Q1: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.
Q2: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.
Q3:Prepare a CSV OR ARFF format data file of the data.
Q4:Load the dataset in Weka or if you prefer to use any python tools such as Google Collaborate Labhttps://research.google.com/colaboratory/
Q5:Do a basic preprocessing to the dataset such data cleaning / Data reduction /Normalization (if exist or required) etc.
Q6:Based on dataset run Apriori algorithm with different support and confidence values. Discuss the generated rules.
Q7:Based on your dataset selection, apply SVM data mining algorithm.
Provide the result and accuracies of the algorithms and discuss it with supporting screenshots.
Q8:Based on your selection dataset, Apply the Decision tree data mining algorithm with different parameter setting and record the accuracies.
Q9:Apply the K-mean algorithm on the dataset (for k=4) and study the clusters formed.
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