What is Linear Regression? Details of Cost function and Derivation.

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Linear Regression is a statistical method to model the relationship between a dependent variable and one or more independent variables.

Linear Regression is used to predict a continuous output based on one or more input features.
The cost function for Linear Regression is the Mean Squared Error (MSE), which measures the average squared difference between the predicted values and the actual values.
The goal of Linear Regression is to minimize the cost function by adjusting the parameters (coefficients) of the model.
The derivation of the cost function involves taking the partial derivative with respect to each parameter and updating the parameters using gradient descent.
Example: Predicting house prices based on features like size, number of bedrooms, and location using Linear Regression.

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