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College of Computing and Informatics Instructions: This project report must be submitted on Blackboard ( WORD format only ) via the allocated folder. You are advised to make your work clear and well-presented; marks may be reduced for poor presentation Email submission will not be accepted. Late submission will result in ZERO mark. The work should be your own, copying from students or other resources will result in ZERO mark. Use Times New Roman font for all your answers. PROJECT Deadline: Tuesday 05/12/2023 @ 23:59 [Total Mark for this Project is 14] Data Mining and Datawarehouse IT446 Student Details: Name: Fatimah Mubarak AlSubaie Name: Name: Name: ID: 170305956 ID: ID: ID: CRN: 10745

Pg. 1 Question TwoQuestion Two Question One Select one of the datasets from UCI Machine Learning Repositories. (http://archive.ics.uci.edu/ml/ ) OR ( https://www.kaggle.com/datasets ) OR use your own dataset if available from any source. Then write name and link of selected dataset. Note: The dataset may follow the following requirements (Data description) a) Number of instances: between 300-500 165474 b) Number of attributes: between 10 to 15 14 Name of dataset: clickstream data for online shopping Link of dataset: https://archive.ics.uci.edu/dataset/553/clickstream+data+for+online+shopping 1 Marks Learning Outcome :CLO1 Define different data mining tasks, problems and the algorithms most appropriate for addressing them.

Pg. 2 Question TwoQuestion Two Question Two Load the dataset in Weka or if you prefer to use any python tools such as Google Collaborate Lab https://research.google.com/colaboratory/ 0.5 Marks Learning Outcome: CLO3 Employ data mining and data warehousing techniques to solve real-world problems.

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Pg. 3 Question TwoQuestion Two

Pg. 4 Question TwoQuestion Two Question Three Understand and describe the nature and structure of the selected dataset. -A brief description about the dataset: include number of attributes, number of instances, outliers in the dataset. 1 Marks Learning Outcome: CLO3 Employ data mining and data warehousing techniques to solve real-world problems

Pg. 5 Question TwoQuestion Two

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Pg. 6 Question TwoQuestion Two Question Four Provide descriptive statistics for some attributes [at least 2 attributes] using statistical method: (1) Include the measure of central tendency such as the mean, median, and mode. (2) Describe the spread of your data. This may include the measure of variance, standard deviation, skewness, and kurtosis. 1.5 Marks Learning Outcome: CLO2 Demonstrate a wide range of clustering, estimation, prediction, and classification algorithms to solve a specific program or application.

Pg. 7 Question TwoQuestion Two Question Five Do a basic preprocessing to the dataset such data cleaning / Data reduction /Normalization (if exist or required) etc. 2 Marks Learning Outcome(s): CLO2 Demonstrate a wide range of clustering, estimation, prediction, and classification algorithms to solve a specific

Pg. 8 Question TwoQuestion Two Question six Based on dataset run Apriori algorithm with different support and confidence values. Discuss the generated rules. 2 Marks Learning Outcome(s): CLO2 Demonstrate a wide range of clustering, estimation, prediction, and classification algorithms to solve a specific

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Pg. 9 Question TwoQuestion Two Question seven Based on your dataset selection, apply SVM data mining algorithm. Provide the result and accuracies of the algorithms and discuss it with supporting screenshots. 2 Mark Learning Outcome(s): CLO2, CLO4 Demonstrate a wide range of clustering, estimation, prediction, and classification algorithms to solve a specific

Pg. 10 Question TwoQuestion Two Question eight Based on your selection dataset, Apply the Decision tree data mining algorithm with different parameter setting and record the accuracies. 2 Mark Learning Outcome: CLO2 Demonstrate a wide range of clustering, estimation, prediction, and classification algorithms to solve a specific program or application.

Pg. 11 Question TwoQuestion Two Question nine Apply the K-mean algorithm on the dataset (for k=4) and study the clusters formed. 2 Mark Learning Outcome: CLO2 Demonstrate a wide range of clustering, estimation, prediction, and classification algorithms to solve a specific program or application.

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