Prediction and Analysis of Crime against Women Data using Machine Learning and Statistical Imputation Techniques
Keywords:
Statistics, Machine Learning, Missing Values Imputation, Fuzzy C-means, K-means, KNNAbstract
Data analytics is a vast research field all around the world. Missing data is a significant difficulty in this discipline, as missing values decrease the algorithm's performance. In research, imputations of missing values are critical; incorrect imputation of absent values leads to erroneous predictions.These types of missing data must be handled efficiently.In this article, statistical-based and Machine Learning based imputation methods are proposed. These values are applied in two different crimes against women data set.There are thirteen types of machine learning algorithms implemented by using these proposed values. Simple linear, multiple linear, KNNr, SVR. Polynomial regression, Decision Tree Regression, and Random forest regressions are used to predict the crime rate against women in India and Salem District in Tamil Nadu. The Novel algorithm KNN_ET has proposed for missing data imputation whose value is compared with other statistical and machine learning-based imputations such as mean, median, mode, K-Means cluster, fuzzy C-Means cluster, K-Medoids and K-nearest neighbor method. The main aim of this work is to reduce errors and improve machine learning accuracy. Finally, the algorithm efficiency is compared based on MSE, MAE, RMSE for Prediction. Proposed algorithm KNN_ET has reduced the maximum number of errors and gives accuracy 94.78 % for Salem District Crime Data-set and 98.7 % accuracy for India level Crime dataset .This is better accuracy compared with all other imputation techniques. In the future, this work will be very helpful to the police department for control the crimes against women in India and Salem.
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