Introduction to Supervised Learning

Python Example: Simple Linear Regression

Let’s build a simple linear regression model using scikit-learn.

# Install required libraries if not already installed:
# pip install scikit-learn matplotlib

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt

# Sample dataset: Years of experience vs. Salary
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([30000, 35000, 40000, 45000, 50000])

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Display predictions
print("Predicted salaries:", y_pred)

# Plotting the results
plt.scatter(X, y, color='blue', label='Actual Data')
plt.plot(X, model.predict(X), color='red', label='Regression Line')
plt.xlabel("Years of Experience")
plt.ylabel("Salary")
plt.title("Simple Linear Regression")
plt.legend()
plt.show()

Output -

Predicted salaries: [35000.]

This is a basic example that demonstrates how a supervised learning algorithm (in this case, linear regression) learns from labeled data and makes predictions.

Tips for Beginners

• Start by understanding how data is structured in supervised learning: input features (X) and target outputs (y).
• Use small, easy-to-understand datasets for practice.
• Use visualization tools like Matplotlib to see how models make predictions.

Tips for Professionals

• Explore advanced evaluation metrics: R², MAE, RMSE for regression; Precision, Recall, F1-score for classification.
• Preprocess your data properly (e.g., normalization, encoding categorical variables).
• Use cross-validation to prevent overfitting and ensure robust performance.
• Understand model assumptions and hyperparameters to tune for better results.

Summary

• Supervised learning requires labeled data.
• It includes regression and classification tasks.
• It is used in a wide range of applications across industries.
• Simple tools like scikit-learn make it easy to get started with Python.