摘要
Machine learning implementations are being done in a long way in science and technology and especially in medical stream. In this article, we are focusing on machine learning implementation on mall customers and based on their income and how they can invest in the purchase in a mall. This explains the features like Customer ID, gender, age, income, and spending score. There, we mentioned a score in purchasing the goods in the mall. In this scenario, we are implementing clustering mechanisms, and here we apply the dataset of mall customers which is a public dataset and create clusters related to the customer purchase. We implement machine learning models for the prediction of whether the visited customer will purchase any product or not. For this kind of works, we require many of the inputs like the features mentioned in the paper. To maintain the features, we require a model with machine learning capability. We are performing K-Means clustering and Hierarchical clustering mechanisms, and finally, we implement a confusion matrix to achieve and identify the highest accuracy in those two algorithms. Here, we consider machine learning mechanisms to predict the category of the customer about whether they can buy a product or not based on the independent variables. This work presents you a simple machine learning prediction model based on which we can predict the category of the customer based on clustering. Before clustering, we don’t know to what group they belong to. But after clustering, we can identify the category that data node belongs to. In this article, we are mentioning the process of determining the employee based information using machine learning clustering mechanisms.
Machine learning implementations are being done in a long way in science and technology and especially in medical stream. In this article, we are focusing on machine learning implementation on mall customers and based on their income and how they can invest in the purchase in a mall. This explains the features like Customer ID, gender, age, income, and spending score. There, we mentioned a score in purchasing the goods in the mall. In this scenario, we are implementing clustering mechanisms, and here we apply the dataset of mall customers which is a public dataset and create clusters related to the customer purchase. We implement machine learning models for the prediction of whether the visited customer will purchase any product or not. For this kind of works, we require many of the inputs like the features mentioned in the paper. To maintain the features, we require a model with machine learning capability. We are performing K-Means clustering and Hierarchical clustering mechanisms, and finally, we implement a confusion matrix to achieve and identify the highest accuracy in those two algorithms. Here, we consider machine learning mechanisms to predict the category of the customer about whether they can buy a product or not based on the independent variables. This work presents you a simple machine learning prediction model based on which we can predict the category of the customer based on clustering. Before clustering, we don’t know to what group they belong to. But after clustering, we can identify the category that data node belongs to. In this article, we are mentioning the process of determining the employee based information using machine learning clustering mechanisms.
