crime analysis from amosoyar #26

Exploring Crime Data within Chicago

In this particular project, I will focus on performing exploratory data analysis (EDA) on a sample from the 'Crimes in Chicago from 2001 to Present' dataset. The aim is to identify relationships connected to how often, when, and what type of crimes are committed in Chicago.

Core Goals:

Establish what type of crimes are committed the most.

Study the crime distribution over time (hour, day of week, month, year).

Establish the most popular places where crime is committed.

Analyze how crime rate trends have changed over the years.

Highlights of the Analysis:
Loading and validation of data: The script validates that the file path is correct and subsequently loads the dataset using pandas.

First glance: Provides dataset info alongside displaying a few of the relevant columns from the dataset.

Visualization of crime types: Bar chart depicting how frequently different types of crime (Primary Type) is committed.

Time analysis:

Analyze hourly and daily distribution of crimes through countplot.
Trends on the month and year of the crime.

Insights on places: Establish along with visualizing top10 places where crime is mostly committed.

Trend analysis: depicts how many crimes in each year is reported and visualizes what years had the most or least peaks.

The tools used are the following:

Data Manipulation: Pandas and Numpy.

Data Visualization: Matplotlib and Seaborn.

Datetime to extract time-based p features.