🦈 Shark-Attack Exploratory Data Analysis (EDA)

This project focuses on the analysis of shark attack data worldwide. Using a detailed dataset, we have conducted an Exploratory Data Analysis (EDA) to identify trends, patterns, and key factors related to shark attacks. Through meaningful visualizations and conclusions, we aim to provide a deeper understanding of these events and their characteristics.

🔍 Exploratory Data Analysis (EDA)

📈 Historical Evolution of Shark Attacks

• Conclusion: The number of shark attacks has shown an increasing trend over the years, with significant peaks in recent decades.

🌍 World Map of Shark Attacks

• Conclusion: The United States, Australia, and South Africa are the countries with the highest number of recorded shark attacks.

🇺🇸 USA Map of Shark Attacks

• Conclusion: Florida is the state with the highest number of shark attacks in the United States, followed by Hawaii and California.

📅 Distribution of Attacks by Month

• Conclusion: The months of January, July, and August have the highest number of shark attacks, while February, May, and November have the fewest attacks.

🦈 Types of Shark Attacks

• Conclusion: Most shark attacks are unprovoked, followed by provoked attacks and those related to watercraft.

🏄 Activities with Most Attacks

• Conclusion: Surfing and swimming are the activities with the highest risk of shark attacks, while activities like skin diving and kayak fishing present lower risk.

👨‍👩‍👧‍👦 Distribution of Attacks by Age and Gender

• Conclusion: Most shark attack victims are men, and the age distribution shows that younger individuals are more prone to attacks.

📊 Key Techniques Used

1. Data Cleaning: Extensive data cleaning was performed to ensure the dataset's accuracy and consistency.
2. Data Visualization: Various visualizations were created to illustrate trends and patterns in the data.
3. Geospatial Analysis: Maps were used to show the geographical distribution of shark attacks.
4. Statistical Analysis: Statistical methods were applied to identify significant trends and correlations.

📂 Project Structure

• notebooks/
  • main.ipynb: Data cleaning and preprocessing.
  • exploratory_analysis.ipynb: Exploratory data analysis and visualizations.
• data/
  • raw/: Raw data files.
  • processed/: Cleaned and processed data files.
• img/: Images used in the README and notebooks.

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/yourusername/Shark-Attack-EDA.git
   cd Shark-Attack-EDA

2. Install dependencies:

   pip install -r requirements.txt

3. Run the notebooks: Open main.ipynb and exploratory_analysis.ipynb in Jupyter Notebook or JupyterLab and run the cells.

📈 Results and Conclusions

• The analysis revealed significant trends in shark attacks over time and across different regions.
• The visualizations provided insights into the most dangerous activities and times of the year for shark attacks.
• The findings can help inform safety measures and awareness campaigns to reduce the risk of shark attacks.

📧 Contact

For any questions or feedback, please contact [your email].

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Note: This project is for educational purposes only. The data and conclusions should be interpreted with caution and in the context of broader research.