Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic

Journal of Big Data

Table 9 Machine learning model results

Models		4 Months average in rent and sell operations
Models		Accuracy	AUC	“Recall”	Prec	F1	Kappa	MCC
1	Extreme Gradient Boosting	0.859	0.782	0.350	0.476	0.400	0.322	0.328
2	CatBoost classifier	0.857	0.784	0.324	0.477	0.378	0.300	0.311
3	Light gradient boosting machine	0.835	0.769	0.320	0.452	0.372	0.295	0.302
4	Random forest classifier	0.851	0.779	0.323	0.453	0.370	0.289	0.297
5	Ridge classifier	0.725	0.000	0.584	0.279	0.367	0.223	0.252
6	Linear discriminant analysis	0.725	0.735	0.582	0.279	0.367	0.223	0.251
7	Gradient boosting classifier	0.834	0.767	0.351	0.385	0.364	0.268	0.270
8	Logistic regression	0.723	0.715	0.563	0.272	0.359	0.213	0.238
9	Extra trees classifier	0.851	0.780	0.305	0.455	0.358	0.278	0.287
10	Ada boost classifier	0.791	0.737	0.421	0.311	0.356	0.233	0.237
11	Decision tree classifier	0.787	0.627	0.408	0.303	0.346	0.221	0.225
12	Naive Bayes	0.520	0.666	0.692	0.198	0.293	0.101	0.139
13	K neighbors Classifier	0.671	0.614	0.478	0.209	0.288	0.116	0.133
14	Quadratic discriminant analysis	0.451	0.464	0.713	0.186	0.277	0.076	0.097
15	SVM—linear kernel	0.485	0.000	0.638	0.200	0.237	0.076	0.103