Data Collection
Teams can use any dataset for their final project. Following are two sources of data – UCI Machine Learning Repository
https://archive.ics.uci.edu/ml/datasets.php
Kaggle
https://www.kaggle.com/datasets
Data Analysis: Teams can use any software (e.g., MS Excel, R, Python, IBM SPSS, etc.) to analyze their data. Any analysis method (e.g., regression, classification, time-series analysis, etc.) that is appropriate for the dataset and the problem is acceptable.
Final Project Report: Each team will submit a report that discusses the aspects mentioned below.
Report page size should be Letter (8.5” X 11”) with 1” margins all around; font should be Times New Roman 12 pt. single spaced; and maximum page limit for the report is 8 pages. Tables, graphs, and visualizations should be numbered and near the text that describes them.
Following are some of the aspects each team needs to include in the report –
I. Introduction to the problem: A brief discussion on the specific real-world problem the team is trying to solve.
II. Introduction to the dataset: A brief description of the data source and the data collection process. An explanation of how the dataset relates to the real-world problem. Explanation of any steps taken to prepare the data for analysis.
III. Explanation of the variables: Definitions and discussions on each variable in the dataset.
IV. Analysis method: Description of the method used to analyze the data. Justification of the method used to analyze the data (i.e., how the chosen method solves the real-world problem).
V. Descriptive statistics: Discussion on the descriptive statistics for each variable.
VI. Results of the analysis: Discussion on the results of the analysis.
VII. Decisions/recommendations: What decisions/recommendations can be made based on the results of the analysis. How does the analysis solve the real-world problem?
VIII. Conclusion: Concluding remarks.
