Problem & Solution
Manual crime data analysis is time-consuming and often misses patterns that could help prevention efforts.
Automated Processing
Automated data cleaning and preprocessing.
Pattern Recognition
Clustering algorithms for crime pattern analysis.
Interactive Dashboard
Visualizations for data exploration.
Project Facts
Key Features
What I Built
Highlight features that deliver value to users.
Data Upload
Import crime data from various formats.
Clustering Analysis
K-means and hierarchical clustering.
Visualization
Charts, maps, and statistical summaries.
Export Reports
Download analysis results.
Tech Stack
Tools & Technologies
Technologies used to build this solution.
PythonStreamlitPandasScikit-learnPlotlyNumPy
Process
Development Process
- Data collection and understanding crime data structure.
- Built data preprocessing pipeline.
- Implemented clustering algorithms.
- Created interactive Streamlit dashboard.
- Validation and testing with real datasets.
Impact
Results & Impact
- Pattern recognition accuracy improved by ~30-40%.
- Analysis time reduced from hours to minutes.
- Actionable insights for crime prevention.