3 Use Cases

• Estimate Brooklyn Home Prices Using GLM Regression
  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.
• Identify Profitable Customer Segments Using Random Forest
  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.
• Customer Segmentation in Retail Using k-Means Clustering
  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.
• Forecast Future Sales Using Time Series Analysis
  You work in an electronic store, and sales of laptops and tablets have increased over the last two quarters. You want to forecast your product sales for the next four quarters using historical timestamped data. You forecast sales using the Exponential Smoothing algorithm, predicting changes over evenly spaced intervals of time using historical data.
• Recommend Movies Using Association Rules
  A popular movie rental website is being updated. The company wants to offer personalized movie recommendations to customers based on their rental and purchase history. They have asked you, a data scientist, to help create these recommendations. To solve this, you use the Apriori algorithm to analyze the movie data and identify groups of movies that are frequently rented or purchased together.