Motonitor

front screenshot

Inspiration

What encouraged us to involve us in the development of this project is the freedom that the project provided with the treatment of the data. We wanted to use it in a creative way to build a solution for a possible problem that the company would have, that is giving the clients an estimated price for their motorbikes before they directly meet an agent.

What it does

The software lets the user calculate an estimated price of their motorbikes from home, letting them have a preview before starting contact with any agent, or selling it for a worse price.

How we built it

For this project we got a really poor dataset, then we decided to scrap their own webpage to get a nice and clean data from their current motorbike stock building a deep scrapper (parallelized). Other 2 parts of the project are the front-end client which will manage all the data between the server and the client and the back-end server which will work on a machine learning model to get the best possible predictions based on the actual website data.

Challenges we ran into

One of the most important challenges we ran into was the dataset we had to work with at the beginning, which was impossible to use to build a minimal valid machine learning model. After this we had another big challenge that was scrapping all the data to be able to build the working the ml-model which was also a hard challenge for the complexity and diversity of possibilities what we can use to calculate it and get clear conclusions.

Accomplishments that we're proud of

We are proud of our parallelized data scrapping software, the machine learning model we built and all the challenges which led us to work together to solve them.

What we learned

We have learnt a lot in different aspects of informatics like Machine Learning, Data Scrapping, Data Cleaning, and other developer topics that we needed to investigate to achieve our goals.

What's next for Motonitor

After this huge project that had us 36 hours with a non-stop of coding (no sleep), we expect to keep expanding our knowledge by working on similar projects that keep us focused on this topics that we liked so much.