# ML | Implementing L1 and L2 regularization using Sklearn

Prerequisites: L2 and L1 regularization

This article aims to implement the L2 and L1 regularization for Linear regression using the Ridge and Lasso modules of the Sklearn library of Python.
Dataset – House prices dataset .

Step 1: Importing the required libraries

 `import` `pandas as pd ` `import` `numpy as np ` `import` `matplotlib.pyplot as plt ` `from` `sklearn.linear_model ``import` `LinearRegression, Ridge, Lasso ` `from` `sklearn.model_selection ``import` `train_test_split, cross_val_score ` `from` `statistics ``import` `mean `

 `# Changing the working location to the location of the data ` `cd C:\Users\Dev\Desktop\Kaggle\House Prices ` ` `  `# Loading the data into a Pandas DataFrame ` `data ``=` `pd.read_csv(``'kc_house_data.csv'``) ` ` `  `# Dropping the numerically non-sensical variables ` `dropColumns ``=` `[``'id'``, ``'date'``, ``'zipcode'``] ` `data ``=` `data.drop(dropColumns, axis ``=` `1``) ` ` `  `# Separating the dependent and independent variables ` `y ``=` `data[``'price'``] ` `X ``=` `data.drop(``'price'``, axis ``=` `1``) ` ` `  `# Dividing the data into training and testing set ` `X_train, X_test, y_train, y_test ``=` `train_test_split(X, y, test_size ``=` `0.25``) `

Step 3: Building and evaluating the different models

a) Linear Regression:

 `# Bulding and fitting the Linear Regression model ` `linearModel ``=` `LinearRegression() ` `linearModel.fit(X_train, y_train) ` ` `  `# Evaluating the Linear Regression model ` `print``(linearModel.score(X_test, y_test)) ` b) Ridge(L2) Regression:

 `# List to maintain the different cross-validation scores ` `cross_val_scores_ridge ``=` `[] ` ` `  `# List to maintain the different values of alpha ` `alpha ``=` `[] ` ` `  `# Loop to compute the different values of cross-validation scores ` `for` `i ``in` `range``(``1``, ``9``): ` `    ``ridgeModel ``=` `Ridge(alpha ``=` `i ``*` `0.25``) ` `    ``ridgeModel.fit(X_train, y_train) ` `    ``scores ``=` `cross_val_score(ridgeModel, X, y, cv ``=` `10``) ` `    ``avg_cross_val_score ``=` `mean(scores)``*``100` `    ``cross_val_scores_ridge.append(avg_cross_val_score) ` `    ``alpha.append(i ``*` `0.25``) ` ` `  `# Loop to print the different values of cross-validation scores ` `for` `i ``in` `range``(``0``, ``len``(alpha)): ` `    ``print``(``str``(alpha[i])``+``' : '``+``str``(cross_val_scores_ridge[i])) ` From the above output, we can conclude that the best value of alpha for the data is 2.

 `# Building and fitting the Ridge Regression model ` `ridgeModelChosen ``=` `Ridge(alpha ``=` `2``) ` `ridgeModelChosen.fit(X_train, y_train) ` ` `  `# Evaluating the Ridge Regression model ` `print``(ridgeModelChosen.score(X_test, y_test)) ` c) Lasso(L1) Regression:

 `# List to maintain the cross-validation scores ` `cross_val_scores_lasso ``=` `[] ` ` `  `# List to maintain the different values of Lambda ` `Lambda ``=` `[] ` ` `  `# Loop to compute the cross-validation scores ` `for` `i ``in` `range``(``1``, ``9``): ` `    ``lassoModel ``=` `Lasso(alpha ``=` `i ``*` `0.25``, tol ``=` `0.0925``) ` `    ``lassoModel.fit(X_train, y_train) ` `    ``scores ``=` `cross_val_score(lassoModel, X, y, cv ``=` `10``) ` `    ``avg_cross_val_score ``=` `mean(scores)``*``100` `    ``cross_val_scores_lasso.append(avg_cross_val_score) ` `    ``Lambda.append(i ``*` `0.25``) ` ` `  `# Loop to print the different values of cross-validation scores ` `for` `i ``in` `range``(``0``, ``len``(alpha)): ` `    ``print``(``str``(alpha[i])``+``' : '``+``str``(cross_val_scores_lasso[i])) ` From the above output, we can conclude that the best value of lambda is 2.

 `# Building and fitting the Lasso Regression Model ` `lassoModelChosen ``=` `Lasso(alpha ``=` `2``, tol ``=` `0.0925``) ` `lassoModelChosen.fit(X_train, y_train) ` ` `  `# Evaluating the Lasso Regression model ` `print``(lassoModelChosen.score(X_test, y_test)) ` Step 4: Comparing and Visualizing the results

 `# Building the two lists for visualization ` `models ``=` `[``'Linear Regression'``, ``'Ridge Regression'``, ``'Lasso Regression'``] ` `scores ``=` `[linearModel.score(X_test, y_test), ` `         ``ridgeModelChosen.score(X_test, y_test), ` `         ``lassoModelChosen.score(X_test, y_test)] ` ` `  `# Building the dictionary to compare the scores ` `mapping ``=` `{} ` `mapping[``'Linear Regreesion'``] ``=` `linearModel.score(X_test, y_test) ` `mapping[``'Ridge Regreesion'``] ``=` `ridgeModelChosen.score(X_test, y_test) ` `mapping[``'Lasso Regression'``] ``=` `lassoModelChosen.score(X_test, y_test) ` ` `  `# Printing the scores for different models ` `for` `key, val ``in` `mapping.items(): ` `    ``print``(``str``(key)``+``' : '``+``str``(val)) ` `# Plotting the scores ` `plt.bar(models, scores) ` `plt.xlabel(``'Regression Models'``) ` `plt.ylabel(``'Score'``) ` `plt.show() ` My Personal Notes arrow_drop_up Check out this Author's contributed articles.

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Improved By : shubham_singh

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