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37 changes: 32 additions & 5 deletions README.md
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Expand Up @@ -22,18 +22,45 @@ To write a python program to implement multivariate linear regression and predic

## Program:
```
import matplotlib.pyplot as plt
import numpy as np
from sklearn import linear_model
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split

# Load dataset
data = load_diabetes()
X = data.data
y = data.target

# Split dataset
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.4, random_state=1
)

# Model
reg = linear_model.LinearRegression()
reg.fit(X_train, y_train)

print("Coefficients:", reg.coef_)
print("Variance score:", reg.score(X_test, y_test))

plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train,
color="green", s=10, label="Train data")

```
## Output:
plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test,
color="blue", s=10, label="Test data")

### Insert your output
plt.hlines(y=0, xmin=0, xmax=400, linewidth=2)

<br>
plt.legend(loc="upper right")
plt.title("Residual Errors")
plt.show()

```
## Output:
<img width="1388" height="740" alt="image" src="https://github.com/user-attachments/assets/5cb68c09-7664-48eb-8d18-129fdef02a2e" />
<img width="1381" height="649" alt="image" src="https://github.com/user-attachments/assets/37a4e01e-3c93-4025-8a82-34846f700957" />

## Result
Thus the multivariate linear regression is implemented and predicted the output using python program.
Thus the multivariate linear regression is implemented and predicted the output using python program.