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Linear Regression – Assumption – 2 (How To Detect & Avoid Multicollinearity ?)
Linear Regression – Assumption- 2 (How To Detect & Avoid Multicollinearity ?) Table Of Contents: How To Detect Multicollinearity In The Dataset ? Correlation Matrix. Variance Inflection Factor Model Behavior Observation. How To Avoid Multicollinearity In The Dataset ? Remove One of the Correlated Variables Use Principal Component Analysis (PCA) Use Regularization Techniques (Ridge/Lasso) (1) How To Detect Multicollinearity In The Dataset? Method – 1: Correlation Matrix (Pearson correlation) We will use Pearson ‘r’ Correlation Coefficient to find the correlation between two variable. import seaborn as sns import matplotlib.pyplot as plt # Load dataset tips = sns.load_dataset("tips") # Compute the
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Linear Regression – Assumption -2 (No Multicollinearity)
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Linear Regression – Assumption- 1 (Linear Relationship)
Linear Regression – Assumption- 1 (Linear Relationship) Table Of Contents: What Is Linear Relationship Assumption ? Why Linear Regression Assumption Is Important ? How To Check Linearity Between Dependent & Independent Variable ? How The Residuals Can Say About The Linearity ? What To Do If You Have Non Linearity Present In The Data ? (1) What Is Linear Relationship Assumption ? (2) What Is Linear Relationship Assumption Important ? (3) How To Check Linearity Between Dependent & Independent Variable ? (1) Using Scatter Plot import seaborn as sns import matplotlib.pyplot as plt # Load a real dataset tips =
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Seaborn – Interactive Seaborn With Plotly
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Seaborn – Combining Multiple Plots
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Seaborn – Working With Big Datasets
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Seaborn – Saving Plots
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Seaborn – Adjusting Figure Size
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Seaborn – Changing colors & Palettes
