Provide the nature of your project: Determine the scope of the business problem and objectives.

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

The goal of this project is to obtain in-depth knowledge and experiences in a particular data mining application (for example: Spam Detection, cancer diagnosis, Fraud detection, User segmentation,...) and to share the insights with the rest of the class. 

The project consists of 6 phases, the work should be documented and submitted to the instructor for discussion and approval according to the due date. The tasks for each phase are described below: 

Phase 1: Business Understanding (2 marks) 

Provide the nature of your project: Determine the scope of the business problem and objectives. Describe what your project is about include whether you will be performing data mining tasks, or modifying some other system to incorporate data mining features, etc. It is critical that your problem is well-defined. 

Phase 2: Data understanding and preparation (4 marks)  

Explore and collect data that will help solve the stated business problem. Prepare the data for further modeling procedures. Include the origin of the data set, an overview of the data set organization, attributes of the data, and challenges of the data set you've selected.

Phase 3:Data Mining Task (2 marks)  

Provide the specific tasks you will perform on the data set. Include specific questions you will investigate, and the goals for the tasks. This should be independent of the specific techniques you will use to achieve your goals. 

Phase 4: Methods and Models (supervised data mining techniques) (8 marks, 4 marks for each technique)

In order to achieve the goals, you set in the data mining task section & find valuable and hidden knowledge from data you need to apply supervise learning techniques (two Machine Learning classification techniques: for example, Naive Bayes and k-Nearest Neighbor (k-NN) classifiers.) 

You may use data mining packages (e.g. WEKA). Or implement the data mining algorithms yourself, in any programming language. Make clear in your report what existing software you are using. 

Phase 5: Compare models and assess results (4 marks) 

Compare the efficiency of the techniques used in phase 4, draw conclusions from the data models and assess their validity. Translate the results into a business decision and mention how they help the organization to improve decision-making processes and gain competitive advantage.

Phase 6: The Project Presentation (10 Marks) 

Each group of students should present their work in front of their classmates.

The assessment of the project presentation is based on the following criteria:

• Accuracy of the presentation;

• Presentation skills;  

• Quality of discussion of each student in the group. 

The deliverables of the project are:  

A project report maximum 15 pages in in MS-word format that includes most of the below, plus other material if needed: 

• data description 

• problem definition 

• data preprocessing ■ data mining algorithms used and why 

• evaluation, graphs of experiments, result tables ■ screenshots if the program has an interesting user interface 

• discussion on what was hard to achieve, limitations 

• observations, conclusions 

The assessment of the project report is based on the following criteria:  

• Quality of analysis; ■ Accuracy of concepts/theories used; • General organization of the project; 

• Variety of references; 

• Spelling and grammar. 

Attachments:

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