This study endeavours to utilize machine learning algorithms in order to prognosticate the elements which contribute toward water potability and construct a prophetic model that can establish if drinking water is fit for human consumption. The Water Potability dataset, consisting of 3276 observations of diverse quality metrics such as pH levels, hardness, TDS content, chloramines concentration, sulfate ratio conductivity measures trihalomethanes level and overall turbidity was employed. This set was divided into training data sets alongside testing ones while undergoing pre-processing with regard towards handling missing variables or outliers present within them. Three separate machine-learning models namely: random forest analysis & decision trees along XGBoost-analysis made use of these segregated datasets during optimization processes until accuracy scores were acceptable via determination using performance evaluation techniques including; precision rates evaluations based on recall statistics indicating how well each method performed relative one another helped determine outcomes here where even F1 score could be evaluated-where Random Forest Analysis ultimately resulted in superior results over other methods boasting an impressive record-breaking 70% success rate! The value stemming from this endeavour lies mainly through improving safety protocols & standards surrounding drinking water thus ensuring effective delivery methodologies geared at safeguarding public health whilst providing efficient alternative treatment options too may arise among those utilizing findings gleaned herein-including Lawmakers/Government officials charged with policy matters relating thereto can incorporate suggestions resulting from it-make best possible policies.

Designing Brain Computer Interfaces (BCIs), for helping patients, needs appropriate datasets which are relevant for the language of the patients. There exists a significant shortage of datasets for Indian languages that can be used for BCI research. Malayalam is a prominent south Indian language spoken by more than 34 million people, yet, there exist no BCI datasets for research. We address this issue by creating a dataset for selected Malayalam words by collecting Electro Encephalograph (EEG) signal samples. Our dataset was created by generating EEG samples using the OpenBCI Cyton device when the commonly used Malayalam words were spoken by a volunteer. The created dataset consists of three major types of data: (i) EEG data for spoken Malayalam words, (ii) EEG data for the spoken English words which were closest to the English translation of the corresponding Malayalam words, and (iii) EEG data for sub-vocal (silent) pronunciation of the Malayalam words. We created the dataset for 26 words where each of these words had been recorded for the above mentioned three types. For each word, 10 EEG samples over 8 channels were recorded. This dataset is useful for developing BCI solutions for patients suffering from neuro-degenerative diseases by developing Machine Learning (ML) classifiers for translating EEG-signals to Malayalam words, vocal or sub-vocal, especially considering the scarcity of datasets available in Indian languages.