In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.
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.
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.
Build your own image similarity application using Python to search and find images of products that are similar to any given product. You will implement the K-Nearest Neighbor algorithm to find products with maximum similarity.
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.
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.
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.