MTH 522 Friday 27 October

Title : Exploring How Mental Health and Race Connect in Police Shootings

So today  I was diving into the connection between signs of mental illness and race in police shootings. We’re using something called facet grids in R to explore this. The goal is to find any patterns or connections that can help us understand the complicated link between mental health and how people interact with the police. The pictures that I was making give us a detailed view of how mental health and race might be related in the context of police shootings.

Come along as we go through these visual grids and talk about what these findings mean. We’ll discuss how these discoveries can shape conversations among the public and might even influence policies when it comes to dealing with mental health in encounters with law enforcement.

MTH 522 Wednesday 25 October

Title: Looking at the Gender Differences in Police Shootings

I was looking closely at whether the people who are victims of police shootings in the U.S. are mostly male or female. The graphs we made using a program called R show a big difference between the number of male and female victims. This investigation not only points out that one gender is affected more than the other but also makes us wonder why this is happening. We’re going to explore the details of how gender plays a role in these situations and talk about what these graphs can tell us about police violence.

On the other side I was shifting our focus to detailed graphs that show the racial makeup of people who are victims of police shootings in the U.S. These graphs help us see differences among different racial groups leading to important discussions about problems within law enforcement. Using a tool called R . I was not just showing the numbers but also telling a visual story that encourages people to think deeply about the information. Let’s explore what these racial differences mean and how they contribute to a better understanding of the complicated issues around police shootings in the United States.

MTH 522 Monday 23 October

Title: Correlations and Hypotheses

I started looking closely at the data and trying to find connections between different things. Using numbers and stats I came up with some initial ideas about how certain factors, like signs of mental illness, threat level, and whether someone tried to run away, might affect what happens during police interactions. These early ideas are like a pathway for us to do more detailed analyses in the next few weeks.

It is like we are building a roadmap to understand the complicated relationships between different aspects of these situations. As we uncover more connections and patterns it helps us piece together a better picture of what might be going on in police encounters. It is all about exploring and making sense of the data to find out more about the factors that play a role in how these situations unfold.

MTH 522 Friday 20 October

Title: Patterns and Anomalies in Police Encounters

I went beyond just looking at the obvious things. I dug deep into the data trying to find patterns and strange things. I carefully looked at different aspects like how people died, whether they were armed, and if they showed signs of mental illness. The goal was to figure out if there were any common themes in the information. This deeper understanding is like a big first step, helping us look even closer at what’s going on with police shootings in the United States. It’s like peeling back layers to reveal the complicated stuff that surrounds these incidents.

MTH 522 Wednesday 18 October

Title : Map Revealed

As we kept going, I focused on where things were happening on a map. By using data about longitude and latitude I could see patterns and groups of incidents. This helped me spot areas that might have more problems. Looking at the map not only showed me where things were worse, but it also gave me clues about why. Knowing this can help us make specific plans and suggestions for those areas. Understanding the geography is really important for coming up with effective solutions.

MTH 522 Monday 16 October

we engaged in a thorough examination of our dataset, aiming to discern correlations among various variables. My objective was to investigate whether factors such as an individual’s race or their armed status have any discernible connections to the occurrence of fatal police shootings.

Applying advanced data analysis techniques including the creation of informative visualizations, we systematically explored the dataset to identify patterns that could contribute to a more comprehensive understanding of the underlying dynamics.

MTH 522 Friday 13 October

Title: Data Acquisition and Initial Exploration

We performed an initial exploration of the dataset to understand its structure. This included taking a look at the first few rows, examining the columns and data types, and gaining an overview of the features available.

The first steps of data preparation were taken which included identifying any potential issues or anomalies in the data. So I was ensuring that the dataset was up-to-date for our project. However as expected, data quality issues were identified during the initial exploration. These issues included missing data, inconsistent formats, and potential outliers.

MTH 522 Wednesday 11 October

Title: Analyzing the Washington Dataset

I have been working on visualizing the Washington dataset, which includes various features, and I have also been reading articles from The Washington Post. The primary motivation behind reading these articles is to make comparisons between my own analysis which I would be doing during my project and the analysis presented in the articles.

This is an important part of my project, and it allows me to gain insights and perspectives from different sources, contributing to a more comprehensive understanding of the data and its implications. By doing this I can make sure that my project is well-informed and looks at things from different angles.

MTH 522 Monday 09 October

Title: Finalizing the Project Report

I have completed the methodology section of our project report, detailing the data collection process, data cleaning steps, and the methodology used for building and evaluating our machine learning models.

Other hand I have made more efforts toward the results section, conclusion, and discussion sections of the report which included visualizations, tables, and clear explanations to convey our findings effectively.