Przejdziemy do Machine Learning – nauczymy komputery przewidywać pensje na podstawie wieku, doświadczenia i innych cech. Poznamy Linear Regression i Random Forest.
Algorytmy ML:
- Regresja liniowa
- Random Forest
- Przygotowanie danych
- Ewaluacja modeli
Wyniki modeli Machine Learning:
Kod Python – Machine Learning z scikit-learn:
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score, mean_squared_error
import numpy as np
# Przygotowanie danych
X = df[['wiek', 'doswiadczenie']] # Features
y = df['pensja'] # Target
# Podział na train/test
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Linear Regression
lr_model = LinearRegression()
lr_model.fit(X_train, y_train)
lr_pred = lr_model.predict(X_test)
lr_r2 = r2_score(y_test, lr_pred)
lr_rmse = np.sqrt(mean_squared_error(y_test, lr_pred))
print(f"Linear Regression - R²: {lr_r2:.3f}, RMSE: {lr_rmse:.0f}")
# Random Forest
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
rf_pred = rf_model.predict(X_test)
rf_r2 = r2_score(y_test, rf_pred)
rf_rmse = np.sqrt(mean_squared_error(y_test, rf_pred))
print(f"Random Forest - R²: {rf_r2:.3f}, RMSE: {rf_rmse:.0f}")
# Porównanie modeli
models = ['Linear Regression', 'Random Forest']
r2_scores = [lr_r2, rf_r2]
rmse_scores = [lr_rmse, rf_rmse]
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