In this project, the classification of water quality involves the application of various machine learning algorithms to accurately predict whether water samples are safe for consumption or not. Each classifier brings its unique approach to the ML regression task. Decision trees segment the feature space into regions, revealing the most informative attributes for classification. k-Nearest Neighbors (k-NN) classifies data points based on the majority class among their k-nearest neighbors, making no assumptions about the data distribution. Naive Bayes classifiers use Bayes' theorem to predict class probabilities, assuming feature independence. Support Vector Machines (SVM) find the optimal hyperplane to separate data classes, employing different kernels for non-linear boundaries. Neural Networks consist of interconnected layers of neurons, ideal for capturing complex patterns but requiring careful tuning. By using these classification methods, the project aims to ensure water safety, promoting public health through accurate predictions and thorough analysis.