This repository contains Python code for analyzing flight data. The code utilizes various libraries such as NumPy, Pandas, SciPy, Matplotlib, and Seaborn to perform data preprocessing, exploration, and visualization tasks.
Ensure that you have the necessary libraries installed. You can install them using pip or conda.
pip install numpy pandas scipy matplotlib seaborn
Download the flight data in CSV format and place it in the same directory as the Python script.
Open the Python script and modify the file path in the flight_data=pd.read_csv("/flight_data.csv") line to match the name and location of your flight data file.
Run the script. It will execute the analysis steps and generate various visualizations and insights about the flight data.
The code is organized into sections, each focusing on a specific aspect of the analysis. Here's an overview of the main sections:
Data Loading and Preprocessing: This section loads the flight data from the CSV file and performs necessary preprocessing steps such as converting date columns and creating additional columns for analysis.
Data Summary: This section generates descriptive statistics of the flight data, providing insights into the data distribution and central tendencies.
Visualizations: This section creates various visualizations to understand different aspects of the flight data. It includes pie charts, bar plots, box plots, scatter plots, line graphs, and heatmaps.
Data Exploration: This section explores patterns in departure delays, the number of scheduled departures by carriers, scheduled arrivals at different destinations, and the behavior of different carriers.
Performance Analysis: This section analyzes carrier performance by calculating average monthly departure delays, average monthly departure delays by carrier, average arrival delays by carrier, and monthly trends in arrival delays.
Additional Analyses: This section includes additional analyses such as identifying top destinations with early arrivals, determining the best airport for early departures, examining delay distributions through histograms, analyzing the distribution of delays for on-time/early flights, identifying the fastest flights, and visualizing flight speed patterns.
Correlation Analysis: This section calculates the correlation between different variables in the flight data and visualizes the correlation matrix.
The analysis provides insights into flight patterns, delays, carrier performance, and other aspects of the analyzed data. Visualizations help visualize the data distribution and identify patterns or anomalies. The code generates various statistics and charts to aid in understanding and interpreting the flight data.
Please note that this analysis is based on the provided flight data and its interpretation may vary depending on the specific dataset used.
For any questions or issues, feel free to contact me at ayusht18dec@outlook.com.