https://github.com/djdurga/data-visualization-of-bird-strikes-between-2000-2011

This analysis provides a comprehensive view of bird strike patterns, impacts, and areas requiring attention. It can help stakeholders, including airlines, airports, and regulatory bodies, to make informed decisions and implement effective measures to reduce bird strike incidents and their consequences.
https://github.com/djdurga/data-visualization-of-bird-strikes-between-2000-2011

matplotlib pandas

Last synced: 2 months ago
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This analysis provides a comprehensive view of bird strike patterns, impacts, and areas requiring attention. It can help stakeholders, including airlines, airports, and regulatory bodies, to make informed decisions and implement effective measures to reduce bird strike incidents and their consequences.

• Host: GitHub
• URL: https://github.com/djdurga/data-visualization-of-bird-strikes-between-2000-2011
• Owner: Djdurga
• Created: 2024-07-29T07:13:40.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-29T09:02:22.000Z (almost 2 years ago)
• Last Synced: 2025-02-01T21:32:54.365Z (over 1 year ago)
• Topics: matplotlib, pandas
• Language: Jupyter Notebook
• Homepage:
• Size: 3.04 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Data Visualization of Bird Strikes (2000 – 2011)

## Project Overview

**Title:** Data Visualization of Bird Strikes between 2000 – 2011  

**Domain:** Transportation and Communication  

**Technologies:** Data Science (Python, Tableau, Power BI, etc.)  

**Difficulty Level:** Advanced  

## Problem Statement

The project aims to analyze and visualize data on bird strikes collected by the FAA (Federal Aviation Administration) from 2000 to 2011. Bird strikes pose significant threats to aircraft safety, and understanding the patterns and impacts of these incidents is crucial for improving safety measures.

## Key Questions and Analysis Objectives

1. **Visuals Depicting the Number of Bird Strikes:**

   - Create visualizations showing the total number of bird strikes over the years.

2. **Yearly Analysis & Bird Strikes in the US:**

   - Analyze the data to show the yearly trends of bird strikes.

3. **Top 10 US Airlines in Terms of Bird Strikes:**

   - Identify which airlines have encountered the most bird strikes.

4. **Airports with Most Incidents of Bird Strikes – Top 50:**

   - Determine which airports have reported the highest number of bird strikes.

5. **Yearly Cost Incurred Due to Bird Strikes:**

   - Analyze the financial impact of bird strikes on a yearly basis.

6. **When Do Most Bird Strikes Occur?**

   - Identify the time of day, month, or season when bird strikes are most frequent.

7. **Altitude of Aeroplanes at the Time of Strike:**

   - Analyze the altitudes at which bird strikes typically occur.

8. **Phase of Flight at the Time of the Strike:**

   - Determine the phase of flight (take-off, landing, etc.) during which bird strikes are most common.

9. **Average Altitude of Aeroplanes in Different Phases at the Time of Strike:**

   - Analyze the average altitude during different phases of flight when strikes occur.

10. **Effect of Bird Strikes & Impact on Flight:**

    - Examine the consequences of bird strikes on flight operations and safety.

11. **Effect of Strike at Different Altitudes:**

    - Analyze how the impact of bird strikes varies at different altitudes.

12. **Were Pilots Informed? & Prior Warning and Effect of Strike Relation:**

    - Investigate if prior warnings about bird activity were given and their effect on the outcome of bird strikes.

## Dataset

The dataset provided includes detailed records of bird strikes, including:

- Incident Year

- Operator (Airline)

- Airport Name

- Cost ($)

- Incident Date

- Altitude Bin

- Phase of Flight

- Effect on Flight

- Pilot Warned

## Approaches and Tools

- **Python:** For data cleaning, analysis, and creating visualizations.

- **Tableau/Power BI:** For creating interactive dashboards and visualizations.

## Submission Requirements

- **For Tableau:** Share the Tableau Public link of your work.

- **For Python:** Submit your code in a PDF file.

- **Detailed Project Report:** Create a detailed project report following the provided sample.

## Project Evaluation Metrics

- **Code Quality:** Ensure the code is modular, safe, testable, maintainable, and portable.

## Steps to Get Started

1. **Load the Data:** Import the dataset into your analysis tool (Python, Tableau, etc.).

2. **Clean the Data:** Handle missing values and ensure data consistency.

3. **Perform Analysis:** Address each question by analyzing the data and creating visualizations.

4. **Create Visualizations:** Use appropriate tools to create visual representations of your findings.

5. **Report Findings:** Document your analysis process, findings, and visualizations in a detailed report.

## Example Code Explanation

The provided Python code snippet helps you get started with the analysis:

- **Loading Data:** Load the data from the Excel file.

- **Data Cleaning:** Check for missing values and handle them appropriately.

- **Exploratory Data Analysis (EDA):** Perform initial visualizations to understand the data.

- **Detailed Analysis:** Analyze specific aspects such as yearly trends, top airlines, top airports, costs, time of occurrence, altitudes, phases of flight, and impact on flights.