Getting Started with Scikit‑Learn

Scikit‑learn is a powerful, user‑friendly Python library for machine learning. This guide walks you through the essential steps to build your first model using scikit‑learn.

1️⃣ Install the library

pip install scikit-learn

2️⃣ Load a dataset

We’ll use the classic Iris dataset.

from sklearn import datasets
iris = datasets.load_iris()
X, y = iris.data, iris.target

3️⃣ Split the data

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)

4️⃣ Choose a model

We'll train a simple Logistic Regression classifier.

from sklearn.linear_model import LogisticRegression
model = LogisticRegression(max_iter=200)

5️⃣ Train the model

model.fit(X_train, y_train)

6️⃣ Evaluate performance

from sklearn.metrics import accuracy_score
pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, pred))

🚀 Next steps

Explore data preprocessing techniques.
Try other algorithms like Random Forest or K‑Means clustering.
Read about model evaluation metrics to fine‑tune your models.