Chapter 5 Data Preparation Data Preparation The data preparation will double as the first portion of the upcoming project. Your goal is to take the data on basketball statistics and clean it to the point that it may be analyzed using the data analysis techniques that we will cover in chapter 6. The key goals … Continue reading “Data Preparation | My Assignment Tutor”
Chapter 5 Data Preparation Data Preparation The data preparation will double as the first portion of the upcoming project. Your goal is to take the data on basketball statistics and clean it to the point that it may be analyzed using the data analysis techniques that we will cover in chapter 6. The key goals of cleaning up this data are: Assembling the data in a datasheet format:One header row, with clear headers.One instance (team’s season) per row.Translation for making the playoffs (more below).A mirrored copy of the source data, containing the two most recent seasons on which the actual predictions will be made. The source for our data is Basketball Reference (https://www.basketball-reference.com/leagues/NBA_2018.html). The exact data used to build your model is something that you must determine – the starting point is to download the Miscellaneous Stats table into Excel: You may need to modify column names, or otherwise clean the data to prepare it for analysis. As part of this you will need to create a Playoff column that holds a variable denoting if the team made the playoffs (1 = playoffs, 0 = no playoffs) or not (it is denoted by a star on the original data). Once you’ve assembled your sheet with the 2017-2018 data, repeat the process for at least 4 previous seasons, so you have 150+ total rows of data on which to build the model. Note: When you’ve created the sheet, you must remove the Win-proxy stats, some of those are L, PW, PL, MOV, and SRS. There may be others depending if you’ve added extra stuff to your model. Ensure you double check this as inclusion of any win/loss statistics will ruin the accuracy of your model when we get to the project. Subject Data To prepare your subject data you must basically redo the same steps in setting up your Source Data for the most recent full seasons – 2018-2019. Also, delete all values from the wins (W) column and the playoff columns as those are our predicted values. Place this into another sheet in the same workbook.
