1. What are the assumptions of Linear Regression ? 2. What is the formula for Euclidean distance in K-Means ? 3. How does SVM work ? 4. How does SVM work on non linearly separable data ?

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Answers to questions related to Linear Regression, K-Means, and SVM in data science.

Assumptions of Linear Regression include linearity, independence, homoscedasticity, and normality of errors.
Euclidean distance formula in K-Means is the square root of the sum of squared differences between two points.
SVM works by finding the hyperplane that best separates the classes in the feature space.
SVM on non-linearly separable data uses techniques like kernel trick to map data into higher dimensions for separation.

1. What are the assumptions of Linear Regression ? 2. What is the formula for Euclidean distance in K-Means ? 3. How does SVM work ? 4. How does SVM work on non linearly separable data ?

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