https://github.com/r-mahesh45/crime-data-clustering
done clustering using crime and airlines dataset
https://github.com/r-mahesh45/crime-data-clustering
Last synced: 7 months ago
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done clustering using crime and airlines dataset
- Host: GitHub
- URL: https://github.com/r-mahesh45/crime-data-clustering
- Owner: R-Mahesh45
- Created: 2024-03-07T09:45:27.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-04T16:10:08.000Z (9 months ago)
- Last Synced: 2025-01-30T07:16:10.152Z (8 months ago)
- Language: Jupyter Notebook
- Size: 2.93 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Crime Data Clustering
This project performs clustering analysis on U.S. crime data using Hierarchical, K-Means, and DBSCAN algorithms. The aim is to identify the optimal number of clusters and derive meaningful inferences about crime patterns based on features like **Murder**, **Assault**, **Urban Population**, and **Rape**.
## Table of Contents
- [Data Description](#data-description)
- [Clustering Techniques](#clustering-techniques)
- [Hierarchical Clustering](#hierarchical-clustering)
- [K-Means Clustering](#k-means-clustering)
- [DBSCAN Clustering](#dbscan-clustering)
- [Visualizations](#visualizations)
- [Inferences](#inferences)
- [Setup and Usage](#setup-and-usage)
- [Results](#results)
---
## Data Description
- **Murder**: Murder rates in different places of the United States.
- **Assault**: Assault rates in different places of the United States.
- **UrbanPop**: Urban population in different places of the United States.
- **Rape**: Rape rates in different places of the United States.
## Clustering Techniques
### Hierarchical Clustering
- Used **dendrograms** to identify the number of clusters.
- Applied **Agglomerative Clustering** to group similar regions.
### K-Means Clustering
- Used **Elbow Curve** to find the optimal number of clusters.
- Clustered data using K-Means and analyzed intra-cluster similarities.
### DBSCAN Clustering
- Applied **Density-Based Spatial Clustering** for identifying arbitrarily shaped clusters.
- Tuned `eps` and `min_samples` parameters for optimal clustering.
## Visualizations
- Dendrograms for hierarchical clustering.
- Elbow curve for K-Means optimization.
- Scatter plots for cluster visualization.
## Inferences
- Hierarchical and K-Means clustering identified consistent cluster patterns.
- DBSCAN was sensitive to parameter tuning but struggled with noise in the data (silhouette score: -0.268).
- Urban population and crime rates influence clustering significantly.
## Setup and Usage
1. Clone the repository:
```bash
git clone https://github.com/R-Mahesh45/crime-data-clustering.git
```
2. Install required libraries:
```bash
pip install pandas numpy matplotlib seaborn scipy scikit-learn
```
3. Run the clustering analysis:
```bash
python clustering_analysis.py
```
## Results
- **Hierarchical Clustering**: X clusters were formed.
- **K-Means Clustering**: Y clusters were optimal based on the elbow method.
- **DBSCAN Clustering**: Challenging to form clusters due to noise.