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Product-Named-Entity-Recognition

Indepth Blog on the Project

Train a model to find the names of products in text

Goal is to identify products, locations, conditions, price and other relevant information in text about products.

Example of Early Stages of the Model:

Takes in the following text:

Outputs the correct Tagged words using Named Entity Recognition:

Product Trending

Goals

  • Determine Price Trends
  • Determine average price for product
  • Identify good deals as soon as they come onto the market place

Once the entites have been labeled using the NER model, rows in the dataframe can be filtered for a specific product. In the below example I filter for iPhone X. Using this tool I can identify good deals quickly based on market history. Using the data on the iPhone X i would be looking to buy below 480 as that has been the mean sale price from December 2019 - February 2020.

Listing Location's

12/20/2019 - 2/26/2020

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