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.

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.

This report provides a comprehensive guide to performing clustering analysis using the k-means and DBSCAN algorithms in R. The analysis is applied to the Iris dataset, and several methods for determining the optimal number of clusters are demonstrated, including the Elbow Method, Silhouette Method, and Gap Statistics. The report also features a detailed exploration of density-based clustering using DBSCAN and visualization techniques to interpret clustering results. The code and visualizations make it easy to understand clustering concepts and apply them to other datasets.

Collected images of 250 Birds,cats,dogs,fruits,vegetables this data set contains 10% of my favorite images and performed clustering

This report demonstrates the use of an Artificial Neural Network (ANN) to predict patient survival based on the Haberman’s Survival Dataset. Key steps include exploratory data analysis, feature scaling, data normalization, and building a neural network with two hidden layers. The analysis explores the relationship between patient age, surgery year, and lymph node count in predicting survival outcomes, and presents the final model's predictions and performance.