This project analyzes gun violence data to uncover trends, correlations, and insights that can help inform policy decisions and public awareness. The analysis is performed using various data science techniques and visualizations.
- Data Cleaning: Removal of missing or inconsistent data to ensure accurate analysis.
- Exploratory Data Analysis (EDA): Initial analysis to find patterns, anomalies, and hypothesis generation.
- Data Visualization: Graphs and charts to visualize key trends in gun violence.
- Python: The main programming language used.
- Pandas & NumPy: For data manipulation and analysis.
- Matplotlib & Seaborn: For creating visualizations.
- Jupyter Notebook: For running and sharing code.
- Dalila Solis: Project Lead
- Zach Yuen
- Aditya Kumar
- Andrew Phan
- Ricardo Aguilar