Machine-Learning

1) K-means Clustering (Kmean.ipynb):

Implement K-Means clustering on socio-economic and health data of countries. Explore optimal cluster numbers, visualize results, and interpret cluster characteristics. Analyze how K-Means clusters contribute to determining the overall country development. Provide insights into significant factors driving the clustering outcomes.

2) K-medoids Clustering (kmedoid.ipynb):

Apply K-Medoid clustering to the same socio-economic and health dataset. Compare clustering results with K-Means, highlighting differences and similarities. Discuss the implications of K-Medoid clusters for assessing country development. Identify and interpret key socio-economic and health factors shaping K-Medoid clusters.

3) Linear Regression (Linear_Regression.ipynb):

Use a suitable dataset to implement linear regression. Explore the relationship between variables and interpret the regression coefficients. Evaluate the model's performance using appropriate metrics.

4) Density-Based Clustering (DBscan.ipynb):

Implement the DBSCAN algorithm on a dataset with varying densities. Visualize the clusters and identify outliers. Discuss situations where DBSCAN is advantageous over other clustering methods.

Limitation of Silhouette score:

PCA

Matrix Visualization: https://shad.io/MatVis/