● This project Clusters the students into different groups on the basis of features fed to our model.
● Features include Scholar_Id, Gender, Monthly Expenditure, Mother tongue, Hobbies, Ambitions, Music genres,
Addictions, etc.
● K- Means and DBSCAN Clustering techniques are used to cluster the students..
Result Obtained After DBSCAN*
Estimated number of clusters: 3
Estimated number of noise points: 70
Homogeneity: 0.788
Completeness: 0.627
V-measure: 0.698
Adjusted Rand Index: 0.658
Adjusted Mutual Information: 0.624
Silhouette Coefficient: 0.435
Result Obtained After K-Means Clustering*
