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https://github.com/manjit-baishya-datascience/nypd-hate-crime-eda

This project analyzes hate crimes reported in the NYPD using data from a Kaggle dataset. It explores temporal and spatial patterns, offense types, bias motivations, and arrest trends. The analysis involves data cleaning, preprocessing, and visualization to reveal insights into crime trends and geographical concentrations.
https://github.com/manjit-baishya-datascience/nypd-hate-crime-eda

crime-analysis crime-data data-analysis-project eda nypd-dataset python

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This project analyzes hate crimes reported in the NYPD using data from a Kaggle dataset. It explores temporal and spatial patterns, offense types, bias motivations, and arrest trends. The analysis involves data cleaning, preprocessing, and visualization to reveal insights into crime trends and geographical concentrations.

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# **NYPD Hate Crime Analysis**

![image](https://github.com/manjit-baishya-datascience/NYPD-Hate-Crime-EDA/assets/127611924/d0e4f61b-3322-44cb-8722-20fac9bda2d2)



This project is an analysis of the crimes in **NYPD** over various factors. The data has been sourced from [**this**](https://www.kaggle.com/datasets/manjitbaishya001/nypd-hate-crimes) Kaggle dataset and the corresponding [**Kaggle**](https://www.kaggle.com/code/manjitbaishya001/nypd-hate-crime-eda) notebook has been also linked.



## **About the Dataset**

The dataset has not been cleaned before hand. It has been originally sourced from [**data.gov**](https://catalog.data.gov/dataset/nypd-hate-crimes). It provides a list of **Hate Crimes** conducted by people in **NYPD.** It comprises of `13` features. Some of them are:

- **Full Complaint ID:** Provides a unique complaint ID

- **Complaint Year Number:** Year of complaint

- **Month Number:** Month of complaint

- **Record Create Date:** Date of record creation

- **Complaint Precinct Code:** Code of complaint precinct code



NYPD hate crime data analysis delves into uncovering trends in the data, both in terms of temporal patterns, like changes over time, and geospatial distribution, identifying areas with higher concentrations of hate crimes. Examining the details of the data, including offense types, associated law codes, bias motivations behind the crimes, and arrest trends, provides a deeper understanding of the nature of these incidents and how law enforcement responds.



## **Contents**

- [**Info**](#info)

- [**Statement of Work**](#statement-of-work)

- [**Data Analysis**](#data-analysis)



## **Info**

`Author:` Manjit Baishya 


`Start Date:` 06/03/2024 


`Project Status:` Completed


`End Date:` 08/08/2024 


`Last Update:` 08/03/2024



## **Statement of Work**

This project is dedicated to determine the key points responsible for crimes in `NYPD`. Analysis has been done through various measures - including categorical, temporal and spatial points. It has been accompanied with visualizations wherever necessary.



## **Data Analysis**



### `Step 1:` Importing Data

In this step, we import all required `libraries` for the analysis and also the `source file` itself.



### `Step 2:` Data Cleaning

In this step, we clean the data of any null values.



### `Step 3:` Data Pre-Processing

In this step, we make our data ready foir analysis - extracting only required features, creating new features from existing ones, fixing date-time formats, renaming columns and so on.



### `Step 4:` Analysis 

Here, we perform all sorts of temporal, categoriccal and geospatial analysis on the given data.



### `Step 5:` Visualization

Here, we use `Matplotlib` and `Seaborn` to visualize our data.



### `Step 6:` Conclusion

Some conclusions drawn from the analysis on each instance.



# **THANK YOU**