## Inspiration

The COVID-19 pandemic has changed the way we go about everyday errands and trips. Along with needing to plan around wait times, distance, and reviews for a location we may want to visit, we now also need to consider how many other people will be there and whether its even a safe establishment to visit. *Planwise helps us plan our trips better.*

## What it does

Planwise searches for the places around you that you want to visit and calculates a PlanScore that weighs the Google **reviews**, current **attendance** vs usual attendance, **visits**, and **wait times** so that locations that are rated highly, have few people currently visit them compared to their usual weekly attendance, and have low waiting times are rated highly. A location's PlanScore **changes by the hour** to give users the most up-to-date information about whether they should visit an establishment. Furthermore, PlanWise also **flags** common types of places that are prone to promoting the spread of COVID-19, but still allows users to search for them in case they need to visit them for **essential work**.

## How we built it

We built Planwise as a web app with Python, Flask, and HTML/CSS. We used the Google Places and Populartimes APIs to get and rank places.

## Challenges we ran into

The hardest challenges weren't technical - they had more to do with our *algorithm* and considering the factors of the pandemic. Should we penalize an essential grocery store for being busy? Should we even display results for gyms in counties which have enforced shutdowns on them? Calculating the PlanScore was tough because a lot of places didn't have some of the information needed. We also spent some time considering which factors to weigh more heavily in the score.

## Accomplishments that we are proud of

We're proud of being able to make an application that has actual use in our daily lives. Planwise makes our lives not just easier but **safer**.

## What we learned

We learned a lot about location data and what features are relevant when ranking search results.

## What's next for Planwise

We plan to further develop the web application and start a mobile version soon! We would like to further **localize** advisory flags on search results depending on the county. For example, if a county has strict lockdown, then Planwise should flag more types of places than the average county.
## Inspiration

We got lost so many times inside MIT... And no one could help us :( No Google Maps, no Apple Maps, NO ONE. Since now, we always dreamed about the idea of a more precise navigation platform working inside buildings. And here it is. But that's not all: as traffic GPS usually do, we also want to avoid the big crowds that sometimes stand in corridors.

## What it does

Using just the pdf of the floor plans, it builds a digital map and creates the data structures needed to find the shortest path between two points, considering walls, stairs and even elevators. Moreover, using fictional crowd data, it avoids big crowds so that it is safer and faster to walk inside buildings.

## How we built it

Using k-means, we created nodes and clustered them using the elbow diminishing returns optimization. We obtained the hallways centers combining scikit-learn and filtering them applying k-means. Finally, we created the edges between nodes, simulated crowd hotspots and calculated the shortest path accordingly. Each wifi hotspot takes into account the number of devices connected to the internet to estimate the number of nearby people. This information allows us to weight some paths and penalize those with large nearby crowds.
A path can be searched on a website powered by Flask, where the corresponding result is shown.

## Challenges we ran into

At first, we didn't know which was the best approach to convert a pdf map to useful data.
The maps we worked with are taken from the MIT intranet and we are not allowed to share them, so our web app cannot be published as it uses those maps...
Furthermore, we had limited experience with Machine Learning and Computer Vision algorithms.

## Accomplishments that we're proud of

We're proud of having developed a useful application that can be employed by many people and can be extended automatically to any building thanks to our map recognition algorithms. Also, using real data from sensors (wifi hotspots or any other similar devices) to detect crowds and penalize nearby paths.

## What we learned

We learned more about Python, Flask, Computer Vision algorithms and Machine Learning. Also about frienship :)

## What's next for SmartPaths

The next steps would be honing the Machine Learning part and using real data from sensors.
## 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!