For your final project and presentation in this course, you will need to propose a dataset to analyze. This proposal has four parts, in which you will choose your dataset, provide the background for that dataset including why you chose it and from where, provide information on the data itself, and provide an image from your imported data. This assignment is due by 11:59 CT on Sunday. A word document containing writing, tables, and images from Part 1, Part 2, Part 3 and Part 4. Your chosen dataset For the project in this course you will need to choose a dataset to analyze. This dataset will need to meet the following requirements: Recommended sites to find a dataset: Write a summary describing the topic of the dataset. Include: why you chose that dataset, where it came from, and what kind of problem you could solve with it. Provide information on the dimensions of the dataset. Include information on the fields and their data types. Be sure to state if they are continuous/numeric or categorical? Provide an image of the results of importing your dataset and using the head() function for printing. National Louis University

Dataset Proposal: Analysis of Crime Rates in Chicago

1. Dataset Background and Rationale:
The dataset I have chosen to analyze is the “Crime in Chicago” dataset. I chose this dataset because of my interest in studying crime rates and understanding the factors that contribute to criminal activity in urban areas. This dataset is publicly available and was obtained from the City of Chicago’s Open Data Portal. By analyzing this dataset, I aim to identify patterns and trends in crime rates in Chicago, which can help policymakers and law enforcement agencies in making informed decisions.

2. Dataset Information:
The “Crime in Chicago” dataset includes information about reported crimes in the city of Chicago. It contains various fields such as date, type of crime, location description, arrest status, and community area. The dataset provides detailed information about each reported crime, allowing for a comprehensive analysis of different aspects of criminal activity in the city.

The dimensions of the dataset include:
– Number of rows: This dataset consists of approximately 6 million rows, representing individual reported crimes.
– Number of columns: There are multiple columns in the dataset, each representing a specific attribute or characteristic of the reported crime.

The dataset includes several fields, including:
1. Date: This field represents the date and time of the reported crime. It is stored as a categorical data type.
2. Type of Crime: This field describes the category or type of crime committed, such as theft, assault, or burglary. It is stored as a categorical data type.
3. Location Description: This field provides the description of the location where the crime occurred, such as a street, residence, or park. It is stored as a categorical data type.
4. Arrest: This field indicates whether an arrest was made in relation to the reported crime. It is stored as a categorical (Yes/No) data type.
5. Community Area: This field represents the community area where the crime occurred. It is stored as a categorical data type.

3. Image of Imported Dataset:
To demonstrate the imported dataset, I have used the head() function to display the first few rows of the data. This image provides an overview of the dataset, including the column names and the corresponding data for each column. (Please see the attached word document for the image).

In conclusion, the chosen dataset for analysis is the “Crime in Chicago” dataset, obtained from the City of Chicago’s Open Data Portal. By analyzing this dataset, I aim to gain insights into crime rates in Chicago and identify patterns that can assist in addressing and preventing criminal activity in the city. The dataset consists of various fields representing different attributes of reported crimes, and it provides a comprehensive view of criminal activity in Chicago.