Online Hostel Management System Using Hybridized Techniques of Random Forest Algorithm and Long Short-Term Memory

Hostel management is a Web application that is created for managing various hostel activities that limit physical labor and makes jobs much easier for all the users of the app. Many universities are using the traditional procedure for storing data. This has a bad effect on the efficiency of the institution. This Web application provides the users a user-friendly GUI that makes all hostel-related activities easier than before. Machine learning is used in every module such as registration, token booking, room allotment and leave form module to make all these processes quicker and support multiple users at the same time. Machine learning facilitates systems to enrich their ability to automatically improve their functionality through learning. These kinds of systems use datasets to learn and train themselves through it. This training does not even require human assistance or intervention. Supervised machine learning techniques involve a set of predefined training dataset to train themselves. Unsupervised machine learning techniques explore datasets to infer structure for unstructured data. This paper focuses on identification of the drawbacks of the existing system which leads to the designing of computerized GUI-oriented system. LSTM is used to enhance the effectiveness of the computerized system. Random forest algorithm is a supervised learning method for tasks such as classification and prediction that involve the construction of multiple decision trees at training time and outputting the class that is the mode of the classes in classification or the mean prediction of the individual trees in regression. The reason that we use random forest algorithm is that it does not overfit data for large datasets which is suitable in our case. It combines the result of one or more decision trees to avoid overfitting. They are flexible and provide results with high accuracy. Scaling of data is not necessary.