Predicting StockX Sneaker Prices With Machine Learning

Predicting StockX Sneaker Prices With Machine Learning

Sneaker prices change like stocks. Let me show you how I predict them with my machine learning model. StockX is a platform where individuals can post and resell shoes, while also providing statistics and analytics about each pair of shoes.

The Footwear industry consists of companies engaged in the manufacturing of footwear such as dress shoes, slippers, boots, galoshes, sandals and athletic and trade related footwear; however, the most lucrative sector of this industry is collectible sneakers. The rise of marketplace apps like StockX and GOAT, alongside the proliferation of social media sites where you’re just one message away from turning a rare pair of trainers into cash, mean that more people are selling their shoes than ever before. The global sneaker resale market has been valued at over $2 billion, while the right pair of kicks can go for over $10,000 💸. Moreover, the massive margin of profit for each shoe makes the resale market attractive to those who would like to make some extra cash, given that in the past year, the average profit margin in the sneaker industry was 42.5%.

While there is plenty of money to be made, it can be risky to buy a shoe due to the volatile nature of each shoe. Sneakers are like stocks with their resale price constantly changing from day to day. Thus, I developed this web application to predict the price of a given shoe based on factors such as date, shoe size, buyer region, and more.

This tool resolves the issue of knowing which sneaker is worthwhile and when to buy it. As a “sneakerhead” and reseller myself, I know that this program will have lots of value in the community.

For in-depth details on this project, check out my GitHub Repo.

python predictions sneakers machine-learning programming

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