Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non- commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) doi : https://doi.org/10.32628/IJSRCSEIT 173 Analysis and Prediction of Water Quality Data using Machine Learning Approaches and Exploratory Data Analysis Ravindra Changala 1 , A Tharun 2 , A Sai Akshith 3 , K Karthik 4 1 Assistant Professor, IT Department, Guru Nanak Institutions Technical Campus, Hyderabad, India 2,3,4 IT Department, Guru Nanak Institutions Technical Campus, Hyderabad, India Article Info Publication Issue : Volume 8, Issue 6 November-December-2022 Page Number : 188-193 Article History Accepted: 07 Nov 2022 Published: 19 Nov 2022 ABSTRACT Drinking Water Supply (DWS) is one of the most critical and sensitive systems to maintain city operations globally. In Europe, the contradiction between the fast growth of population and obsolete water supply infrastructure is even more prominent. The high standard water quality requirement not only provides convenience for people’s daily life but also challenges the risk response time in the systems. Prevalent water quality regulations are relying on periodic parameter tests. This brings the danger in bacteria broadcast within the testing process which can last for 24-48 hours. In order to cope with these problems, we propose a EDA (Exploratory Data Analysis) model for water quality assessment. This model consists of two dimensions, including water quality parameters and score. Furthermore, we applied this model to predict water quality changes in the DWS system using a Random Forest algorithm using Pycaret. For a case study, we select an industrial water supply system. The preliminary results show that this model can provide high predictions & accuracy i.e., 73.76% for water quality understanding. Keywords: Water Quality Monitoring, Water Quality Assessment, Water Quality Analysis, Chain of Custody. I. INTRODUCTION Water plays a vital role in everyone’s life and is observed everywhere and in every form [1]. In Today’s world, due to climatic changes and pollution the water quality is been affected in areas and various experiments are done to test the quality of water [2]. Due to poor water quality, risk occurs in the industrial areas which damage the whole environment and causes an economical loss [3].The root cause for many diseases such as typhoid, diarrhea, cholera is due to usage of contaminated water caused by increased industrialization and urbanization in India. [4]. According to reports form WHO, it is estimated that about 77 million people affected by contaminated water in India and 21% of diseases are caused due to it.[5] Due to insufficient rainfall and drying up of main reservoirs that supplies water, India faces water crisis frequently, hence making water one of the most precious and limited land resources. Many Organizations including WHO and BIS has framed standards for water parameters