Using Machine Learning Techniques to Enhance the Efficiency of Management Information Systems

Organizations have become increasingly large and business operations more complex, resulting in a number of challenges for traditional Management Information Systems (MIS). For effective managerial decision making, Intelligent Systems that can handle huge amounts of data and see patterns, trends and insights in the data are crucial for modern organizations. In this regard the Machine Learning techniques have been found to be very useful in improving the efficiency and analysis capability of Management Information Systems.

The aim of this research is to see how Machine Learning can be used to enhance the performance of MIS by predicting customer churn. The study seeks to build predictive models to detect customers at risk of attrition from an organization. An accurate churn prediction model enables businesses to act proactively in order to prevent customers from churning, to prevent wasted monetary resources, and to optimize business operations.

A number of Machine Learning algorithms implemented in the study were Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). The experimental steps involved in the process included data preprocessing, a feature selection technique, feature scaling and dataset balancing with the Synthetic Minority Oversampling Technique (SMOTE). Standard classification metrics (accuracy, precision, recall, and F1-score) were used to test the models.

After optimizing and pre-processing the best model with respect to overall predictive performance according to the results found was Logistic Regression. The optimized model achieved a precision of 98%, and was extremely accurate in both recall and f1-score, indicating very good performance on identifying the churn customers.

The results also emphasized the need to solve the class imbalance issue and use the right preprocessing methods to enhance the performance of Machine Learning in business datasets.