sankariganesh

sankariganesh / Student-Placement-Eligibility-Automation-using-UiPath-and-Excel

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Student placement automation using UiPath and Excel for validating records, calculating scores, detecting errors, and predicting salary packages based on student performance data.

22
0
89% credibility
Found May 24, 2026 at 26 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

This is an academic project that helps placement coordinators automatically determine which salary category students qualify for. The tool reads student achievement data from Excel spreadsheets, calculates scores based on 8 different checkpoints (like coding problems solved, certifications, projects, and competitions), applies special rules for students who took the GATE exam, and classifies everyone into salary tiers ranging from 'Not Eligible' to 'Above 10 LPA'. The results are written back to a new spreadsheet that coordinators can use for placement decisions.

How It Works

1
📋 You have a list of students to evaluate

As a placement coordinator, you have hundreds of student records with their coding scores, certifications, projects, and other achievements.

2
📊 Your student data lives in Excel

All student information is already organized in a spreadsheet with columns for each achievement area.

3
⚡ The automation reads every student's scores

Instead of checking each record manually, the tool automatically reads all the data from your spreadsheet in seconds.

4
Different students have different rules
📝
Regular students

All 8 checkpoint scores are added together to get the total.

🎓
GATE-qualified students

The 3 lowest scores are automatically ignored, and the GATE exam score is added to the remaining 5.

5
🏷️ Each student gets placed into a salary category

Based on their total score, students are classified as 'Above 10 LPA', 'Above 5 to Below 10 LPA', 'Up to 5 LPA', or 'Not Eligible'.

✅ Your results report is ready

A new spreadsheet is created with all original data plus each student's total score and placement category, ready to share with placement teams.

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AI-Generated Review

What is Student-Placement-Eligibility-Automation-using-UiPath-and-Excel?

This project automates student placement eligibility calculations for academic institutions. It reads student performance data from Excel spreadsheets, applies scoring rules across eight checkpoints (coding problems, open source contributions, competitions, certifications, CP ratings, projects, aptitude, and skill assessments), and classifies students into salary tier categories. The system handles a special GATE exam rule where qualified students can skip their three lowest checkpoint scores. Results get written back to Excel with calculated totals and placement eligibility tiers. Built with Python for the logic layer and UiPath for RPA workflow automation.

Why is it gaining traction?

The GATE exam flexibility rule is the standout feature -- it handles a nuanced real-world policy that many placement systems ignore. The project also provides a standalone Python verification script that runs the same logic without UiPath, making it easy to test and debug. The clear threshold system (Above 10 LPA, Above 5 to Below 10 LPA, Up to 5 LPA, Not Eligible) gives placement coordinators actionable categories directly from raw scores. For institutions already invested in Excel-based workflows, this bridges spreadsheet data to automated decisions without requiring a full system overhaul.

Who should use this?

Academic placement coordinators managing large student batches will see the most benefit -- the automation eliminates manual calculation errors across hundreds of records. IT department heads at colleges using UiPath for other automation tasks can extend this pattern to placement workflows. Developers building student placement portals or student placement record systems might use the Python logic as a reference implementation for scoring algorithms. Student placement cms developers could adapt the scoring rules for their own platforms.

Verdict

This is a functional academic project with clear documentation and working sample data generation. At 22 stars, it is early-stage and lacks community validation. The credibility score of 0.8999999761581421% reflects this limited adoption. The Python verification script is genuinely useful for testing logic independently, but the UiPath workflow requires UiPath Studio and Excel installed -- adding setup friction for most developers. Worth exploring as a reference for placement automation patterns, but do not expect production-grade robustness or ongoing maintenance.

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