## Inspiration

Imagine a world where the number of mass shootings in the U.S. per year don't align with the number of days. With the recent Thousand Oaks shooting, we wanted to make something that would accurately predict the probability that a place has a mass shooting given a zipcode and future date.

## What it does

When you type in a zipcode, the corresponding city is queried in the prediction results of our neural network in order to get a probability. This probability is scaled accordingly and represented as a red circle of varying size on our U.S. map. We also made a donation link that takes in credit card information and processes it.

## How we built it

We trained our neural network with datasets on gun violence in various cities. We did a ton of dataset cleaning in order to find just what we needed, and trained our network using scikit-learn. We also used the stdlib api in order to pass data around so that the input zipcode could be sent to the right place, and we also used the Stripe api to handle credit card donation transactions. We used d3.js and other external topological javascript libraries in order to create a map of the U.S. that could be decorated. We then put it all together with some javascript, HTML and CSS.

## Challenges we ran into

We had lots of challenges with this project. d3.js was hard to jump right into, as it is such a huge library that correlates data with visualization. Cleaning the data was challenging as well, because people tend not to think twice before throwing data into a big csv. Sending data around files without the usage of a server was challenging, and we managed to bypass that with the stdlib api.

## Accomplishments that we're proud of

A trained neural network that predicts the probability of a mass shooting given a zipcode. A beautiful topological map of the United States in d3. Integration of microservices through APIs we had never used before.

## What we learned

Doing new things is hard, but ultimately worthwhile!

## What's next for Ceasefire

We will be working on a better, real-time mapping of mass shootings data. We will also need to improve our neural network by tidying up our data more.
## Inspiration

I was interested in exploring the health datasets given by John Snow Labs in order to give users the ability to explore meaningful datasets. The datasets selected were Vaccination Data Immunization Kindergarten Students 2011 to 2014, Mammography Data from Breast Cancer Surveillance Consortium, 2014 State Occupational Employment and Wage Estimate dataset from the Bureau of Labor Statistics, and Mental Health Data from the CDC and Behavioral Risk Factor Surveillance System.

Vaccinations are crucial to ending health diseases as well as deter mortality and morbidity rates and has the potential to save future generations from serious disease. By visualization the dataset, users are able to better understand the current state of vaccinations and help to create policies to improve struggling states. Mammography is equally important in preventing health risks. Mental health is an important fact in determining the well-being of a state. Similarly, the visualization allows users to better understand correlations between preventative steps and cancerous outcomes.

## What it does

The data visualization allows users to observe possible impacts of preventative steps on breast cancer formation and the current state of immunizations for kindergarten students and mental health in the US. Using this data, we can analyze specific state and national trends and look at interesting relationships they may have on one another.

## How I built it

The web application's backend used node and express. The data visualizations and data processing used d3. Specific d3 packages allowed to map and spatial visualizations using network/node analysis. D3 allowed for interactivity between the user and visualization, which allows for more sophisticated exploration of the datasets.

## Challenges I ran into

Searching through the John Snow Labs datasets required a lot of time. Further processing and finding the best way to visualize the data took much of my time as some datasets included over 40,000 entries! Working d3 also took awhile to understand.

## Accomplishments that I'm proud of

In the end, I created a working prototype that visualizes significant data that may help a user understand a complex dataset. I learned a lot more about d3 and building effective data visualizations in a very constrained amount of time.

## What I learned

I learned a lot more about d3 and building effective data visualizations in a very constrained amount of time.

## What's next for Finder

I hope to add more interaction for users, such as allowing them to upload their own dataset to explore their data.
### Inspiration

A general liking of watching Twitch streams led me to try and make this. When a streamer gets too big, it can be hard to keep track of questions, reactions, subscriber notifications, and more. This application is meant to help streamline the process of broadcaster and chat interaction.

### What it should do

It would gather data from Twitch chat in real time, analyze and catagorize them, and display the results on a web page that gets updated in real time.

### What it actually does

Barely simulates a Twitch chatbot typing into the chat.

### How I built it

I used Nodejs and Reactjs for my framework. Another community developed API called tmi.js connects the Twitch chat to my code. So far, I have programmed the .js to output rudimentary messages in chat.

### Challenges I ran into

For one thing, I have zero prior experience working with JS and APIs, so I had to start from the ground up. Every step of the way, I ran into problems, searched for solutions, and got stuck again.

### Accomplishments that I'm proud of

The fact that I made something that does something is quite amazing. Although it isn't even a minimum viable product, it's still something.

### What I learned

I have learned so much about APIs.

### What's next for Better Twich Chat Info

To conceive the actual goal I want to achieve.