Practical Data Science using Python - Linear Regression Model Building

The video tutorial explains the process of splitting a dataset into training and test sets for building a machine learning model, specifically linear regression. It emphasizes the importance of using separate data for training and testing to ensure the model's accuracy. The tutorial covers data scaling using MinMaxScaler and preparing predictor and target variables. It details the model building process using ordinary least squares and evaluates model performance using R-squared and adjusted R-squared values.

Why is it important to ensure that the training and test data are separate?

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Explain the process of creating a linear regression model using the OLS function.

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What is the significance of the coefficients in a linear regression model?

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What does a low p-value indicate in the context of model features?

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