Today during the Friday doubt class, we discussed that in our initial exploration, did we notice any correlations or interesting trends between variables, such as the presence or absence of weapons and the outcome of these incidents.
During my initial exploration of the dataset, I did observe some correlations and trends related to the presence or absence of weapons and the outcomes of these incidents. It appeared that there were variations in outcomes based on the presence of weapons. However, to provide more specific and statistically significant insights, we would need to perform a more in-depth analysis, possibly using techniques such as logistic regression to model the relationship between the presence of weapons and the likelihood of specific outcomes.
We also talked about what visualization techniques other than histograms are we considering to further analyze and interpret the dataset.
In addition to histograms, I’m considering a variety of visualization techniques to gain a more comprehensive understanding of the dataset. These techniques include scatter plots, which will allow us to explore relationships between two numerical variables, such as age and the type of threat. Additionally, we are contemplating the use of bar charts to effectively visualize categorical data and compare counts or proportions, particularly in the context of understanding the distribution of races among the victims. Time series plots will help us uncover trends and patterns in fatal police shootings over time, while box plots will provide insights into the distribution of numerical data, aiding in the identification of outliers and variations across different variables.
I also discussed with Gary about heatmaps offering a means to discover correlations between various attributes, shedding light on the interplay of factors in these incidents. However, we also talked that we could use geospatial maps that can be employed if the dataset contains location data, allowing us to map incidents on a geographic scale and reveal geographic patterns and hotspots.
I’m trying to plot pie charts that can illustrate proportions of specific categorical variables, such as the proportion of different kinds of threats. The selection of the most appropriate visualization technique will depend on the specific questions and insights that I aim to derive from the dataset.