1. Select any dataset that contains more than 300 observations with at least 10 attributes from https://archive.ics.uci.edu or https://www.kaggle.com or any other online free data repository. Perform
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1. Select any dataset that contains more than 300 observations with at least 10 attributes from https://archive.ics.uci.edu or https://www.kaggle.com or any other online free data repository. Perform detailed analyses on the selected data by using ONE (1) data reduction method and ONE (1) clustering method of your choice. Explain your choices and discuss your results.
NOTES: • The link and the description of the selected dataset should be provided, and the dataset should NOT have been used in the lectures or labs of the course.
• Describe data set information such as number of instances/ features/ attributes/ columns, number of dataset/rows, area/ domain/ field, and/or missing value(s) if any.
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Order Paper Now• Any preprocessing method (e.g. removal or filling of empty cells) performed on the original data needs to be fully described and shown.
• Your analyses shall include the descriptions of your Python codes and plots. [50 marks]
1. Select any dataset that contains more than 300 observations with at least 10 attributes from https://archive.ics.uci.edu or https://www.kaggle.com or any other online free data repository. Perform
1. Select any dataset that contains more than 300 observations with at least 10 attributes from https://archive.ics.uci.edu or https://www.kaggle.com or any other online free data repository. Perform detailed analyses on the selected data by using ONE (1) data reduction method and ONE (1) clustering method of your choice. Explain your choices and discuss your results. NOTES: • The link and the description of the selected dataset should be provided, and the dataset should NOT have been used in the lectures or la bs of the course. • Describe data set information such as number of instances/ features/ attributes/ columns, number of dataset/rows, area/ domain/ field, and/or missing value(s) if any. • Any preprocessing method (e.g. removal or filling of empty cells) p erformed on the original data needs to be fully described and shown. • Your analyses shall include the descriptions of your Python codes and plots. [50 marks]
