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A Technical Note on Data Preparation and Model Building with a Real Estate Dataset
內容大綱
Data preparation is a necessary pre-processing step in analytics. It aims to clean the data from various resources and improve its quality for better productivity. This process includes many tasks such as fusion, cleaning, and augmentation of data. This teaching note will focus on illustrating data cleaning using the programming language Python, with all codes completed in Google Laboratory. Different solutions using the programming languages R and Microsoft Excel are also provided. To effectively illustrate the data preparation process, the relatively simple dataset Bengaluru House Prices is used. This is a relatively messy dataset with a few variables and many records, making it ideal for explaining data preparation steps.
學習目標
This technical note is suitable for undergraduate- and graduate-level courses on data science, finance, management science, operations, accounting, marketing, statistics, economics, or any quantitative decision making courses. After working through this technical note, students will be able to<ul><li>understand the steps for data preparation;</li><li>learn about the steps for model building; and</li><li>review the data preparation and model building implementation steps using the programming tools Python, R, and Microsoft Excel with a real estate dataset.</li></ul>