Project 1: Human Capital Analysis Overview: This project focuses on analyzing human capital data to help organizations make informed decisions related to hiring, employee retention, and workforce planning. Using a structured approach, I explored key HR metrics to identify trends in recruitment, turnover, employee distribution, and skill alignment.

Objective: To use data-driven methods to uncover patterns and insights that support effective human resource strategies, reduce attrition, and improve talent acquisition and retention efforts.

Key Analyses Performed:

Workforce demographics and distribution

Department-wise hiring and exit trends

Average tenure and turnover rate analysis

Identification of skill gaps by role or function

Forecasting hiring needs based on historical patterns

Tools & Technologies Used:

R Programming: Data wrangling, visualization, and statistical analysis using dplyr, ggplot2, and forecast.

Excel: Preliminary data cleaning and structure.

Tableau/Power BI (optional, if used): For interactive dashboards and KPI monitoring.

Outcomes:

Visual dashboards that highlight HR trends and red flags.

Actionable recommendations for optimizing recruitment strategy.

Predictive insights into future hiring needs and attrition risks.

Files Included:

📄 Detailed Project Report: A comprehensive PDF outlining objectives, methodology, visual insights, and strategic recommendations.

💻 R Code Files: Complete and well-commented scripts for data preprocessing, analysis, and visualization.

Why This Matters: Understanding human capital is critical for organizations aiming to stay competitive. This project bridges data analysis with strategic HR decision-making by offering a clear view of workforce dynamics and enabling data-backed actions.