## BAS 474 Final Project

• The purpose of this project is to give students an opportunity to apply some of the data mining algorithms they learned in this course to a dataset of their choosing.
• Five potential datasets are provided for you to select one from.
• Your written report must contain an executive report (brief summary of fifindings),not to exceed three paragraphs, on a separate page (i.e. page 1).
• This project is intended to be completed individually. You are encouraged to work together, but it’s not ok to copy/paste other’s work.

#### The body of the write up should, in no more than 12 pages, cover: 算法分析报告代写

(1) Problem(s) you want to answer using your analysis, i.e. problem statement(s).

(2) Description of the data – use visualizations when possible, and numerical summaries.

(3) Details of any data pre-processing steps that you undertake: transformations, handling of missing data (if any), discritization, etc.

(4) List of methods that you consider, but no need to describe the methods in detail in the report.

(5) What metric will be used to compare the methods that are considered.

(6) Results of your selected methods. Description of the results and which method(s) perform the best and is (are) to be used as a fifinal method.

(7) Interpretation of the results. Focus on the highlights of your analysis results in relation to the problem(s) stated in (1). Report variable importance plots, and explain their effffect when possible.

(9) Appendix containing any references utilized. This appendix is included in the 12 page limit.

A higher level structure of your write-up could be:

(a) Problem statement(s).

(b) Description of the data, and any data pre-processing steps.

(c) Methods considered, and the best method(s).

(d) Summary of (interesting) results.

### Here are the list of datasets: 算法分析报告代写

• HR Analytics: Job Change of Data Scientists: Predict who will move to a new job. https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists
• Real Estate DataSet: Dragon Real Estate – Price Predictor. https://www.kaggle.com/arslanali4343/real-estate-dataset
• Census Income Data Set: Predict whether income exceeds ✩50K/yr based on census data. https://archive.ics.uci.edu/ml/datasets/Census+Income
• Australian Credit Approval: This fifile concerns credit card applications. https://archive.ics.uci.edu/ml/datasets/Statlog+(Australian+Credit+Approval)