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# Linear Regression with the Diabetes Dataset Using Python Machine Learning

In this we use the diabetes dataset from sklearn and then we need to implement the Linear Regression over this:

```#import libraries
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score```

```# Load the diabetes dataset

Split Dataset

```# Use only one feature
diabetes_X = diabetes_X[:, np.newaxis, 2]

# Split the data into training/testing sets
diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]

# Split the targets into training/testing sets
diabetes_y_train = diabetes_y[:-20]
diabetes_y_test = diabetes_y[-20:]```

Creating Model

```# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)
```

Make Prediction

```# Make predictions using the testing set
diabetes_y_pred = regr.predict(diabetes_X_test)```

Finding Coefficient And Mean Square Error

```# The coefficients
print('Coefficients: \n', regr.coef_)
# The mean squared error
print('Mean squared error: %.2f'
% mean_squared_error(diabetes_y_test, diabetes_y_pred))
# The coefficient of determination: 1 is perfect prediction
print('Coefficient of determination: %.2f'
% r2_score(diabetes_y_test, diabetes_y_pred)) ```

Output

Coefficients: [938.23786125] Mean squared error: 2548.07 Coefficient of determination: 0.47

Plot the Result

```#Scatter Plot
plt.scatter(diabetes_X_test, diabetes_y_test,  color='black')
plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
plt.xticks(())
plt.yticks(())
plt.show()```

Output

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