# 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.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).

You can find the Github link here.