https://github.com/vivekntagline/crime-data

Analyzes crime patterns across different areas, exploring factors such as crime type, weapon usage, demographic influences, and geographic distribution to uncover trends in frequency, correlations, and hotspots.
https://github.com/vivekntagline/crime-data

apache-superset data-analysis eda jupyter-notebook python

Last synced: 11 months ago
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Analyzes crime patterns across different areas, exploring factors such as crime type, weapon usage, demographic influences, and geographic distribution to uncover trends in frequency, correlations, and hotspots.

• Host: GitHub
• URL: https://github.com/vivekntagline/crime-data
• Owner: vivekntagline
• Created: 2025-02-18T09:09:37.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2025-02-18T09:21:58.000Z (11 months ago)
• Last Synced: 2025-02-18T10:29:05.852Z (11 months ago)
• Topics: apache-superset, data-analysis, eda, jupyter-notebook, python
• Language: Jupyter Notebook
• Homepage:
• Size: 1.36 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Crime Data Analysis  

![Crime-Data](https://i.ytimg.com/vi/4ZUXYOTiZsg/maxresdefault.jpg)

This repository contains an exploratory data analysis (EDA) and visualization of crime data. The analysis aims to uncover key crime patterns and correlations, helping to understand crime trends and factors influencing crime rates in different areas.  

## Dataset  

The dataset used for this analysis is sourced from Kaggle. You can access it here: [Crime Data Dataset](https://www.kaggle.com/datasets/ishajangir/crime-data)  

## Contents  

- **Jupyter Notebook**: Contains exploratory data analysis (EDA), including bar charts and other visualizations to highlight trends in the dataset.  

- **Dashboards**: Three dashboards built in Apache Superset to visualize crime trends, correlations, and area-based crime distributions.  

- **Questions & Insights PDF**: A set of questions designed to extract meaningful insights from the data.   

## Key Insights  

- Correlation between crime types and weapon usage, identifying patterns in violent crimes.  

- Crime distribution across different areas, with classification based on urban density, commercial hubs, and residential zones.  

- Analysis of demographic impact on crime trends, including age, sex, and descent.  

- Identification of co-occurring crimes within the same area to highlight crime clusters.  

- Dashboards that provide interactive visualizations for deeper exploration.  

## Usage  

- Open the Jupyter Notebook to explore the EDA and visualizations.  

- Refer to the **Dashboards** for an interactive crime analysis experience in Apache Superset.  

- Use the **Questions & Insights PDF** to guide further investigations.