https://github.com/tanay-dwivedi/police-dataset-data-analysis

The project conducts comprehensive data analysis on a police dataset to uncover patterns, biases, and insights in law enforcement practices, aiding in policy formulation and enhancing transparency.
https://github.com/tanay-dwivedi/police-dataset-data-analysis

dataanalysis matplotlib-pyplot plotly police-data python seaborn visualization

Last synced: 12 days ago
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The project conducts comprehensive data analysis on a police dataset to uncover patterns, biases, and insights in law enforcement practices, aiding in policy formulation and enhancing transparency.

• Host: GitHub
• URL: https://github.com/tanay-dwivedi/police-dataset-data-analysis
• Owner: Tanay-Dwivedi
• Created: 2024-03-09T18:05:12.000Z (8 months ago)
• Default Branch: master
• Last Pushed: 2024-03-14T15:00:59.000Z (8 months ago)
• Last Synced: 2024-03-14T17:37:05.809Z (8 months ago)
• Topics: dataanalysis, matplotlib-pyplot, plotly, police-data, python, seaborn, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 1.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Police Dataset Data Analysis

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## Problem Statement

The project aims to analyze a **police dataset** comprising various attributes such as **driver_gender**, **driver_dob**, **driver_age**, **driver_race**, **violation_raw**, **violation**, **search_conducted**, **stop_outcome**, **is_arrested**, **stop_duration**, and **drugs_related_stop**. The objective is to conduct comprehensive **data analysis** and visualize key insights through six distinct graphs using **Matplotlib**, **Seaborn**, and **Plotly** libraries.

The analysis encompasses various aspects such as demographic patterns, enforcement trends, and potential biases within the dataset, facilitating deeper understanding and insights into law enforcement practices.

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## Identify the Data

[Dataset](https://github.com/Tanay-Dwivedi/Police-Dataset-Data-Analysis/blob/master/police.csv)

The dataset comprises **demographic** and **enforcement-related** attributes, including driver gender, age, race, violation type, stop outcome, and search conduct. Each entry provides insights into police stops, highlighting patterns in law enforcement actions and demographic disparities.

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## Aim of the analysis

1. **Exploratory Data Analysis (EDA):** Understand patterns and trends in police stops by examining demographic distributions, violation types, and stop outcomes.

2. **Insight Generation:** Identify potential biases or disparities in law enforcement practices based on gender, race, or age through statistical analysis and visualization.

3. **Inform Policy and Decision Making:** Provide actionable insights to policymakers and law enforcement agencies for improving fairness, transparency, and accountability in traffic enforcement procedures.

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