Flight Data Analytics Project

Overview

This project analyzes flight data to gain insights into airline pricing, departure and arrival trends, source and destination city statistics, and other key patterns. The project is implemented using Python and popular data analysis libraries like Pandas, Matplotlib, and Seaborn.

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Features

• Airline Analysis: Count and visualize flights per airline.
• Departure & Arrival Analysis: Analyze flights by departure and arrival times.
• Source & Destination Cities: Visualize the number of flights from each city.
• Price Analysis: Explore flight prices over days left until departure.
• Interactive Visualizations: Bar charts, horizontal bar charts, and line plots for clear insights.

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Technologies & Libraries

• Python 3.x
• Pandas
• Matplotlib
• Seaborn
• Jupyter Notebook (for development & visualization)

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Installation

1. Clone the repository:

   git clone https://github.com/yourusername/Flight-Data-Analytics.git
   

2. Navigate to the project folde

   cd Flight-Data-Analytics

3. Install required libraries

   pip install pandas matplotlib seaborn

4. Open the Jupyter Notebook

   jupyter notebook
   

How to Use

Load the flight dataset (data.csv) into the project folder. Run the Jupyter Notebook cells step by step to see the analysis and visualizations. Modify plots or analysis as needed for further insights

Images(Visualizations - data analysing)

Airlines Price Analysis

Departure Time Distribution

Arrival Time Distribution

Source vs Destination Cities

Time Series Analysis

Price vs Days Left