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VIF application in python for beginners

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Computer Science

Lessons no : 9

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Lessons | 9

Principal component analysis step by step PCA explained step by step PCA in statistics

Principal component analysis step by step PCA explained step by step PCA in statistics

Linear discriminant analysis explained LDA algorithm in python LDA algorithm explained

Linear discriminant analysis explained LDA algorithm in python LDA algorithm explained

Variance Inflation Factor Simplified Variance Inflation Factor in Multicollinearity VIF

Variance Inflation Factor Simplified Variance Inflation Factor in Multicollinearity VIF

Why multicollinearity is a problem Why is multicollinearity bad What is multicollinearity

Why multicollinearity is a problem Why is multicollinearity bad What is multicollinearity

Feature Selection techniques in Python feature selection machine learning machine learning tips

Feature Selection techniques in Python feature selection machine learning machine learning tips

Recursive Feature Elimination Technique Recursive feature elimination in machine learning

Recursive Feature Elimination Technique Recursive feature elimination in machine learning

Feature Selection Wrapper and Embedded techniques Feature Selection Playlist

Feature Selection Wrapper and Embedded techniques Feature Selection Playlist

VIF Application in Python VIF In python Variance Inflation Factor In Python

VIF Application in Python VIF In python Variance Inflation Factor In Python

Interview Questions On Feature Scaling normalization vs standardization machine learning

Interview Questions On Feature Scaling normalization vs standardization machine learning

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VIF application in python course, in this course dives into the fundamentals of Variance Inflation Factor (VIF) and its application in Python to detect and address multicollinearity in regression models. Learn how multicollinearity affects model accuracy and discover effective techniques to mitigate it. Through hands-on examples, explore the use of Python libraries like Statsmodels and Pandas to compute VIF values for independent variables, interpret the results, and improve model performance. Gain practical insights into feature selection and data preprocessing strategies for building robust statistical and machine learning models. Whether you are a beginner or an advanced user, this course equips you with essential skills to enhance the quality of your predictive models.