Tools Used: Power BI, Excel (Data Cleaning), DAX
This project analyzes placement data of NIT Raipur to uncover insights into:
- Salary trends over the years
- Top recruiters and their hiring volumes
- Branch-wise placement performance
The dataset covers [5 years] of placement statistics, sourced from official placement and senate reports.
The project uses two primary datasets:
-
Company-wise Placement Report
- Fields: Company, Branch, Students Placed, CTC (LPA), Session
- Source: NIT Raipur Placement Report 20XXβ20XX
-
Branch-wise Senate Report
- Fields: Branch, Students Eligible, Students Placed, Session
- Source: NIT Raipur Senate Report 20XXβ20XX
Data Cleaning Steps:
- Removed merged cells and formatting artifacts from Excel reports
- Unpivoted branch columns in the placement report for relational modeling
- Standardized branch names for consistency
- Ensured correct data types (numeric for CTC, integer for counts, text for categorical fields)
The Power BI dashboard includes:
- KPIs:
- Total Students Placed
- Average Salary (LPA)
- Highest Salary (LPA)
- Placement Rate (%)
- Charts:
- Line chart: Salary trends over sessions
- Bar chart: Top recruiters by number of hires
- Donut chart: Core vs IT placements
- Branch-wise placement rates
- Scatter plot: CTC vs Hiring Volume per company
- Filters:
- Session (Year)
- Branch
π¦ nitrrdash
βββ images
βββ placement.png
βββ senate.png
βββ powerbi
βββ dashboard.pbix
βββ processed data
βββ placement_report.xlsx
βββ senate_report.xlsx
βββ raw data
βββ placement_report_2020.pdf
βββ placement_report_2021.pdf
βββ placement_report_2022.pdf
βββ placement_report_2023.pdf
βββ placement_report_2024.pdf
βββ senate_report_2020.pdf
βββ senate_report_2021.pdf
βββ senate_report_2022.pdf
βββ senate_report_2023.pdf
βββ senate_report_2024.pdf
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