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Python PROJECT

Air Traffic Passengers Analysis

Exploratory Data Analysis of Passenger Traffic, Airlines & Travel Patterns

Python Pandas Plotly Data Cleaning Exploratory Data Analysis Data Visualization

Python Dashboard

Passenger traffic changed over time
Passenger traffic changed over time
Geographic distribution of passengers
Geographic distribution of passengers
Passenger Count by Terminal and Boarding
Passenger Count by Terminal and Boarding
Highest passenger traffic
Highest passenger traffic Analysis

Project Overview

This project uses Python to explore and analyze air traffic passenger data, with a focus on passenger trends, airline performance, geographic distribution, terminal activity, boarding areas, activity types, price categories, and seasonal patterns.

The analysis applies data cleaning, transformation, aggregation, and interactive visualization techniques to uncover meaningful patterns in passenger traffic and compare domestic and international travel activity.

Dataset

Air Traffic Data

The dataset was obtained from Kaggle and contains air traffic information covering airlines, geographic regions, activity types, terminals, boarding areas, price categories, and passenger counts over time.


Dataset : https://www.kaggle.com/datasets/rohanshetty678/air-traffic-data/data

Data Preparation

  • Dataset loading
  • Data inspection
  • Column and data-type checks
  • Duplicate checking
  • Missing-value checking
  • Missing-value replacement
  • Date transformation

Key fields include

  • Activity Period
  • Operating Airline , Published Airline
  • GEO Summary, GEO Region
  • Activity Type, Price Category,
  • Terminal, Boarding Area
  • Passenger Count, Adjusted Passenger Count
  • Year, and Month

Tools & Technologies

Python Pandas Plotly Jupyter Notebook Data Cleaning Exploratory Data Analysis Data Visualization

Project Brief

01

Loaded the air traffic dataset using Pandas and inspected the first records, columns, and data types.

02

Checked the dataset for duplicate records and missing values to improve data quality.

03

Converted year and month information into datetime fields to support time-series analysis.

Data Cleaning

Air Traffic Data Cleaning

Interactive Python Project

Explore the interactive Python Project.

Open Python Code →