Shark Attacks Data Analysis

This project analyzes shark attack data to identify patterns and trends in shark-human interactions. By examining data from various regions and seasons, the project aims to provide insights into factors influencing shark attacks and implications for public safety. The analysis includes geographical distribution, demographic trends, and seasonal variations in shark activity, offering valuable information for stakeholders such as businesses, tourism agencies, and water sports enthusiasts.

Table of Contents

1. Installation
2. Usage
3. Features/Functionalities

Installation

Instructions for installing the code and any dependencies required.

# Clone the repository
git clone https://github.com/iSundhararajan/Shark_Attacks.git

# Install dependencies
pip install -r requirements.txt

Usage

Instructions for using the code and examples of how to run it.

# Command to run the code
python main.py --input input_file.txt --output output_file.txt

Features/Functionalities

1. Data Exploration and Hypothesis Formulation:

   • Utilizes exploratory data analysis techniques to understand the structure and content of the Shark Attack dataset.
   • Formulates hypotheses about factors influencing shark attacks, such as geographical locations, activities, or seasonal variations.

2. Data Cleaning Techniques:

   • Implements Python programming and the pandas library to perform at least five data cleaning techniques.
   • Techniques include handling missing values, duplicates, and formatting inconsistencies in the dataset.

3. Exploratory Data Analysis (EDA):

   • Performs basic exploratory data analysis to validate initial hypotheses and extract meaningful insights.
   • Utilizes descriptive statistics, data visualization, and correlation analysis to explore relationships between variables and identify patterns in the data.

4. Business Case Implementation:

   • Applies findings from the data analysis to address a defined business case, such as suggesting destinations for shark spotting trips or identifying safe locations for setting up surf shops.
   • Provides recommendations based on insights derived from the data to support business decision-making processes.

5. Documentation and Code Structure:

   • Ensures clear documentation of code functionality, including comments and docstrings for clarity and reproducibility.
   • Organizes code into modular functions or classes to facilitate code reuse and maintainability.

6. Usage Instructions:

   • Provides clear instructions for running the code, including installation steps and examples of command-line usage.
   • Includes sample input data and expected output to demonstrate the functionality of the code.

Our Presentation

https://docs.google.com/presentation/d/1OGd7DCcsSH8YKNK7F5VwOIE-4SYFw-IrhvNdEJq7fVQ/edit?usp=sharing