Data Science — Regression

Linear regression

from sklearn.linear_model import LinearRegression
import numpy as np

X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 5, 4, 5])

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

print(f"Coefficient: {model.coef_[0]:.4f}")
print(f"Intercept: {model.intercept_:.4f}")
print(f"R²: {model.score(X, y):.4f}")

Polynomial regression

from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression

poly = PolynomialFeatures(degree=2)
X_poly = poly.fit_transform(X)

model = LinearRegression()
model.fit(X_poly, y)

Logistic regression

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification

X, y = make_classification(n_samples=100, n_features=2, random_state=42)

model = LogisticRegression()
model.fit(X, y)

accuracy = model.score(X, y)
print(f"Accuracy: {accuracy:.4f}")

Model evaluation

from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

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

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

print(f"MSE: {mean_squared_error(y_test, y_pred):.4f}")
print(f"R²: {r2_score(y_test, y_pred):.4f}")

Mini Practice

1. Implement linear regression
2. Try polynomial regression
3. Evaluate model performance
4. Compare models

Up Next

Continue with Classification - Categorizing data.