|1||Decision tree (J48)||59.15%||Took more time of 0.76 s to build model compared to 0.09 s of other models.||They took J48 naïve Bayesian and ZeroR and compared them by running tests.|||
|2||KNN (K = 5)||66.6939%||Data filling algorithms needs to be added to increase the accuracy.||In their research they try to prove that higher accuracy can be achieved if GBWKNN filling algorithm and KNN classification algorithm is combined.|||
|3||KNN (K = 10)||87.03%||Naïve Bayesian has slightly higher accuracy.||They essentially divided data into critical and non-critical and then compared it in 5 classification algorithms and noted that naïve Bayesian, neural networks, and KNN predict better than the SVM and decision tree.|||
|4||Naïve Bayes classifier||87.00%||Cannot be applied to the dataset having large number of features.||They implemented a novel crime detection naïve Bayes method for crime prediction and analysis.|||
|5||Decision tree||83.9519%||As they are unstable, a small change in data can lead to a large change in the structure.||They showed that decision tree performed better than the naïve Bayesian with the same crime dataset, using WEKA.|||
|6||Naïve Bayes||65.59%||Computational speed, robustness, scalability, and interpretability were not taken into consideration.||This paper presented comparative analysis on the accuracy of k-NN, naive Bayescand decision tree algorithms in predicting crimes and criminal actions.|||
|7||Autoregressive integrated moving average (ARIMA)||
The mean absolute error and standard deviation of the model are|
(1) Test average test standard deviation is 0.0867 ± 0.0293;
(2) Training average training standard deviation is 0.0413 ± 0.0084.
|They have described this model as quite complex compared to others.||This paper is about the cons of fuzzy cognitive maps with respect to time series prediction. The ARIMA uses the auto-correlation parameters.|||
|8||Regression model||They first took 10 crimes per month the expected forecast for absolute percent error (APE) was 42%. When 20 crimes were taken the expected forecast APE was 28%, and at 25 crimes per month the expected forecast APE was 25%. After 30 crimes they 13.5% error.||This research date backs 20 years.||
They aim at predicting crimes 30 days ahead.|
They conduct the experiment in Pittsburgh.
|9||SVM||Over 10 months of experiment its accuracy was 84.37%.||The challenge that they indicated that we may face in the future could be to locate the best point at which spatial knowledge is available.||They compare different model to analyze which has the best chance at predicting hotspots.|||
|10||Random Forrest Regressor||Ninety-seven percent accuracy in predicting crimes||They got this high accuracy in previous recorded crimes to actually predict crimes in real life will be a challenge.||They had divided their data into 2 parts 80% of it was used to train the model and the rest 20% was used to test the model in this the model achieved the score of 90%.|||