33 Code Templates for Machine Learning Models

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33 templates include:

1. Linear Regression: A simple algorithm for predicting a continuous outcome based on linear relationships.
2. Logistic Regression: Used for binary classification problems, estimating probabilities using a logistic function.
3. k-Nearest Neighbors (k-NN): Classifies a data point based on the majority class of its k-nearest neighbors.
4. Naive Bayes: A probabilistic algorithm based on Bayes’ theorem, often used for classification.
5. Decision Trees: Hierarchical tree-like structures for decision-making, widely used in classification and regression.
6. Random Forest: An ensemble of decision trees, providing robustness and improved performance.
7. Support Vector Machines (SVM): Classifies data points by finding the hyperplane that maximizes the margin between classes.
8. Principal Component Analysis (PCA): Reduces dimensionality by identifying the most important features in the data.
9. K-Means Clustering: Divides data into k clusters based on similarity.
10. Hierarchical Clustering: Builds a hierarchy of clusters by merging or splitting them iteratively.
11. Gradient Descent: An optimization algorithm used for finding the…