Linear Regression in Python

Linear regression is a statistical method used to model the relationship between one or more independent variables and a dependent variable. The goal of linear regression is to find a linear relationship between the independent and dependent variables.

Linear regression is widely used in many disciplines, including business, science, and engineering. It can predict future trends and outcomes based on historical data and identify relationships between variables.

In this article, we are gonna to see the linear regression in Python:

Load the necessary libraries

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

Load the diabetes dataset

diabetes = load_diabetes()

Use only one feature for simplicity (e.g., BMI)

X = diabetes.data[:, np.newaxis, 2]

Split the data into training and testing sets

X_train = X[:-20]
X_test = X[-20:]
y_train = diabetes.target[:-20]
y_test = diabetes.target[-20:]

Create a linear regression object

model = LinearRegression()

Train the model using the training sets

model.fit(X_train, y_train)

Make predictions using the testing set

y_pred = model.predict(X_test)

Calculate accuracy measures

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print("Mean squared error:", mse)
print("R-squared:", r2)

Plot the results

plt.scatter(X_test, y_test, color='black')
plt.plot(X_test, y_pred, color='blue', linewidth=3)
plt.xlabel("BMI")
plt.ylabel("Disease Progression")
plt.title("Linear Regression - Diabetes Dataset")
plt.show()

The diabetes inbuilt dataset is imported from the 'sklearn.datasets. The data preprocessing, creating a linear regression object, fitting the model to the data, and making predictions using the test dataset. After these, the coefficients and intercept and the mean squared error and R - Squared value are used to evaluate the model's accuracy. 

Finally, we plot the data points and the regression line. You can replace 'data.csv' with the actual name of your dataset file.

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