advidical/data-cleaning-pandas

Jaws Attack - Team Matt Cooper

Introduction

This project aims to analyze and visualize data related to shark attacks worldwide. The dataset used in this project contains information about shark attacks such as location, date, type of activity, and more. By exploring this dataset, we aim to gain insights into factors influencing shark attacks and raise awareness about shark-human interactions.

Important Notes

Original Data Set

The original dataset used in this project is found in (https://www.sharkattackfile.net/incidentlog.htm). It includes information about shark attacks from various sources, compiled into an extensive dataset, provided in excel format. It includes categories such as the date, time, location, type of encounter, species of shark, type of injury, activity that the victim was doing prior to the attack, as well as many others.

Problem & Hypothesis

For many years, the Great White Shark has been given a bad reputation when it comes to human encounters, going as far as being the antagonist to the 1975 classic movie, Jaws. Given that there are over 500 species of sharks in the world, is it possible that the top species in shark attacks is not in fact the Great White? Furthermore, despite it being a popular fear in the ocean, we believe it is much more common for an injury to be non fatal in such encounters.

Analysis

The group aims to explore and analize the original data, focusing on the following: The various species of sharks recorded in the data set, filtering out the most popular The type of injuries most often presented and dividing them by fatal and non-fatal

About the DataSet

To perform a similar analisis follow these simple steps:

1. Download the dataset (you will find it in https://www.sharkattackfile.net/incidentlog.htm)
2. Install dependencies into your coding notebook
3. Run various codes to explore and analyze the data
4. Come up with a conclusion with your findings

Dependencies

You will need to import the following:

1. Pandas --> import pandas as pd
2. Numpy --> import numpy as np
3. Regex --> import re
4. Seaborn --> import seaborn as sns

Presentation

Click the link below for a simple presentation regarding our data findings: https://docs.google.com/presentation/d/1SF5qa-4_7m0_acO5jmxaXOaPayokIlvpFpywSHyJlGM/edit?usp=sharing You may also clone this repo onto your local machine and open the powerpoint file in Hypothesis & Presentation folder