Doha, Qatar
TeacherOn.com
Part time
1) You are entitled to use any datasets you find fit for this individual project. The main requirements are:
a. The dataset was not mined before to achieve the same (or similar) results.
b. The purpose of the data mining exercise must be related to solving business problems or
finding business related insights.
2) Use Rapid Miner tool to conduct your data mining exercise.
Objectives
1) The approach you can take could be guided or unguided. With a guided approach, you have a question (or set of questions) in mind that you want answers for. Different techniques can be adopted including but not exclusive of:
a. Pattern finding
b. Classification or clustering
c. Prediction (pricing, availability, promotion)
d. Association
e. Regression
2) Remember, it’s all about finding insights. The more mining and manipulating of the data you do, the more likely you’ll find useful insights.
3) It is very much recommended to integrate different but related datasets into your analysis to get more superior insights.
a. For example, you can analyse sales data and get some insights, but if you integrate sales
data with a) economic data, b) population data, c) weather data, d) etc. you’ll be able to find better insights. A usual (but not only) way to integrate data is based on datetime fields.
b. You can find adjacent data from:
i) The home of the U.S. Government’s open data
ii) U.S. Census Bureau
iii) Board of Governors of the Federal Reserve System
iv) FRED Economic Data | ST.LOUIS
v) World Bank Open Data
vi) Kaggle
vii) Qatar Planning and Statistics Authority viii)Qatar Open Data
ix) Google Dataset Search
Submission
Follow the Cross Industry Standard Process for Data Mining (CRISP-DM) process described to you in class (see figure below). you will need to submit a report that includes the following sections:
1. Business Objective: Describe the business questions you intend to address. In this section you must present an understanding of the business based on the research you conduct on the subject under investigation.
2. Data Requirements: describe the data at hand and identify the attributes you deem important.
3. Data preparation: explain in details the plan you executed to prepare the data and ensure its validity, completeness, and consistent. The cleaned-up file must be submitted with the report.
4. Specify the Model: describe the method you adopted to extract the knowledge from the data. This section should explain the range of statistical techniques that you ran to reach the outputs. The script or model file development must be submitted with the report.
5. Justify the model: Explain how the results were validated. Provide the methods and the results of your validation techniques.
6. Recommendation: Explain how the model and associated results address the business objectives.
Level: Masters/Postgraduate
Gender Preference: None
Meeting options:
Available online – via skype etc.
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