UNIVERSITY ADMISSION 2024

The Excel File provides a detailed overview of applicant profiles for the 2024 academic cycle. It serves as a central repository for tracking student demographics, academic performance, and administrative status.

Drive File: https://drive.google.com/drive/folders/14vd2SAb_r31uCOrIPN8QJPdJ-pMuEsHK?usp=sharing

The powerbi file serves as the foundational data source for this project, containing a comprehensive repository of applicant information for the 2024 academic cycle including student demographics, home districts, and academic performance metrics like SSC and HSC GPAs. This raw data is transformed into actionable insights through the dashboards captured in the images which provide a visual narrative of the admission landscape. Specifically, the images offer a high-level overview of regional applicant density, academic group distributions, and real-time payment statuses, complemented by a geographical map of Bangladesh to track the university's physical reach. Supplementing this image utilizes a decomposition tree and ribbon charts to analyze the operational flow of applications, breaking down the student journey from payment verification to specific unit selection. Together, these three components provide a transparent and data-driven overview of the 2024 admission process, making it easy to identify key trends and departmental demand.

Drive File: https://drive.google.com/drive/folders/1i3mujbHE7Cpk0_NtoBIJhGtbO0aBSsuQ?usp=sharing

The project integrates data from the excel file with advanced machine learning analytics to provide a comprehensive view of the admission cycle. This dataset, which tracks applicant credentials and demographics, was processed through the BigML platform to uncover deep structural patterns and predictive trends, as illustrated in the provided screenshots. Specifically, the first 3 screenshots display the initial data exploration phase, showcasing field summaries, distributions, and statistical histograms for variables like GPA and home districts. The analysis then moves into predictive modeling, where the fourth and the fifth present complex decision trees that map out the logical paths influencing admission outcomes based on applicant attributes. To evaluate these models, the sixth screenshot provides a Model Summary Report highlighting feature importance, while the seventh and the eighth demonstrate the final predictive capabilities, including probability distributions and real-time prediction interfaces. Together, these elements transform raw spreadsheet data into a sophisticated analytical framework for understanding and forecasting 2024 university admission trends. 

Drive Link: https://drive.google.com/drive/folders/1GPupHLQ68APeRAzNnWGxDs4Rh1-MFCRZ?usp=sharing

The following description integrates the data analysis workflow from the excel file with the specialized visual analytics showcased in the Orange data mining environment. This segment of the project utilizes the excel file to perform advanced statistical comparisons and relationship mapping, as illustrated in the provided screenshots. In the third screenshot, the visual programming workflow in Orange is shown, where data from the primary file is funneled into various widgets like Box Plots and Venn Diagrams for exploratory analysis. The first screenshot highlights this process by displaying a comparative box plot analysis of HSC GPA across different academic groups (Commerce, Science, and Arts), providing a clear statistical visualization of academic performance trends. Furthermore, the second screenshot uses a mosaic display to cross-reference payment status with academic groups, while venn diagram offers a set-based view of overlapping applicant characteristics. Together, these tools transform the raw information in the excel file into a sophisticated visual framework that identifies key correlations and student segments within the 2024 admission cycle. 

Drive Link: https://drive.google.com/drive/folders/1PhnE19m-GOmaU5g6dM5dMTGE2PRnvYn5?usp=sharing

The project shows through rigorous statistical analysis using IBM SPSS, as documented in the provided screenshots. In the screenshots, the raw data from the Excel file is imported into the SPSS Data Editor, where variables such as Applicant Name, HSC Roll, Home District, and Payment Status are organized for formal testing. This structured environment allows for deeper academic inquiry, as seen in the second screenshot, which displays a One-Sample T-Test performed on SSC GPA scores to determine if the applicant pool meets specific academic benchmarks. Additionally, the third screenshot showcases an Independent Samples T-Test used to compare HSC GPA averages between different groups, such as payment status categories, providing statistical validation for trends observed in the earlier dashboards. By integrating these SPSS outputs, the project moves beyond simple visualization to provide high-confidence statistical evidence regarding the 2024 applicant data.

Drive Link: https://drive.google.com/drive/folders/19FPCsu9IVMOodcyb-FOLu2tOR3zjAkc7?usp=sharing

This project shows in a functional application prototype designed to streamline university admissions, utilizing the data structured in the excel file. As shown in the image,, the prototype transforms raw data into a user-centric mobile interface, beginning with a secure Login screen for the University Admission Portal. The application features a comprehensive administrative dashboard that displays real-time key metrics, such as average SSC/HSC GPAs, applicant volume by group, and payment status distributions. Furthermore, the prototype includes specialized views for managing student records, where administrators can see a ranked list of applicants with their specific credentials-including Unit, Quota status, and Payment verification-alongside interactive options to approve or reject candidates. Individual student profiles are also accessible, pulling detailed fields directly from the excel fole to show specific roll numbers, application dates, and academic history. By integrating the analytical insights from previous stages into this mobile prototype, the project provides an end-to-end solution for modernizing and managing the 2024 admission cycle efficiently.

Drive Link: https://drive.google.com/drive/folders/1JuEoJ8r4BvR-20Qvep5Y14S_Mvn75_4M?usp=sharing