Crime Data Clustering – Data Mining Project

This project was part of our Data Mining course, where we applied KMeans and Hierarchical Clustering techniques to analyze and group crime data.

Project Overview We explored a crime dataset and used clustering methods to discover hidden patterns and crime categories.

Techniques Used

• KMeans Clustering: Grouped crimes based on features like location, time, and severity
• Hierarchical Clustering: Visualized relationships between data points using dendrograms

Outcomes

• Identified crime clusters and trends
• Gained insight into crime behavior based on similarity
• Hands-on experience in applying unsupervised learning methods

Tools & Libraries

• Python
• pandas, scikit-learn, seaborn, matplotlib, scipy

Files

• clustering_kmeans_hierarchical.ipynb – main analysis notebook
• crime_data.csv – dataset used
• output/ – contains plots and visualizations