The problem QuickPark solves

QuickPark provides the users with an efficient and less time-consuming method to book parking slots at their will by location tracking.

The common problem most drivers face is finding a free, well-defined, and legitimate parking spot. Be it shopping malls or any local market areas, this problem exists in every sphere of our lives. It can often be quite irritating for visitors to drive around in search of a vacant parking spot. Due to this, many drivers have to unwillingly stop and leave their vehicles at the edge of streets/roads which further leads to crowding and an increase in frustration levels of the public. During busy hours or on weekends, this can cause an additional lead time in getting in and out of the parking area.

A recent global study of parking in big cities across the world suggests that the average city driver spends an average of 18 to 20 minutes searching for a parking space, resulting in stress, congestion on streets, and decrease in productivity. In addition to this, high amounts of fuel is wasted in this endless search for an optimal parking spot, leading to increased emission of harmful gases which in turn has hazardous effects on human health and also spoils the air quality.

Our application ensures that the user finds a safe spot to park their vehicle and henceforth conserves valuable time, fuel, and needless hassle. Search your desired location and check the availability with a single click. Confirm your booking and you are good to go. It's that simple!

Challenges we ran into

Being beginners and having no experience of participating in Hackathons, we came across many challenges and hurdles in the making of this project.
The very first challenge we faced was rendering the map to the HTML page. Although we had worked with pygeocoder and folium modules which help in geolocation, we had no clue how to render a map object through a Django application on the front-end. After some research, we found an already existing project based on geolocation in Django. Taking some help and insight from there, we implemented the idea successfully.
The second challenge we faced was storing login and registration information without the use of a database. Instead, we used a .csv file to store login information and we manipulated the data using a Pandas Dataframe, which solved the problem on a small scale. We also wrote the dataframe into the same .csv file repeatedly and called the .csv file again in all functions to reflect real-time changes in login information.