## Inspiration

Insurance Companies spend millions of dollars on carrying marketing campaigns to attract customers and sell their policies.These marketing campaigns involve giving people promotional offers, reaching out to them via mass marketing like email, flyers etc., these marketing campaigns usually last for few months to almost years and the results of such huge campaigns are inedible when raw.

## What it does

Intellisurance visualizes such campaign data and allows the insurance companies to understand and digest such data. These visualizations help the company decide whom to target next, how to grow their business? and what kind of campaigns or best media practices to reach to a majority of their potential customer base.

## How we built it

We wanted to give the insurance companies a clear overview of how their past marketing campaigns were, the pros , the cons, the ways to target a more specific group, the best practices etc. We also give information of how they gained customers over the period. Most key factor being the geographic location, we chose to display it over a map.

## Challenges we ran into

When we are dealing with the insurance campaign data we are dealing with millions of rows, compressing that data into usable information.

## Accomplishments that we're proud of

This part was the most challenging part where we pulled really necessary data and had algorithms to help users experience almost no lag while using the application.
## Inspiration

When we first read Vitech's challenge for processing and visualizing their data, we were collectively inspired to explore a paradigm of programming that very few of us had any experience with, machine learning. With that in mind, the sentiment of the challenge themed around health care established relevant and impactful implications for the outcome of our project. We believe that using machine learning and data science to improve the customer experience of people in the market for insurance plans, would not only result in a more profitable model for insurance companies but improve the lives of the countless people who struggle to choose the best insurance plans for themselves at the right costs.

## What it does

Our scripts are built to parse, process, and format the data provided by Vitech's live V3 API database. The data is initially filtered using Solr queries and then formatted into a more adaptable comma-separated variable (CSV) file. This data is then processed by a different script through several machine learning algorithms in order to extract meaningful data about the relationship between an individual's personal details and the plan that they are most likely to choose. Additionally, we have provided visualizations created in R that helped us interpret the many data points more effectively.

## How we built it

We initially explored all of the ideas that we had regarding how exactly we planned to process the data and proceeded to pick Python as a suitable language and interface in which we believed that we could accomplish all of our goals. The first step was parsing and formatting data after which we began observing it through the visualization tools provided by R. Once we had a rough idea about how our data is distributed, we continued by making models using the h2o Python library in order to model our data.

## Challenges we ran into

Since none of us had much experience with machine learning prior to this project, we dived into many software tools we had never even seen before. Furthermore, the data provided by Vitech had many variables to track, so our deficiency in understanding of the insurance market truly slowed down our progress in making better models for our data.

## Accomplishments that we're proud of

We are very proud that we got as far as we did even though out product is not finalized. Going into this initially, we did not know how much we could learn and accomplish and yet we managed to implement fairly complex tools for analyzing and processing data. We have learned greatly from the entire experience as a team and are now inspired to continue exploring data science and the power of data science tools.

## What we learned

We have learned a lot about the nuances of processing and working with big data and about what software tools are available to us for future use.

## What's next for Vitech Insurance Data Processing and Analysis

We hope to further improve our modeling to get more meaningful and applicable results. The next barrier to overcome is definitely related to our lack of field expertise in the realm of the insurance market which would further allow us to make more accurate and representative models of the data.
## Inspiration

The long and tedious process of processing insurance claims. With Machine learning capabilities from Azure and AWS, we strived to expedite this long process.

## What it does

We developed an app that allows users in accidents to take pictures of their damaged car, and from there, we used Machine learning to determine what percentage the car is damaged. From this and other information submitted by the user, we can make insurance companies' lives way easier by providing them information such as the model of the car, the current market value of the car as well as what % the car is damaged. This will ultimately help speed up their process of determining how much to pay the user for their damaged car alot quicker and the user will get cash back 10x faster.

## How I built it

We used Android studio and java.

## Challenges I ran into

Getting azure's custom Vision API to work was a big pain.

## Accomplishments that I'm proud of

Its Done!

## What I learned

Lots! We learned tons about android studio and how to use azure's custom Vision API.

## What's next for Insure

We plan to expand our target audience to target other insurances, such as house insurance, etc.