#28 Case Study on Regression | Part III | Python for Data Science

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This video focuses on building and comparing regression models for predicting car prices. It explores two approaches to handling missing data: omitting rows with missing values and imputing missing values. For each approach, the lecture demonstrates how to build two regression models, linear regression and random forest regression, using Python libraries like scikit-learn and pandas. The video emphasizes the importance of choosing the right model for the data, as well as the trade-offs involved in handling missing data. The analysis includes calculating various metrics like Root Mean Squared Error (RMSE) and R-squared to assess the performance of the models, ultimately concluding that the random forest regression model outperforms linear regression, particularly in cases where data has been imputed.
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#LogTransformation #ModelImprovement #LinearRegression #ModelComparison

[harshsunilchhajed]

How can someone be this bad at teaching