## Inspiration

Have you ever used a budgeting app? Do you set budgets for each month?
I use a budgeting app and when I check my weekly spending, I always realize I spent a $100 too much!
So, we decided to make something proactive rather than reactive that will help you immensely in your spending decisions.

## What it does

The app will alert you in real-time when you enter places like coffee shop, grocery store, restaurant, bar, etc. and tell you how much budget you have left for that paticular place type. It will tell you something like "You have $40 left in your Grocery Budget" or "You have $11 left in your Coffee Budget". This way you can make better spending decision.

## How we built it

We used Android platform and Google Places API to built the app.

## Challenges we ran into

It was hard to implement Google Places API automatically without the app running in the background. Our plan was to send real-time alerts to a wearable device but we did not have access to any, so we decided to use make the smart phone app instead.

## Accomplishments that we're proud of

Implementing Google Places API and quering data from Firebase in real-time using in-built GPS successfully without the use of Wi-Fi or Bluetooth.

## What we learned

We learned querying data from Firebase and learned how we can identify places around easily using Google Places API

## What's next for Budget Easy

We want to parter with a bank or budgeting app which tracks user's spending and integrate this proactive feature into their app!
## Inspiration

The expense behavior of the user, especially in the age group of 15-29, is towards spending unreasonably amount in unnecessary stuff. So we want them to have a better financial life, and help them understand their expenses better, and guide them towards investing that money into stocks instead.

## What it does

It points out the unnecessary expenses of the user, and suggests what if you invest this in the stocks what amount of income you could gather around in time.

So, basically the app shows you two kinds of investment grounds:

1. what if you invested somewhere around 6 months back then what amount of money you could have earned now.
2. The app also shows what the most favorable companies to invest at the moment based on the Warren Buffet Model.

## How we built it

We basically had a python script that scrapes the web and analyzes the Stock market and suggests the user the most potential companies to invest based on the Warren Buffet model.

## Challenges we ran into

Initially the web scraping was hard, we tried multiple ways and different automation software to get the details, but some how we are not able to incorporate fully. So we had to write the web scrapper code completely by ourselves and set various parameters to short list the companies for the Investment.

## Accomplishments that we're proud of

We are able to come up with an good idea of helping people to have a financially better life.
We have learnt so many things on spot and somehow made them work for satisfactory results. but i think there is many more ways to make this effective.

## What we learned

We learnt firebase, also we learnt how to scrape data from a complex structural sites.
Since, we are just a team of three new members who just formed at the hackathon, we had to learn and co-operate with each other.

## What's next for Revenue Now

We can study our user and his behavior towards spending money, and have customized profiles that suits him and guides him for the best use of financial income and suggests the various saving patterns and investment patterns to make even the user comfortable.
## Inspiration

The only thing worse than no WiFi is slow WiFi. Many of us have experienced the frustrations of terrible internet connections. We have too, so we set out to create a tool to help users find the best place around to connect.

## What it does

Our app runs in the background (completely quietly) and maps out the WiFi landscape of the world. That information is sent to a central server and combined with location and WiFi data from all users of the app. The server then processes the data and generates heatmaps of WiFi signal strength to send back to the end user. Because of our architecture these heatmaps are real time, updating dynamically as the WiFi strength changes.

## How we built it

We split up the work into three parts: mobile, cloud, and visualization and had each member of our team work on a part. For the mobile component, we quickly built an MVP iOS app that could collect and push data to the server and iteratively improved our locationing methodology. For the cloud, we set up a Firebase Realtime Database (NoSQL) to allow for large amounts of data throughput. For the visualization, we took the points we received and used gaussian kernel density estimation to generate interpretable heatmaps.

## Challenges we ran into

Engineering an algorithm to determine the location of the client was significantly more difficult than expected. Initially, we wanted to use accelerometer data, and use GPS as well to calibrate the data, but excessive noise in the resulting data prevented us from using it effectively and from proceeding with this approach. We ran into even more issues when we used a device with less accurate sensors like an Android phone.

## Accomplishments that we're proud of

We are particularly proud of getting accurate paths travelled from the phones. We initially tried to use double integrator dynamics on top of oriented accelerometer readings, correcting for errors with GPS. However, we quickly realized that without prohibitively expensive filtering, the data from the accelerometer was useless and that GPS did not function well indoors due to the walls affecting the time-of-flight measurements. Instead, we used a built in pedometer framework to estimate distance travelled (this used a lot of advanced on-device signal processing) and combined this with the average heading (calculated using a magnetometer) to get meter-level accurate distances.

## What we learned

Locationing is hard! Especially indoors or over short distances.
Firebase’s realtime database was extremely easy to use and very performant
Distributing the data processing between the server and client is a balance worth playing with

## What's next for Hotspot

Next, we’d like to expand our work on the iOS side and create a sister application for Android (currently in the works). We’d also like to overlay our heatmap on Google maps.

There are also many interesting things you can do with a WiFi heatmap. Given some user settings, we could automatically switch from WiFi to data when the WiFi signal strength is about to get too poor. We could also use this app to find optimal placements for routers. Finally, we could use the application in disaster scenarios to on-the-fly compute areas with internet access still up or produce approximate population heatmaps.