In the past years, there has been a lot of interest in water quality and its prediction as there are many pollutants that affect water quality. The techniques provided herein will help us in controlling and reducing the risks of water pollution. In this study, we will discuss concepts related to machine learning models and their applications for water quality classification (WQC). Three machine learning algorithms, J48, Naive Bayes, and multi-layer perceptron (MLP), were used for WQC prediction. The dataset used contains 10 features, and in order to evaluate the machine's algorithms and their performance, some accuracy measurements were used. Our study showed that the proposed models can accurately classify water quality. By analyzing the results, it was found that the MLP algorithm achieved the highest accuracy for WQC prediction as compared to other algorithms.

This study is a preliminary evaluation of the situation of CO2 emissions in Italy, reviewing the international and national literature, using global datasets, and using data mining techniques for analysis and prediction. The study used descriptive methods. It focuses on finding the main potential parameters that effect the concentration of CO2 emissions based on energy resources in Italy. Sequential Minimal Optimization regression (SMOreg), Linear Regression, and Simple Linear Regression are used. Based on the analysis, the Liquid Fuel sector has had the highest rate of increase in CO2 emission 56.8%. R. Linear Regression algorithm gives us a better performance of the prediction for the CO2 emissions than the second algorithm Simple Linear Regression. These results are in line with the present condition in Italy due to the Italian National Program on Climate Change which focuses on reducing carbon dioxide emissions.