USA Optimal Product Price Prediction Dataset

This dataset contains product prices from Amazon USA, with a focus on price prediction. With a good amount of data on what price points sell the most, you can train machine learning models to predict the optimal price for a product based on its features and product name.

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Inspirations

This dataset is a superset of my Amazon USA product price dataset. Another inspiration is this competition that awareded 100K Prize Money

What To Do?

• Your objective is to create a prediction model that will assist sellers in pricing their products within the optimal price range to generate the most sales.
• The dataset includes various data points, such as the number of reviews, rating, best seller status, and items sold last month.
• You can select specific factors (e.g., over 100 reviews = optimal price for the product) and then divide the dataset into products priced optimally vs products priced unoptimally.
• By utilizing techniques like vectorizing product names and features, you can train a model to provide the optimal price for a product, which sellers or businesses might find valuable.

How to know if a product sells?

• I would prefer to use the number of reviews as a metric to determine if a product sells. More reviews = more sales, right?
• According to one source only 1-2% of buyers leave a review
• So if we multiply the reviews for a product by 50x, then we would get a good understanding how many units has sold.
• If we then multiple the product price by number of units sold, we'd get the total revenue generated by the product

How is this useful?

• Sellers and businesses can leverage your model to determine the optimal price for their products, thereby maximizing sales.
• Businesses can assess the profitability of a product and plan their supply chain accordingly.