂 Dataset

• Source: Provided dataset airlines_flights_data.csv
• Rows:
• Columns:
  • airline – Name of the airline
  • source_city – Departure city
  • destination_city – Arrival city
  • duration – Flight time
  • stops – Number of stops ( Non-stop, 1 stop,2 or more)
  • price – Ticket price
  • class – Travel class (Economy/Business)
  • days_left – Days left before departure when ticket was booked

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🔍 Business Questions

1. Which airline has the highest average flight duration?
2. Which source–destination city pairs have the longest average duration?
3. How many flights are recorded per airline and class (Business vs Economy)?
4. Which departure time of day has the longest average durations?
5. Which airlines operate the least number of unique routes?
6. Do non-stop flights cost more than connecting ones for the same route?
7. What are the top 5 most frequent flight routes?
8. Which source cities have the most departures?
9. Do last-minute bookings cost more than early bookings?
10. Do certain airlines dominate specific routes or regions?

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🛠️ Tech Stack & Tools

• Python – Data analysis & visualization
• Pandas – Data manipulation
• Matplotlib / Seaborn – Visualizations
• SQLite – SQL queries for analysis
• Jupyter Notebook – Analysis environment

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📊 Analysis Types

• Univariate Analysis – Single variable distributions (e.g., price, duration)
• Bivariate Analysis – Relationships between two variables (e.g., price vs stops)
• Multivariate Analysis – Multi-variable relationships (e.g., price by airline, class, stops)

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