I have attached a solution form
for the past years but it is divided into two phases.. I want my project to be
solved in the same way.
1-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.
2-The dataset may follow the following requirements (Data
description)
Number of instances: between 300-500
Number of attributes: between 10 to 15
3-Prepare a CSV OR ARFF format data file of the data.
4-Load the dataset in Weka or if you prefer to use any python
tools such as Google Collaborate Lab https://research.google.com/colaboratory/
5-Do a basic preprocessing to the dataset such data cleaning /
Data reduction /Normalization (if exist or required) etc.
6-Based on dataset run Apriori algorithm with different support
and confidence values. Discuss the generated rules.
7-Based on your dataset selection, apply SVM data mining
algorithm.
Provide the result and accuracies of the algorithms and discuss
it with supporting screenshots.
8-Based on your selection dataset, Apply the Decision tree data
mining algorithm with different parameter setting and record the accuracies.
9-Apply the K-mean algorithm on the dataset (for k=4) and study
the clusters formed.
0 comments