House Price Prediction

In this study, it is suggested to evaluate the success rate of integrating machine learning methods such as leveraging artificial neural pathways to regression. Regression methods used in this paper include multifactorial linear, Ridge, Random Forest, and Least Minimum Selection Operator. This study also attempts to figure out the main factors that impact housing prices in Malmö, Sweden by examining the associations between several variables. Two datasets� public and local�were used in this analysis. Both of these include housing costs for Malmö, Sweden, and Ames, Iowa in the United States. For evaluating the precision of the prediction, the root square and mean square root error values of the training model are examined. The appropriate data has been separated into two parts, pre-processing methods are implemented and the evaluation is executed. One component shall be used for the training period, and the other after the test phase. Also claimed was a binning methodology that raises the accuracy of the models. This thesis strives to establish that Lasso outperforms different techniques while training on the public dataset. The correlation diagrams show how the variables are connected. Also according to the research, lending, repo rates, to criminal behaviour, and deposits all have a detrimental effect on housing costs. where the year, inflation, and unemployment rate have favourable impacts on the value of real estate.