This article focuses on the importance of the continuous collection of water parameters data from the sensors and also the prediction of water quality using the latest different Machine learning algorithms like Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, K-nearest Neighbour, XGBoost, Gradient Boosting and Naive Bayes. These Machine learning models are implemented and tested to validate and achieve a satisfactory result of water quality prediction in terms of different attributes like pH, hardness, Solids, Chloramines, Sulfate, Conductivity, organic carbon, trihalomethanes, Turbidity and potability.

In this Research Paper, we have aimed to Analyse and Predict Parkinson's Disease using State-Of-The-Art Machine Learning Algorithms. We have implemented all the necessary and important Data Pre-processing techniques to achieve the highest accuracy possible in correct diagnosis of the disease.