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:

1. Company-wise Placement Report

   • Fields: Company, Branch, Students Placed, CTC (LPA), Session
   • Source: NIT Raipur Placement Report 20XX–20XX

2. Branch-wise Senate Report

   • Fields: Branch, Students Eligible, Students Placed, Session
   • Source: NIT Raipur Senate Report 20XX–20XX

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

📦 nitrrdash
├── images
  ├── placement.png
  ├── senate.png
├── powerbi
  ├── dashboard.pbix
├── processed data
  ├── placement_report.xlsx
  ├── senate_report.xlsx
├── raw data
  ├── placement_report_2020.pdf
  ├── placement_report_2021.pdf
  ├── placement_report_2022.pdf
  ├── placement_report_2023.pdf
  ├── placement_report_2024.pdf
  ├── senate_report_2020.pdf
  ├── senate_report_2021.pdf
  ├── senate_report_2022.pdf
  ├── senate_report_2023.pdf
  ├── senate_report_2024.pdf

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