Home Depot Product Search Relevance ML Project in Python

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

Understanding the problem statement
Importing a Zipped Dataset and unzipping it into Dataframe
Importing necessary libraries
Basics for NLP
Performing basic EDA and data-preprocessing
Stemming and Lemmatization, differences and their uses
Porters stemmer , Lancaster stemmer, and snowball stemmer
Creating new features using the existing features(feature engineering)
Using Lambda function and its significance
Dropping unnecessary columns for final training
Performing train_test_split for training and testing dataset
Applying ensemble model "Random Forest Regressor"
Applying bagging model "Bagging Regressor"
Making final predictions using the best performing model
Saving the final predictions using CSV format

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.

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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.

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Curriculum For This Mini Project

04h 12m