Online ISSN: 2515-8260

Keywords : Linear Regression


A. Rama; S Rajakumari; P. Selvamani

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1833-1839

Water quality prediction play an essential role in aqua environment management. The demand for accurate water quality prediction techniques for efficient water resources management. Currently, the Indian pollution control board has set up various monitoring stations to measure water quality frequently. However, the forecast for water quality is currently not being carried out. In this work, machine learning models have been implemented to predict the indices of water quality. The efficiency of logistic Linear regression and AdaBoostRegressor in the prediction of seven major water quality parameters were evaluated. The Tamil Nadu water quality dataset is used in this analysis. The parameters such as pH value, the quantity of oxygen dissolved, total coli form, B.D.O, electric conductivity, the quantity of phosphorus, and nitrate are considered. The assessed error-index value of the applied models showed that the AdaboostRegressor obtains a lesser error-index and it can consider being a more accurate model than the Linear regression model. The entire methodology proposed here is in the context of water quality is based on numerical analysis. While investigating the outcomes of the implemented machine learning models, it is demonstrated that they have nearly over-estimation properties. The proposed models are assessed using the metrics Mean Square Error and R2 score the results reflect that AdaboostRegressor predicts the (Water Quality Indices) WQI rate with a Mean Square Error value of 0.8, and R2 score rate is 0.41, whereas AdaBoostRegressor with a obtains Mean Square Error (MSE) rate as 0.74 and R2 score rate as 0.44.

Prediction of Population Growth using Machine Learning Techniques

Brintha Rajakumari S; Padmanabhan P; Christy S; Nandhini M

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1885-1890

Population growth prediction shows the future rate of fertility, mortality and migration of people of a country. It is very important for the population and health system. Nowadays, Machine learning concepts are most growing and popular for predicting future values. In order to predict population growth, the machine learning concept applied to build the map between year and population growth. The paper investigates the population growth of Indian government population data using time series forecasting machine learning techniques and analyzed byLinear regression, Support Vector Regression, Multilayer perceptron and Decision tree classifier. The optimum prediction method is based on the technique which gives very less error rate. The increment or degradation of instances in datasets do not affect the performance of the techniques is also analysed. The obtained result shows that the linear regression gives less error than the other classifier to predict population growth of India.