**Linear Regression**:

`Type`

: Regression

`Supervised`

: The model learns from labeled data.

`Usage`

: Used for predicting continuous variables (e.g., predicting house prices).

`Data`

: Typically used for structured data (tabular data).

**Logistic Regression**:

`Type`

: Classification

`Supervised`

: The model learns from labeled data.

`Usage`

: Used for binary classification (e.g., spam detection).

`Data`

: Used for structured data.

**Support Vector Machine (SVM)**:

`Type`

: Can be used for both classification and regression (Support Vector Regression for regression).

`Supervised`

: The model learns from labeled data.

`Usage`

: Classifies data by finding the optimal hyperplane that separates classes; can also be used for regression.

`Data`

: Mostly used for structured data.

**Decision Tree**:

`Type`

: Both classification and regression.

`Supervised`

: The model learns from labeled data.

`Usage`

: Works by splitting data into decision nodes, used for both classification (e.g., medical diagnosis) and regression.

`Data`

: Structured data.

**Random Forest**:

`Type`

: Both classification and regression.

`Supervised`

: The model learns from labeled data.

`Usage`

: An ensemble of decision trees that improves prediction by reducing variance.

`Data`

: Used for structured data.

**K-Nearest Neighbors (KNN)**:

`Type`

: Both classification and regression.

`Supervised`

: The model uses labeled data, but makes predictions based on the proximity (distance) to other data points.

`Usage`

: For classification (e.g., image recognition) and regression (e.g., house price prediction).

`Data`

: Structured data.

**K-Means Clustering**:

`Type`

: Clustering (groups data into clusters based on similarity).

`Unsupervised`

: The model does not require labeled data.

`Usage`

: For clustering tasks, such as customer segmentation.

`Data`

: Typically used with structured data.

**Naive Bayes Classifier**:

`Type`

: Classification.

`Supervised`

: The model learns from labeled data using Bayes' Theorem.

`Usage`

: Used for text classification (e.g., spam detection).

`Data`

: Structured data, often with categorical features.

**Principal Component Analysis (PCA)**:

`Type`

: Dimensionality reduction.

`Unsupervised`

: No labeled data required.

`Usage`

: Reduces the dimensionality of large datasets while retaining most of the variance.

`Data`

: Primarily structured data, but can also be applied as a pre-processing step for unstructured data like images.

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