Clustering

• Classification

• Dimensionality Reduction

• Reinforcement Learning

k-Nearest Neighbors

Decision Trees

Support Vector Machines

Linear Regression

Model too simple

Model fits the training data too closely

Lack of training data

Model underperforms on test data

Mean Squared Error (MSE)

R-squared

F1 Score

Mean Absolute Error (MAE)

Evaluate model accuracy

Assess the impact of feature scaling

Visualize decision boundaries

Summarize the performance of a classification algorithm

• Decision Trees

• k-Nearest Neighbors

Support Vector Machines

SMOTE (Synthetic Minority Over-sampling Technique)

It helps in feature scaling

It transforms raw data into a format suitable for input to machine learning models

It focuses on reducing dimensionality of feature space

It simplifies complex mathematical operations