Step 2.1 – Hands-On: Linear Regression

What Is Linear Regression?

Linear regression is a supervised learning algorithm used to predict a continuous value based on one or more input features. It finds the best-fitting straight line through the data.

Formula: y = w₁x₁ + w₂x₂ + ... + wₙxₙ + b

• y is the predicted value
• x₁, x₂, ..., xₙ are input features
• w₁, w₂, ..., wₙ are weights learned during training
• b is the bias (intercept)

Why Use It?

• Simple and interpretable
• Great for understanding how ML models learn
• Useful for forecasting, trend analysis, and performance modeling

What You’ll Build

A Python script that:

1. Generates synthetic data
2. Trains a linear regression model
3. Visualizes the result
4. Evaluates accuracy using Mean Squared Error

Sample Code

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# Generate synthetic data
X = np.random.rand(100, 1) * 10  # Feature: 0–10
y = 2.5 * X + np.random.randn(100, 1) * 2  # Label with noise

# Train model
model = LinearRegression()
model.fit(X, y)

# Predict
y_pred = model.predict(X)

# Evaluate
mse = mean_squared_error(y, y_pred)
print(f"Mean Squared Error: {mse:.2f}")

# Visualize
plt.scatter(X, y, label="Actual")
plt.plot(X, y_pred, color="red", label="Predicted")
plt.xlabel("X")
plt.ylabel("y")
plt.title("Linear Regression Fit")
plt.legend()
plt.show()

Output Visualization

This is the result of the linear regression model plotted against the actual data: