Welcome to the Air Data Analysis Project, a comprehensive data analysis journey using air.csv! This project dives deep into aviation-related data to uncover meaningful insights, visualize trends, and provide actionable recommendations.
- Data Cleaning & Preprocessing: Handle missing values, duplicate records, and inconsistent data.
- Exploratory Data Analysis (EDA): Discover patterns, trends, and anomalies through interactive visualizations.
- Data Visualization: Generate insightful graphs and charts using tools like Matplotlib, Seaborn, or Pandas.
- Dataset Name:
air.csv - Key Columns:
- Flight Numbers
- Departure/Arrival Times
- Flight Delays
- Airlines and Destinations
- Passenger Count and More
The dataset provides an opportunity to analyze real-world aviation data for meaningful conclusions.
- Languages: Python 🐍
- Libraries:
- Pandas: Data manipulation
- NumPy: Numerical computations
- Matplotlib & Seaborn: Visualizations
- Tools:
- Jupyter Notebook
Here's a sneak peek of the visualizations included:
- Flight Delay Trends: Line plots showcasing delays over time.
- Airline Performance: Bar charts comparing punctuality across airlines.
- Passenger Flow Analysis: Heatmaps illustrating peak travel hours.
- Identify the busiest airlines and routes.
- Highlight common causes of flight delays.
- Clone this repository:
git clone https://github.com/jvpurushotham/air-data-analysis.git