Each project comes with 2-5 hours of micro-videos explaining the solution.
Get access to 50+ solved projects with iPython notebooks and datasets.
Add project experience to your Linkedin/Github profiles.
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.
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.
In this project, we are going to talk about insurance forecast by using regression techniques.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
In this data science project, we will predict the number of inquiries a new listing receives based on the listing's creation date and other features.