In today’s blog, I have performed the K-means clustering for both the given data set. While performing the technique, I got stuck in the first dataset where I need to find out the correlation between the variance for gender, race, city, and state and that too of the police departments where we can link the encounter for an individual

While performing the task I featured age and gender to find out the killings happened for male and female both. While doing so, I can conclude that the killings of male is much higher as compared to female. To compute the gender into a float variable I used the technique label encoding to valuate the string and got the output into the float so that I can correlate with the age factor.

I plot the graph of the killings happened for both the genders. Later I am thinking to perform the validation metrics to asses the quality, performance, and validity of the machine learning and data analysis models. Depending on the particular task or issue you are working on, these metrics assist you in assessing how well a model or method is functioning in terms of its capacity to provide precise predictions or conclusions. In order to evaluate many models, choose the best one, and adjust model hyperparameters, validation metrics are crucial.