Scrapeyard

Inspiration

The used car market is overwhelming, with prices fluctuating across different platforms. It’s hard to tell whether you're getting a fair deal. Inspired by this challenge, we wanted to create a tool that not only aggregates these prices but also helps buyers make informed decisions by comparing them with fair values based on historical trends.

What it does

Scrapeyard scrapes prices of used cars across popular online platforms, gathers historical pricing data, and computes a fair price. It allows users to view price comparisons, helping them identify whether the listed prices are in line with the market trends. Additionally, users have the option to view graphically how prices vary with varying features of a particular car type.

How we built it

We built Scrapeyard using Python to gather and analyze the pricing data, incorporating pandas for data manipulation. The app is powered by a Streamlit interface for data visualisation, and the backend is built using Django while the frontend uses Nextjs.

Challenges we ran into

One of the main challenges was integrating data from multiple sources, as each platform presents the car data differently. Standardizing this information into a cohesive format required substantial data cleaning. Additionally, designing an accurate pricing comparison model based on historical data required balancing between simplicity and robustness.