🎓 Student Admission Records Analysis
This project explores the Student Admission Records dataset sourced from Kaggle, focusing on understanding how high school grades and admission test scores relate to final academic performance.
Current Status: Initial analysis completed using Microsoft Excel Planned Enhancements: Will integrate Python and Power BI for advanced data visualization and deeper insights
Dataset Overview
Source: Kaggle - Student Admission Records(https://www.kaggle.com/datasets/zeeshier/student-admission-records)
Rows: 148 after cleaning
Features:
Name, Age, Gender
High School Percentage
Admission Test Percentage
Grade, Status
Objectives
Understand how different factors affect student performance
Identify correlations between:
High school performance
Admission test results
Final grades
Lay the groundwork for predictive modeling using Python in future updates
Tools Used
Stage
Tool
Data Cleaning
Microsoft Excel
Initial Charts
Excel (PivotTables, Bar Charts)
Planned
Python (Pandas, Seaborn), Power BI
Key Insights (Excel-based)
Students with high admission test scores generally performed better academically
A trend shows that high school percentage also correlates with success
Minor differences in performance by gender observed
range between aceepted and rejected students.
sum of admission test score
Note: These insights are based on Excel visual analysis. More accurate statistical models will be added later.