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Online Data Analytics, RapidMiner, Data Mining, Analysing Business data, Analysis for decision making, Data Analyst, Data analytics tools tutor

2 months ago

Doha, Baladīyat ad Dawḩah, Qatar TeacherOn Full time
data mining a dataset using RapidMiner.

Individual Project Deadline:

*** Mark: 25
Conditions
1) You are intitled 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) You are intitled to use any tool to conduct your data mining exercise. a.

Your data analysis can be doing using a tool like Tableau, PowerBI, SAP Data Analytics, or any other data mining tool you find fit.

Alternatively, you can use python as the main scripting language to process and mine the provided data. b.

A good start would be to visualize the data and a good and easy to use tool for that is Tableau.

Since you are students can download it for free here:
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 also use some of the data I collected over the years here. if you need complete datasets, I can share them. c.

You can find adjacent data here:
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 2 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 4) Alternatively, I prepared a base dataset of Carrefour product pricing, availability, description, pricing, discounts, origin, etc. that I collected over the past few months.

The data is:
a. from three gulf countries. b. organized over different periods of time. you can find this data here. Data of more stores from the same GCC country is available upon request. 5) I also prepared a base dataset of Market product pricing, availability, description, pricing, discounts, origin, etc. that I collected over the past few months.

The data is:
a. from six gulf countries. b. organized over different periods of time. you can find this data here. One hot topic these days is inflation and supply chain disruption.

The dataset I provided can be mined to evaluate inflation overtime, understand what sectors, countries, products, categories realize the highest inflation.

So, insights supply chain disruption can be mined.

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:
Bachelors/Undergraduate

Gender Preference:
None

Meeting options:
Available online - via skype etc.

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