Reference no: EM132278178

Excel Data Analysis Capstone Project -

Overview - This project requires that you use the tools learned throughout this portion of the course to create a model for a real world situation - creating a model to predict the success of NBA teams.

A tragedy has happened and the world has lost all copies of Win-Loss data for the 2017-2018 and 2016-2017 seasons for the NBA. All other stats remain, but we can no longer trust the Win and Loss counts anymore. The only solution the world has is to use your advanced analysis skills to predict what those win totals should be; these win predictions will be written into the history books, so we need to be accurate.

This project is to be completed in groups of 3.

Part 1: Collect and Prepare Data

For this model you will need to prepare two sets of data:

• Source Data - this will contain team statistics including wins and losses for the 2015-2016 and going backwards to earlier seasons. Note: This model must not contain any win-like statistics such as losses, winning percentage, Pythagorean wins/losses, margin of victory, SRS, etc.
• Subject Data - this will contain the same columns as your Source Data sheet, except the wins - as we're predicting those.

Source Data - The source for our data is Basketball Reference. 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.

There are many other tables of team stats available on that page that you can add to your model to potentially improve its accuracy. Once you have the base of your sheet made with the Miscellaneous Stats the other sets of statistics can easily be added. Once you've assembled your sheet with the 2015-2016 data, repeat the process for at least 4 previous seasons, so you have 150+ total rows of data on which to build the model.

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 or not.

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 on what you've added to your model. Ensure you double check this as inclusion of any win/loss statistics will ruin the accuracy of your model.

Subject Data - To prepare your subject data you must basically redo the same steps in setting up your Source Data for the two most recent full seasons - 2017-2018 and 2016-2017. Also, delete all values from the wins (W) column and the playoff columns as those are our predicted values.

Creating a Predictive Model -

Once your data is prepared you can begin creating your predictive model. Any and all of the tools we looked at in the course are available to you. You may find that as you proceed in building your model that data needs to be added or removed from your initial worksheet. You may also choose to use other techniques such as normalization and partitioning to create a more accurate model.

As you are going through this process you must take note of what method you are using, what changes you make to the model data, and why you are making those decisions. You will need to present both your model, and the reasoning of why you built it as you did and why it is superior to the alternatives that proved to be less accurate. The process of developing your model is the most important part of this process, so ensure you are making logical improvements and documenting the reasoning and impact.

Creating a Classification Model -

In this step you must create a classification model that uses your source data to predict if teams will be in the playoffs or not. Follow the same process as the prediction model, but use classification tools to split the teams into the two groups.

Comparing the Results -

Create a chart that compares your predicted results for both the prediction and classification to the real results for the two seasons that are being predicted. Calculate the margin of error for your prediction model and the successful classification percentage. Include this in your report.

Attachment:- Assignment File.rar