Introduction | 1. Installing and Loading the Package | 2. Built-in Datasets | Flower Classification | Logic Gate Datasets | AND Gate Dataset | OR Gate Dataset | XOR Gate Dataset | Vehicle Classification | Extended Vehicle Classification | Regression Test | 3. Algorithm Implementations | K-Nearest Neighbors (KNN) | Decision Trees | Perceptron | Regression Analysis

Installation | Available algorithms | K-Means Clustering | Agglomerative Hierarchical Clustering | DBSCAN | Gaussian Mixture Models | Genetic K-Means | Correlation Clustering | Distances

Usage Examples:

Datasets | Auxiliary functions | Main outlier detection methods | Box and Whiskers (box_and_whiskers()) | DBSCAN (DBSCAN_method()) | KNN (knn()) | LOF simplified (lof()) | Mahalanobis Method (mahalanobis_method()) | Z-score method (z_score_method()) | [Previous content remains the same until after the main methods section] | Comparing Methods | Multivariate Methods Comparison | Univariate Methods Comparison