This project analyzes OSHA workplace safety data (2023) using Python (Pandas, Matplotlib) to identify key trends in injuries, fatalities, and workplace restrictions across various industries. The goal is to apply data-driven insights to improve workplace safety and regulatory compliance.

• Identify high-risk sectors with the most workplace injuries.
• Analyze fatalities by industry to highlight hazardous work environments.
• Examine the correlation between job restrictions (DJTR) and injuries.
• Provide actionable recommendations based on the findings.

• Source: OSHA Workplace Safety Data (2023)
• Key Columns Used:
  • sector – Industry classification
  • total_injuries – Number of workplace injuries
  • total_deaths – Fatalities recorded
  • total_dafw_cases – Days Away From Work (DAFW) cases
  • total_djtr_cases – Days of Job Transfer/Restriction (DJTR) cases

• Python: Data processing and visualization
• Pandas: Data cleaning and aggregation
• Matplotlib: Graphical representation of safety trends

1. Clone the repository:

   git clone https://github.com/yourusername/workplace-safety-analysis.git
   cd workplace-safety-analysis

2. Install dependencies:

   pip install pandas matplotlib

3. Run the Python script:

   python workplace_safety_analysis.py

4. Review results:
   • The script will generate bar charts and scatter plots showing workplace safety trends.
   • The summary findings will be printed in the console.

• Manufacturing, Healthcare, and Retail Trade have the highest workplace injuries.
• Fatalities are most frequent in Mail & Parcel Delivery, Construction, and Trucking.
• Positive correlation between job restrictions and workplace injuries suggests hazardous work environments.

• Apply predictive analytics to forecast future safety risks.
• Implement stronger workplace safety training for high-risk industries.
• Use compliance monitoring tools to reduce OSHA violations.

• Lisa Krasiuk
• Email: lisakrasiuk@gmail.com
• LinkedIn: Lisa Krasiuk