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Home Depot Product Search Relevance ML Project in Python

Given a customer's search query and the returned product in text format, your predictive model needs to tell whether it is what the customer was looking for.

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What will you learn

  • Scikit-learn
  • Pandas
  • Numpy
  • Random Forrest algorithm

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.


  • Language used: Python

Project Description

Shoppers rely on Home Depot’s product authority to find and buy the latest products and to get timely solutions to their home improvement needs. From installing a new ceiling fan to remodeling an entire kitchen, with the click of a mouse or tap of the screen, customers expect the correct results to their queries – quickly. Speed, accuracy and delivering a frictionless customer experience are essential.

Description image.

In this machine learning project, you will help Home Depot improve their customers' shopping experience by developing a model that can accurately predict the relevance of search results.

Search relevancy is an implicit measure Home Depot uses to gauge how quickly they can get customers to the right products. Currently, human raters evaluate the impact of potential changes to their search algorithms, which is a slow and subjective process. By removing or minimizing human input in search relevance evaluation, Home Depot hopes to increase the number of iterations their team can perform on the current search algorithms.