3 Use Cases

  • Regression Use case
    The Brooklyn housing dataset contains the sale prices of homes in brooklyn borough, along with various factors that influence these prices, such as the area of the house, its location, and the type of dwelling. You are tasked with analyzing years of historical home sales data to estimate sales prices, which will help optimize real estate operations. In this case study, you will learn how to predict sales prices using the regression technique and the GLM algorithm.
  • Classification Use Case
    A retail store has information about its customers' behavior and the purchases they make. Now with the available data, they would like you to analyze and identify the type of customers they should target which would result in an increase in the volume of the most profitable product sold, and an increase in profit. In this use case, you will demonstrate how to identify such customers using the Random Forest algorithm.
  • Clustering Use Case
    A retail store has information about its customers' behavior and the purchases they make. Now with the available data, they would like you to analyze and identify if there are any similarities between the customers. Use Oracle Machine Learning to segment customers by finding clusters in the data set which can be then used to support targeted marketing campaigns to increase retail sales. In this use case, you will learn how to identify such segments using the k-Means algorithm.