This was a proof of concept project I tackled with my dad to get our feet wet in machine learning (ML). He loves stats and data so it was a great fit to work this one out together.
I was wondering if machine learning would be able to help shed light on classic car sales prices. So would it be possible to actually give the machine some vehicle properties and let the machine analyse using a model deduced from a data set, to yield a projected price for any given classic car. So in other words, if I want to purchase a Mercedes 280SL 1988 model which is white and has 250 000 Km on the clock, is the asking price legit or is it ludicrous? It is important to note that we weren't looking for the median price but rather a projected price based on the properties we fed the machine learning algorithm. We were trying to establish a market value based on vehicle properties and not simply the average car price for a specific year.
The client/user interface would be an Android app. Asking just a few questions about the classic car and send this off to the Isivuno platform which now has ML integration built into it for data harvesters which might want to generate models based on the data they have harvested. We obviously used this data mining platform for the data of about 30K classic cars currently for sale in South Africa only.
After some tweaking of the algorithm we got the error % down to about 10% which is not too bad. We ran out of time due to other work commitments but it was a huge learning curve to work with Python and an ML toolkit.