In this beginner-friendly tutorial, you’ll learn how to train a machine learning model to estimate house prices using Python’s scikit-learn library.
You don’t need advanced math or ML knowledge to follow along.
✨ What You’ll Learn
- How to load sample data
- How to train a regression model
- How to make predictions
- How to evaluate model performance
🛠️ Step 1: Install scikit-learn
Open your terminal or command prompt and run:
pip install scikit-learn🛠️ Step 2: Create Your Python Script
Here is a complete example script you can use:
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# Load sample dataset
data = load_diabetes()
X = data.data
y = data.target
# 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
)
# Train the model
model = LinearRegression()
model.fit(X_train, y_train)
# Make predictions
predictions = model.predict(X_test)
# Evaluate model performance
mse = mean_squared_error(y_test, predictions)
print("Mean Squared Error:", mse)
✅ How It Works
– We use load_diabetes() as a sample dataset to simulate house pricing data.
– LinearRegression() trains a model to predict continuous values.
– train_test_split() separates data into training and testing sets.
– mean_squared_error() measures how well the model predicts unseen data.
🎉 Congratulations!
You just built your first machine learning model to predict numeric values. You can swap in real datasets later to predict house prices or other data.
👉 Ready to Learn More?
Join our beginner machine learning course and start building real-world ML projects today!