Model Performance
Linear Regression · sklearn · Trained on 47 observations · 7 features
Model Trusted ✓
Excellent
0.9312
R² Score
93.1% of price variance explained
> 0.90 threshold met
Acceptable
$18,420
RMSE
Root Mean Square Error
~5.9% of median price
Good
$13,870
MAE
Mean Absolute Error
~4.5% of median price
All Significant
7
Feature Count
Independent variables
p < 0.01 for all features
Actual vs Predicted Prices
Points near the diagonal indicate accurate predictions
PredictionsPerfect fit line
Residuals Distribution
Predicted − Actual · Should be centered near 0
Feature Coefficients
Marginal effect on price per unit increase
Regression Equation Breakdown
Price = β₀ + β₁·Area + β₂·Bedrooms + … · Intercept: $-42,800