Logistic regression serves as a statistical modeling technique utilized for examining the connection between a binary outcome variable and one or more predictor variables. In this analysis, I designated “manner_of_death” as the binary outcome variable, where the column specifies whether the death resulted from being “shot” or “shot and tasered.” Additional columns such as “armed,” “age,” “gender,” and “race” were considered as predictor variables to gauge the likelihood of a specific mode of death.
Subsequently, I delved into exploring and preprocessing the data, addressing missing values and encoding categorical variables. Following this, a logistic regression model was constructed by fitting the data. In this model, the binary variable became the dependent variable, and the other columns served as independent data, acting as predictors.
The model’s performance was then evaluated using various metrics, including accuracy, precision, recall, F1 score, and R2 score, all of which demonstrated satisfactory results. This comprehensive approach allowed for a thorough understanding of the relationship between the chosen predictors and the binary outcome, providing valuable insights into the risk estimation for different modes of death.