Crime-Data-Analysis

Overview

This repository contains a Jupyter notebook titled "Crime Data Analysis.ipynb," which focuses on analyzing and visualizing crime data. The aim is to uncover patterns, trends, and insights from the crime data, which could be valuable for law enforcement, policy makers, and researchers interested in criminology and urban studies. The strategy for achieving the objectives involves using feature engineering to classify the crime into violent and property based on the UCR document.

Data Source

This work aims to create data-driven prediction models to estimate the crime type in Los Angeles. The information utilised was data obtained from Data Lacity which was provided by LAPD Data Lacity and contains the record number, mocode, description of the crime, date and time at which the crime was reported/occurred, victim age, sex, ethnicity, location where the crime took place, and weapon of crime

Features

The notebook includes:

• Data Cleaning: Procedures for preparing the crime data for analysis.
• Exploratory Data Analysis (EDA): Techniques for summarizing the main characteristics of the dataset.
• Data Visualization: Graphs and charts to visualize the findings in an understandable manner.

Installation

To use this notebook, follow these steps:

Clone the repository to your local machine using Git:

• git clone crime data analysis
• Ensure you have Jupyter Notebook installed.
• Open the jupyter notebook 'Crime Data Analysis.ipynb'

Requirements

This project requires the following Python libraries:

• Pandas
• NumPy
• Matplotlib
• Seaborn

Usage

• Once you have opened the notebook, you can run each cell to see the analysis step-by-step. Feel free to modify the code to suit your specific data set or analysis requirements.

Contributing

Contributions to this project are welcome. Please follow these steps to contribute:

• Fork the repository.
• Create a new branch (git checkout -b feature-branch).
• Commit your changes (git commit -am 'Add some feature').
• Push to the branch (git push origin feature-branch).
• Create a new Pull Request.

Contact

For any queries or further discussion, feel free to contact me at lindaooby@gmail.com.