This project performs an in-depth analysis of homicide data, focusing on understanding the key trends in victim demographics, crime types, and patterns across different years, cities, and states. The goal is to gain insights into crime rates, the characteristics of victims, and the impact of various factors on the occurrence of homicide incidents.

The dataset contains information about homicide incidents, including victim and perpetrator characteristics, weapon types, and the locations where the crimes occurred. The analysis covers:

• Victim demographics (age, gender, race, ethnicity)
• Crime rates over the years
• Crime distribution across cities and states
• Weapon types used in crimes

• Victim Age Distribution: The age distribution of homicide victims.
• Crime Trends: Identification of years with the highest and lowest crime rates.
• Geographic Distribution: Analysis of cities and states with the highest homicide reports.
• Victim Characteristics: Overview of common victim characteristics such as gender, race, and age.

• Python 3.x
• Jupyter Notebook or JupyterLab
• Required libraries: pandas, numpy, matplotlib, seaborn

To install the required libraries, run:

pip install -r requirements.txt

## Steps to Run:
Clone the repository
git clone https://github.com/evans25575/Homicide-Data-Analysis.git

Open the Jupyter Notebook file (homicide data analysis.ipynb) in Jupyter or VS Code.