Goal The goal of this project is to perform an exploratory data analysis on Chicago crime data (specifically robbery, arson, and motor vehicle theft) from 2001-2020.
Method First I inspected and cleaned the datasets. Then, used time series to analyze annual and monthly trends and data visualization to analyze spacial trends. I used correlation matrices to identify relational trends regarding arrests proportions and income as well as arrest proportions and location. I also fit a model using time series analysis to predict future robberies in a specific neighborhood of Chicago.
Files
- Chicago_Crime_Analysis.ipynb - Jupyter Notebook file containing all code and analysis for the 3 crime files
- Jupyter Notebook
- Exploratory Data Analysis
- Data Cleaning
- Model Fitting
- Data Visualization
- Python
- Pandas
- Jupyter Notebook
- Folium
- Tyson Miller