How to Check MultiCollinearity Using VIF in Python?

This tutorial will help you discover the power of VIF in Python to identify multicollinearity issues in your datasets. | ProjectPro

Checking for multicollinearity and addressing it appropriately is important for data scientists to build reliable and accurate machine learning models. This tutorial uses Python's Variance Inflation Factor (VIF) to detect multicollinearity. By the end of this tutorial, you will have the skills to identify and mitigate multicollinearity issues in your regression models confidently. 

What is Multicollinearity in Python? 

Multicollinearity occurs when the independent variables in a regression model are highly correlated. This leads to instability and unreliable estimates of the regression coefficients, which can severely impact the model's interpretability and predictive power.

How to Check for Multicollinearity in Python? 

The variance inflation factor (VIF) can be used to check the multicollinearity.

VIF starts at 1 and has no limits. If VIF = 1, there is no correlation between independent variables. If VIF > 10, there is high multicollinearity between independent variables.

Why Use VIF in Python for Multicollinearity?

Variance Inflation Factor is a statistical measure used to quantify the severity of multicollinearity in a regression analysis. It assesses how much the variance of an estimated regression coefficient is inflated due to multicollinearity. A high VIF value indicates high multicollinearity, warranting further investigation or remedial action. 

How to Check Multicollinearity for Categorical Variables in Python? 

First, let's assume we have a dataset containing information about housing prices. We want to build a regression model to predict the cost of a house based on various features such as size, number of bedrooms, and location. However, before constructing the model, we must check for multicollinearity among the independent variables.   

Checking multicollinearity in Python - Example

Checking multicollinearity in regression using Python

Assuming housing_data.csv contains your dataset with columns 'Size', 'Bedrooms', and 'Location', this code will calculate the VIF for each variable.

The output will display the VIF values for each independent variable. Any VIF value greater than a certain threshold (commonly 5 or 10) indicates multicollinearity issues.

Once you have calculated the VIF values, here's how to interpret the results:

  • VIF = 1: No multicollinearity. The variance of the coefficient estimate is not inflated.

  • VIF > 1 and < 5: Moderate multicollinearity. Consider further investigation.

  • VIF >= 5: High multicollinearity. Action should be taken, such as removing correlated variables or using techniques like ridge regression.

Example For Checking Multicollinearity in Python

Let’s understand with a simple example on checking multicollinearity in regression using Python - 

Step 1- Importing Libraries.

import pandas as pd

from statsmodels.stats.outliers_influence import variance_inflation_factor

Step 2- Reading File

df= pd.read_csv('/content/sample_data/california_housing_train.csv')

df.head()

Step 3- Defining Function.

We will define a function to check the correlation between the independent variables.

def calc_VIF(x):

  vif= pd.DataFrame()

  vif['variables']=x.columns

  vif["VIF"]=[variance_inflation_factor(x.values,i) for i in range(x.shape[1])]

  return(vif)

Step 4- Showing Multicollinearity.

x=df.iloc[:,:-1]

calc_VIF(x)

How to Handle Multicollinearity in Python? 

Let's discuss some standard techniques to address multicollinearity:- 

  1. Remove Correlated Variables - One straightforward approach is to remove one of the highly correlated variables from the analysis. You can identify highly correlated variables by calculating correlation coefficients or VIF values. Once identified, choose the less relevant variable to the outcome or has less theoretical importance and remove it from the model.

  2. Feature Engineering: Instead of eliminating variables, you can create new features that capture the essence of the correlated variables. For example, if you have two highly correlated variables related to area and perimeter, you can create a new feature representing the ratio of area to perimeter.

  3. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms the original variables into a set of linearly uncorrelated variables called principal components. By retaining only the principal components that explain most of the variance in the data, you can effectively reduce multicollinearity.

  4. Regularization Techniques: Regularization methods like Ridge Regression and Lasso Regression add a penalty term to the regression coefficients, which helps to shrink the coefficients and reduce the impact of multicollinearity.

  5. VIF-Based Variable Selection: Instead of removing variables arbitrarily, you can iteratively remove variables with high VIF values until all VIF values are below a certain threshold.

Master Regression Concepts Through ProjectPro’s Real-world Projects! 

Knowing how to spot multicollinearity in Python using VIF is essential for anyone working with data analysis. It helps ensure our predictions and insights from regression models are accurate and reliable. But more than just learning about it is needed; you must practice with real projects to understand it well. That's where ProjectPro comes in handy! It's a great platform with over 250+ projects on data science and big data. Working on these projects will help you get hands-on experience and become skilled at spotting multicollinearity.

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