Today I have learned about KNN (K Nearest Neighbors) algorithm, a handy tool for our Project 2 dataset. KNN excels at finding similarities in datasets where similar features tend to cluster together. The process involves addressing missing values, normalizing numerical features for equal importance, selecting relevant features like demographics and city data, splitting the dataset for training and testing, determining the optimal number of neighbors (K), training the model, evaluating its performance, and finally using it to predict categories for new data points. KNN’s focus on similarity makes it particularly useful for uncovering patterns in geographical dynamics.