Ridge and Lasso Regression Practical Implementation in Python

Linear Regression

Understanding Linear Regression:

Before diving into Ridge and Lasso regression, recap linear regression. click here for understand Linear Regression.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_openml

df = fetch_openml(name='boston')

dataset=pd.DataFrame(df.data)
dataset

# Independent features and dependent features
X=dataset
y=df.target

# train test split
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.30, random_state=42)

# standardizing the dataset
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train=scaler.fit_transform(X_train)
X_test=scaler.transform(X_test)
from sklearn.linear_model import LinearRegression
#cross validation
from sklearn.model_selection import cross_val_score
regression=LinearRegression()
regression.fit(X_train,y_train)

mse=cross_val_score(regression,X_train,y_train,scoring='neg_mean_squared_error',cv=10)
np.mean(mse)
##prediction
reg_pred=regression.predict(X_test)
import seaborn as sns
sns.displot(reg_pred-y_test,kind='kde')

from sklearn.metrics import r2_score
score=r2_score(reg_pred,y_test)
score

Ridge Regression Algorithm

Recap Ridge Regression :- Click here to get complete Mathematical Intuition behind ridge and lasso regression.

from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV

ridge_regressor=Ridge()

ridge_regressor

parameters={'alpha':[1,2,5,10,20,30,40,50,60,70,80,90]}
ridgecv=GridSearchCV(ridge_regressor,parameters,scoring='neg_mean_squared_error',cv=5)
ridgecv.fit(X_train,y_train)

print(ridgecv.best_params_)

print(ridgecv.best_score_)

ridge_pred=ridgecv.predict(X_test)

import seaborn as sns
sns.displot(ridge_pred-y_test,kind='kde')

from sklearn.metrics import r2_score
score=r2_score(ridge_pred,y_test)

score

Lasso Regression Algorithm

Recap Lasso Regression :- Click here to get complete Mathematical Intuition behind ridge and lasso regression.

# Lasso Regression
from sklearn.linear_model import Lasso

lasso=Lasso()

parameters={'alpha':[1,2,5,10,20,30,40,50,60,70,80,90]}
lassocv=GridSearchCV(lasso,parameters,scoring='neg_mean_squared_error',cv=5)
lassocv.fit(X_train,y_train)

print(lassocv.best_params_)
print(lassocv.best_score_)

lasso_pred=lassocv.predict(X_test)

import seaborn as sns
sns.displot(lasso_pred-y_test,kind='kde')

print("Thank-You!Happy Learning!")

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