Assignment Title: "Exploring Predictive Factors in Residential Real Estate: Constructing an

Analytic Base Table $($ABT$)"$

Assignment Description: In this assignment, the objective is primarily identifying and

selecting predictive features for constructing an Analytic Base Table $($ABT$)$ to predict house

prices in Vancouver. Unlike traditional assignments where features are predefined,

students will take on the role of data scientists and conduct exploration and scenario

development to uncover the factors that may have significant influence on residential

property prices.

Key Objectives:

$1.$ Data Exploration: Students will thoroughly identify and examine the key features

and potential predictors that may influence house prices, considering both

traditional factors and unconventional variables. The students need to identify the

rationales for including the features they identify.

As part of the assignment, each students studies the selected features against the

ROCCC test $($Reliable$,$ Original, Comprehensive, Current, and Cited$)$ to determine

the quality and credibility of the analysis presented. The ROCCC test serves as a

framework for assessing the validity and trustworthiness of the information provided

in the case study.

$2.$ Feature Engineering: Leveraging domain knowledge and insights gained from data

exploration, students will engineer new features or derive meaningful variables that

capture the nuanced characteristics of residential properties in Ames. They will

consider factors such as neighborhood demographics, property condition,

architectural styles, and proximity to amenities in their feature engineering process.

$3.$ ABT Construction: Using the selected features and engineered variables, students

will construct an Analytic Base Table $($ABT$)$ that serves as the foundation for

predicting house prices. The ABT will consolidate all relevant information about

residential properties, providing a structured dataset for developing predictive

models.

Outcome: By completing this assignment, students will develop practical skills in

constructing an Analytic Base Table $($ABT$)$ for predictive modeling in the context of

residential real estate. They will gain insights into the complex interplay of factors

influencing house prices and learn how to leverage data$-$driven approaches to predict

housing market trends and inform decision making in the real estate industry. Through this

assignment, students will enhance their proficiency in data exploration, preprocessing,

and feature engineering.

Output: Prepare a document with maximum ten pages to identify the selected features,

and why and how you came up with selected features. Use creative approaches to

summarize your findings.

Marking Schema for Analytic Base Table $($ABT$)$ Construction Assignment

Criteria Description Marks

Feature

Identification

Student identifies a broad spectrum of potential features influencing house

prices, demonstrating domain knowledge and analytical skills. $3$

Feature

Selection

Student selects relevant and useful features based on investigation and

correlation, and domain expertise, prioritizing features with high predictive

power and interpretability. $3$

ABT

Construction

Student constructs a mock$-$up well$-$organized Analytic Base Table $($ABT$)$

that consolidates selected features, providing a structured dataset for

predictive modeling. $2$

Summary Data

Students find a visual representation of a summary of the findings and

rationales related to why and how the data were selected. $2$

ABT Data Relative evaluation to the class. $5$

Total $15$

Note: The rubric is designed to evaluate students' proficiency in key aspects of ABT

construction, including data acquisition, exploratory data analysis, feature identification

and selection, and ABT construction. Each criterion is allocated a specific number of

marks based on its importance to the overall assignment.