BUS5PA Predictive Analytics
– Semester 1, 2021
Assignment 2: Building and Evaluating Predictive Models using SAS Enterprise Miner
Release Date: 19th April, 2021
Due Date: 14th May 11.55 pm – Individual Report
Format of Submission:
A report (electronic form) and electronic submission of SAS project.
a) Demonstrate knowledge of building different types of predictive models using SAS Enterprise Miner
b) Demonstrate skill and knowledge in applying predictive models in a real-life predictive analytics task
c) Relate theoretical knowledge of predictive models and best practices to application scenarios
Business Case – Predictive Model for Vehicle Price Prediction
Alpha (Pvt) Ltd is an Australian online car sales platform for providing an effective car buying and selling service. In order to help boost sales transactions, the management of Alpha is in the process of building a car price estimation system to help second-hand car sellers to sell their cars at the best price.
Alpha management is very keen to trial predictive modeling for this task and has gathered a historic car sales dataset from a publicly available data repository. The dataset contains 21 variables describing previously sold cars. The attributes include the selling price of cars, year, odometer reading, fuel type, condition, location, etc. The list of attributes and their descriptions are given below. Variable Description id Unique Id of the record
Region price Price
Launch year manufacturer Manufacturer
Model condition Overall condition of the vehicle
Number of cylinders fuel Fuel type
Odometer reading title_status Condition – whether the vehicle is free from accidents/ repaired/ rebuilt etc.
Transmission type vin Vehicle identification number
Drive type size Size of the vehicle
BUS5PA Predictive Analytics - 2021
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Vehicle type paint_color Paint color
County state State
Lat long Long
The management of Alpha (Pvt) Ltd. is considering you as an external consulting group to outsource the task to develop a reliable predictive model to predict the selling price of the cars, using the aforementioned historic dataset. Alpha has provided you with a sample data sets of BMW, Mercedes, Toyota and Honda cars to build separate price-prediction models. They also wish to compare and contrast the pricing models between two car brands.
You have to select one dataset from the four datasets provided. Based on the selected vehicle dataset, you are required to build different predictive models, compare and contrast which is the best model for the selected dataset. You are also provided with a scoring dataset which you need to use for price prediction.
1. Setting up the project and exploratory analysis (10%)
a. Create a new project and create a data source based on the selected dataset.
b. Carry out a data exploration by using a StatExplore Node. Explain your findings with regard to your vehicle dataset.
c. Create a Data Partition with 70% of the data for training and 30% for validation.
2. Decision tree-based modeling and analysis (20%)
Carry out the following modeling tasks for the selected vehicle dataset.
a. Create two Decision Tree models. Use two-way and three-way splits to create the two separate decision tree models.
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