I’m currently working on report and have completed most of it — just the few results remains. Once that’s done, we will submit the report this week.

This week, Nina sent me some maps, so I started working on the report.
The maps are helpful because they show important information in a visual way. I looked at them closely and found some key points. These will help me decide how to organize and write the report.

In class, we discussed data cleaning. Nina, Lavanya, and I had a conversation about different organizations involved in protests while organizing the data. I’m also thinking about the title we will give to our report.

I joined Lavanya and Nina group for the upcoming Data Science Report.

I’ve realized that working alone can be quite challenging, especially when it’s your first time doing something. However, working as a team is a completely different experience. In a team, everyone contributes ideas and shares the workload, making the process much smoother. Each person brings something unique to the table, which leads to more creativity and collaboration.

This week, we discussed the problem statements that everyone is working on and explored how each of us will contribute to the report.

Questions are:

• Why did some counties have protests related to Labor laws over others?
• Which organizations are involved most in violent demonstrations?
• What is the ratio of violent protests to peaceful protests? Geographically plot the unique values for both violent and peaceful events to visualize this.
• Is there a year that stands out with more violent protests than others?

 

This week, I have been learning about randomness and exploring how I can perform predictive analysis for the next year using the provided data. I am also considering the following problem statements:

1. Identify the most frequently involved groups or organizations in protests.

2. Create a heatmap to visualize the intensity of protests across different counties and states.

3. Determine which counties and states have the highest number of protests.

4. Analyze the trend of protests over the years to see whether they are increasing or decreasing.

 

This week, we looked at two different datasets that we will work on in the next few weeks. I focused on understanding the data and thinking of questions for my analysis.

I also learned about clustering, especially K-Means clustering. This method helps find patterns and group similar data points. Knowing K-Means will help me organize the data better in my analysis.

Next, I will improve my research questions and see how clustering can help me find useful insights from the data.

I am working on my code and documentation for my report. It focuses on the coding process and methods used in my analysis. I am explaining the steps clearly to make the report easy to understand. My goal is to ensure accurate and well documented Findings.

This week, I focused on data collection and data cleaning, ensuring that my dataset is well-structured and ready for analysis. With this phase completed, I am now shifting my focus to the methodology section of my report, where I will outline the approach and techniques I will use for analysis.

I am analyzing the correlation between unemployment and crime rates across U.S. states. So far, I have collected the data, imported it into Mathematica, and explored various built-in functions.

The best thing I get to know this week is Mathematica Software which I will going to explore furthermore and I will try to use in my report. I am still searching for the state’s unemployment data for the correlation between unemployment and crime rates.