# Allocating Resources After a Major Weather Event
## Overview
When major weather events occur, insurance companies often hire outside contractors to assist the claims adjusters due to the large, temporary increase in claims. 
The taske it to help the insurance company decide where to send the contractors in the aftermath of Hurricane Harvey. 
To do so, it is needed to analyze the 2017 storm event data, create a report, and provide recommendations to management regarding where to send the contractors to.

It is assumed that Harvey related events occurred only from the 17th of August to the 3rd of September. 

### Expected content
<img src="/images/DA Projects/harley1.png" height="150"> <img src="/images/DA Projects/harley2.png" height="150"> <img src="/images/DA Projects/harley3.png" height="150">
<img src="/images/DA Projects/harley4.png" height="150">

## Motivation
In 2017, Hurricane Harvey became one of the costliest hurricanes on record, causing approximately $125 billion in damage. 
Most of the damage was caused from flooding with some areas receiving over 40 inches (102 cm) of rain.

Hurricanes are large weather events with a specific definition – a rotating low-pressure weather system with sustained winds of 74+ mph (119 km/h). 
Harvey became a hurricane August 24th, made landfall on the 25th, and was downgraded to a tropical storm on August 26th.

The impact of Harvey was felt over much more than just 3 days. 
In the 2017 storm events data set, Harvey related events are reported beginning August 17th and end September 3rd as the system moved north and east across the United States. 
Flooding, thunderstorms, hail, and tornadoes are just a few of the weather events related to Harvey. 

The task as the hired data scientist is to find the states and counties with the most damage from Harvey related events and how to best allocate the company resources.  

## Technologies Used
<img src="/images/python.png" height="100"> <img src="/images/matplotlib.jpeg" height="100"> <img src="/images/pandas.png" height="100"> <img src="/images/numpy.png" height="100">
