This blog explains about KNN (K nearest neighbor) machine learning algorithm, which can be effectively used in our Project 2 dataset.
KNN operates on the principle of similarity, making it ideal for datasets where states with similar features tend to cluster together.
Handle Missing Values: Ensure a complete dataset by either imputing or removing missing values.
Feature Scaling: Normalize numerical features to ensure equal importance during distance calculations.
Feature Selection: Identify relevant features for our analysis, considering factors such as demographics, city, and state variables that may influence characteristics.
Data Splitting: Split our dataset into training and testing sets to evaluate the model’s performance.
Choosing K: Determine the best value for K (number of neighbors) through techniques like cross-validation.
Model Training: Train the KNN model on the training dataset using the selected features.
Model Evaluation: Assess the model’s performance on the testing set using metrics like accuracy, precision, and recall.
Prediction: Utilize the trained KNN model to predict the cluster or category of new data points, revealing hidden patterns within the dataset.
KNN, with its focus on proximity and similarity, becomes a valuable machine learning algorithm for uncovering patterns and relationships within the dataset. By exploring the dataset based on shared characteristics, KNN offers a unique perspective on geographical dynamics.
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