Data mining methods are used to identify the needs of the customers and this also helps the companies to stay competitive in the global market. Effective data mining process allows the firms to stay in the running in the competitive world by looking into the strength of the company. Data mining process has the capability of analysing financial stability and that in turn helps the companies implicate suitable policy and strategy to achieve competitive advantages. There are several types of data mining process that helps the companies to analysis their needs and allows the companies to evaluate finances. Different the types have their own approaches and advantages and helps companies to assess their finances to make concrete decision. The study has been carried out with the help of suitable methods to secure the success of the study. Qualitative methods, inductive approach and cross-sectional research design has been utilised to accomplish the study. From the assessment it is evident that despite of the advantages there are some issues that has been faced by the companies is mainly due to the large size of the data and lack of expertise of the higher authority.

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: Data mining is a method used for categorizing and predicting the performance of a student and a teacher as well. It helps both student and teacher in developing the teaching method and the entire system. Every student who uses this method get a huge favor from this. This helps students to choose the right career option. Currently, the tendency of providing immense importance to data mining techniques in the educational sector is increasing as these procedures have a huge necessity in bringing efficiency in both learning and teaching procedures. Each and every person is gathering a large amount of data every day, in case these data are not further examined; only the large amount of data will remain. With the latest systems and technologies, people can utilise these data and examine those and benefit from it. The best technique for this issue is the data mining process. Data mining is a method of bringing out the useful and disclosed data and information from big data sets. Educational data mining is a process from which teachers and students get a lot of help. Teachers are able to observe every student’s performance. On the other hand, students can choose a perfect and accurate career option by this process. This process utilises several techniques and methods such as statistics, machine learning, data analysis and data mining. Educational data mining is a method of raw information converting from a huge educational database to meaningful and effective information.

<p>During the outbreak of COVID - 19 in the last 2020, the traditional face - to - face in - campus has shifted to different learning modalities (synchronous, asynchronous, modified asynchronous, etc.)  among basic educational institutions in the hope of continuing learners’ learning experiences amidst the pandemic. New datasets potential for mining and knowledge discovery emerged during this time. In this study, the learner's enrollment and survey form (LESF) from the data repository of a private high school in the Philippines is analyzed and mined. The dataset contains learners' information relating to the present COVID - 19 crisis, which is bound to be utilized for strategic classification of each to student to their corresponding learning modality. The dataset includes major categories of information pertaining to learners' demographic profile, parent/guardian information, household capacity, school proximity and transportaion information, and access to distance learning.  We present in this paper a machine learning (ML) method called multilayer perceptron neural network (MLP NN) for classifying learner’s learning modality as an alternative to the recommended algorithm for learning modalities delivery (ALDM) by the Department of Education (DepEd). The MLP NN model is trained using learners’ LESF information as input features. Prior to model development, Boruta algorithm (BA) is conducted to determine important features for the learning modality classification problem. We further investigate the sensitivities for each feature with respect to each learning modality using partial derivatives method. </p>

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