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DMBI Final Project (Spring 2024)

The objective of this final project is to utilize data mining algorithms on raw datasets to uncover hidden information, facilitating informed managerial decision-making. Groups, consisting of 2 to 5 members, will navigate through the entire data mining process, extracting valuable insights. The final goal is depicted in the attached figure.

Tasks and accountability Contract:

To successfully complete this final project, draft an agreement outlining the responsibilities of each group member. The agreement should include the following table:

All students within the same group will be assigned the same score. However, in instances of significantly disparate contributions, individuals are required to submit a separate accountability contract via email along with a detailed explanation.

Guidelines for Data Mining Project:

Introduction to Dataset (5 points):

1. Introduce the dataset, displaying all attributes and the number of records.

2. Provide data types of attributes if necessary.

3. Summarize missing values and outliers.

4. Offer background information if not common knowledge.

Dataset Preprocessing (5 points):

1. Clean the dataset using preprocessing techniques.

2. Remove irrelevant attributes, assess missing values, and normalize numeric attributes.

3. Conduct data analysis on attributes, identifying redundancy.

4. Create charts during preprocessing for additional points.

Data Mining Algorithms (5 points):

1. Execute data mining algorithms on the cleaned dataset.

2. Select algorithms at your discretion, such as decision tree, KNN, SVM, rule-based classifier, bagging, boosting, k-means, DB scan, hierarchical clustering, and others.

3. Running more than 7 algorithms earns full points.

Data Visualizations (5 points):

1. Explain interesting results through visualizations using tools like Excel, Tableau, Python, R, Java, C, C++, or C#.

2. Visualization of data mining algorithms enhances points.

Presentation (5 points):

1. Deliver a 15-minute presentation showcasing your work.

2. Record yourself in Zoom or Webex and upload the recording to Canvas.

3. Active participation with cameras on enhances points.

Important Note:

This documents serves as a guideline for the final project, where groups are in competition. Achieving an average performance among all groups results in 4 out of 5 points. Only 30% of groups can score more than 20 points.

Submission:

1. Go to Canvas → People → Final project group.

2. Select a group number and place all team members' names under this group.

3. Click on the three dots beside your group number, then go to the group homepage.

4. Visit the files section on your group homepage and upload your PowerPoint, codes, dataset link, and recording.

5. If the recording is too large for Canvas, upload it to Google Drive and share the link via email.