Linear regression with Python

Linear regression is a popular machine learning algorithm used to predict the relationship between two variables. In this case, we will use Python to implement a simple linear regression model. Step 1: Import necessary libraries and load the dataset 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Load the dataset
data = pd.read_csv('data.csv')
X = data['X'].values
y = data['y'].values

Step 2: Split the data into training and testing sets 
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 3: Fit the linear regression model to the training data and make predictions 
from sklearn.linear_model import LinearRegression

# Reshape X_train and X_test to 2D arrays
X_train = X_train.reshape(-1, 1)
X_test = X_test.reshape(-1, 1)

# Fit the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

Step 4: Visualize the results 
plt.scatter(X_test, y_test, color='blue')
plt.plot(X_test, y_pred, color='red', linewidth=2)
plt.xlabel('X')
plt.ylabel('y')
plt.title('Linear Regression')
plt.show()

By following these steps, you can easily implement a simple linear regression model in Python and visualize the results.

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