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.
Takes in the following text:
Outputs the correct Tagged words using Named Entity Recognition:
- 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.
