Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.
In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.