تطبيق (K-mean ، DBSCAN) على مجموعة بيانات معينة

1.Apply all required pre-processing steps to make the data ready for both clustering algorithms.

2.Apply both clustering algorithms on the dataset using your choice of number of clusters, epsilon and minPts.

3.For the current clusters generated by k-means and DBSCAN, find the silhouette average for all of them and explain which is best.

4.Run both clustering algorithms again but this time, find the best number of clusters using elbow method (if applicable) and silhouette average (plot the elbow and sillouette average like we saw in the lab)

5.Explain why this is the best clustering number

6.Apply PCA on the original dataset and show the variance ratio for all features (like in we saw in lab) and explain them.

7.After seeing the variance ratio for all features, explain how many dimensions should we use or not use to explain the data.

8.Plot the dataset in 3D (using top 3 important features). Can you visualize how many clusters are there? Explain.