This report explores the application of Principal Component Analysis (PCA) on the mtcars dataset, which contains various attributes of car models from the 1970s. The analysis aims to reduce dimensionality while preserving variance, identifying key components that capture the most significant features of the dataset.

R for Data Science Workbook

Clustering on the USArrests dataset, applying different methods such as k-means clustering and hierarchical clustering, also visualize the results using PCA and dendrograms to compare the clustering outcomes.

This analysis explores clustering techniques applied to the USArrests dataset, which includes arrest data per 100,000 residents for various crime categories across U.S. states. We utilize K-Means and hierarchical clustering methods to identify four distinct clusters after standardizing the data. Visualizations, including PCA plots and dendrograms, illustrate the clustering results. Additionally, we compute the Davies-Bouldin Index, which yielded a score of 1.057, indicating satisfactory cluster separation. This study highlights the effectiveness of clustering methods in revealing patterns in complex datasets.

Image classification of animals, fruits, birds, flowers , etc