Large amount of data has been generated by Organizations. Different Analytical Tools are being used to handle such kind of data by Data Scientists. There are many tools available for Data processing, Visualisations, P...Large amount of data has been generated by Organizations. Different Analytical Tools are being used to handle such kind of data by Data Scientists. There are many tools available for Data processing, Visualisations, Predictive Analytics and so on. It is important to select a suitable Analytic Tool or Programming Language to carry out the tasks. In this research, two of the most commonly used Programming Languages have been compared and contrasted which are Python and R. To carry out the experiment two data sets have been collected from Kaggle and combined into a single Dataset. This study visualizes the data to generate some useful insights and prepare data for training on Artificial Neural Network by using Python and R language. The scope of this paper is to compare the analytical capabilities of Python and R. An Artificial Neural Network with Multilayer Perceptron has been implemented to predict the severity of accidents. Furthermore, the results have been used to compare and tried to point out which programming language is better for data visualization, data processing, Predictive Analytics, etc.展开更多
Coronary Artery Disease (CAD) is the leading cause of mortality worldwide. It is a complex heart disease that is associated with numerous risk factors and a variety of Symptoms. During the past decade, Coronary Artery...Coronary Artery Disease (CAD) is the leading cause of mortality worldwide. It is a complex heart disease that is associated with numerous risk factors and a variety of Symptoms. During the past decade, Coronary Artery Disease (CAD) has undergone a remarkable evolution. The purpose of this research is to build a prototype system using different Machine Learning Algorithms (models) and compare their performance to identify a suitable model. This paper explores three most commonly used Machine Learning Algorithms named as Logistic Regression, Support Vector Machine and Artificial Neural Network. To conduct this research, a clinical dataset has been used. To evaluate the performance, different evaluation methods have been used such as Confusion Matrix, Stratified K-fold Cross Validation, Accuracy, AUC and ROC. To validate the results, the accuracy and AUC scores have been validated using the K-Fold Cross-validation technique. The dataset contains class imbalance, so the SMOTE Algorithm has been used to balance the dataset and the performance analysis has been carried out on both sets of data. The results show that accuracy scores of all the models have been increased while training the balanced dataset. Overall, Artificial Neural Network has the highest accuracy whereas Logistic Regression has the least accurate among the trained Algorithms.展开更多
文摘Large amount of data has been generated by Organizations. Different Analytical Tools are being used to handle such kind of data by Data Scientists. There are many tools available for Data processing, Visualisations, Predictive Analytics and so on. It is important to select a suitable Analytic Tool or Programming Language to carry out the tasks. In this research, two of the most commonly used Programming Languages have been compared and contrasted which are Python and R. To carry out the experiment two data sets have been collected from Kaggle and combined into a single Dataset. This study visualizes the data to generate some useful insights and prepare data for training on Artificial Neural Network by using Python and R language. The scope of this paper is to compare the analytical capabilities of Python and R. An Artificial Neural Network with Multilayer Perceptron has been implemented to predict the severity of accidents. Furthermore, the results have been used to compare and tried to point out which programming language is better for data visualization, data processing, Predictive Analytics, etc.
文摘Coronary Artery Disease (CAD) is the leading cause of mortality worldwide. It is a complex heart disease that is associated with numerous risk factors and a variety of Symptoms. During the past decade, Coronary Artery Disease (CAD) has undergone a remarkable evolution. The purpose of this research is to build a prototype system using different Machine Learning Algorithms (models) and compare their performance to identify a suitable model. This paper explores three most commonly used Machine Learning Algorithms named as Logistic Regression, Support Vector Machine and Artificial Neural Network. To conduct this research, a clinical dataset has been used. To evaluate the performance, different evaluation methods have been used such as Confusion Matrix, Stratified K-fold Cross Validation, Accuracy, AUC and ROC. To validate the results, the accuracy and AUC scores have been validated using the K-Fold Cross-validation technique. The dataset contains class imbalance, so the SMOTE Algorithm has been used to balance the dataset and the performance analysis has been carried out on both sets of data. The results show that accuracy scores of all the models have been increased while training the balanced dataset. Overall, Artificial Neural Network has the highest accuracy whereas Logistic Regression has the least accurate among the trained Algorithms.
