Scikit-Learn Linear Regression

In this tutorial, you will learn

1. How to import the Scikit-Learn libraries?

2. How to import the dataset from Scikit-Learn?

3. How to explore dataset?

4. How to split the data using Scikit-Learn train_test_split?

5. How to implement a Linear Regression Model in Scikit-Learn?

6. How to predict the output using a trained Linear Regression Model?

7. How to calculate Mean Squared Error (MSE)?

Linear Regression

In linear regression, we try to build a relationship between the training dataset (X) and the output variable (y). We predict the output variable (y) based on the relationship we have implemented.

1. Import the Libraries

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import mean_squared_error
import numpy as np

2. Import the Dataset

data = load_iris()
X=data['data']
y=data['target']

3. Explore the Dataset

X[:10]

print(y)
print(y.shape)

4. Splitting the Dataset

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
X_train.size

5. Model Implementation and Fitting

linearregression=LinearRegression()
linearregression.fit(X_train,y_train)

6. Model Prediction

predictions=linearregression.predict(X_test)
predictions

7. Calculate Mean Squared Error

mean_squared_error(predictions, y_test)

Summary

1. datasets : To import the Scikit-Learn datasets.

2. shape : To get the size of the dataset.

3. train_test_split : To split the data using Scikit-Learn.

4. LinearRegression( ) : To implement a Linear Regression Model in Scikit-Learn.

5. predict( ) : To predict the output using a trained Linear Regression Model.

6. mean_squared_error( ) : To calculate Mean Squared Error (MSE).

PML