This document performs clustering analysis on the Iris dataset using the K-means clustering algorithm. We'll focus only on the numerical columns for our analysis.

This analysis demonstrates the application of Random Forest classification to predict diamond prices using the built-in diamonds dataset in R. We'll classify diamonds as "expensive" or "not expensive" based on their features, providing insights into what characteristics most influence a diamond's value.

The code begins by loading the Haberman’s Survival dataset, which contains information about breast cancer patients’ survival rates. It then preprocesses the data by normalizing the numerical features (age, year of operation, and number of positive lymph nodes) to a 0-1 scale, which helps in training the neural network. The survival status is converted to a categorical variable. Next, the data is split into training (70%) and testing (30%) sets to allow for model evaluation. An Artificial Neural Network (ANN) is then constructed with one hidden layer containing three neurons. This ANN is trained on the training data to learn the relationship between the input features and patient survival. After training, the model makes predictions on the test set. The code includes a prediction function that converts the network’s output to binary classifications. Finally, the model’s performance is evaluated by calculating its accuracy and plotting a visual representation of the neural network’s structure.