Practical Data Science using Python - Regression Models and Performance Metrics

The video tutorial covers regression predictive modeling, focusing on linear regression and its application in predicting continuous variables like house prices. It explains the importance of minimizing the differences between actual and predicted values using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The tutorial also discusses the gradient descent algorithm for error minimization and introduces other metrics like residual sum of squares and the coefficient of determination (R2) for evaluating regression models.

What is the purpose of using Mean Absolute Error (MAE) in regression analysis?

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What are the differences between Residual Sum of Squares (RSS) and Total Sum of Squares (TSS)?

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Explain the concept of R-squared in the context of regression models.

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How can one assess the performance of a linear regression model?

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