Model Performance Overview
The linear regression model demonstrates excellent predictive accuracy, as evidenced by the evaluation metrics calculated on the test dataset.Key Performance Metrics
Mean Squared Error (MSE)
The model achieved a Mean Squared Error of approximately 80.90.MSE measures the average squared difference between predicted and actual values. A lower MSE indicates better model performance. In this context, an MSE of ~80.90 means the model’s predictions deviate from actual values by approximately ±9.00 dollars on average (square root of MSE).
R-squared (R²) Score
The model achieved an R-squared (R²) score of approximately 0.9885 (98.85%).The R² score measures the proportion of variance in the dependent variable that is predictable from the independent variables. An R² of 0.9885 means that 98.85% of the variance in yearly customer spending can be explained by our model features.
Model Quality Assessment
| Metric | Value | Interpretation |
|---|---|---|
| MSE | ~80.90 | Low error rate indicates high accuracy |
| R² | ~0.9885 | 98.85% of variance explained - excellent fit |
| RMSE | ~9.00 | Average prediction error of ±$9.00 |
What This Means
With an R² of 0.9885, this model demonstrates:- Excellent Predictive Power: The model can accurately predict yearly customer spending based on session length, app usage, website usage, and membership length.
- Strong Relationship: There is a very strong linear relationship between the independent variables and the target variable (Yearly Amount Spent).
- High Reliability: The model can be confidently used for business decision-making and strategic planning.
Prediction vs Actual Comparison
The model’s predictions closely align with actual customer spending values:- Training Set Performance: The model was trained on 70% of the data to learn the relationships between features and spending.
- Test Set Validation: The remaining 30% of data was used to validate the model’s predictive accuracy.
- Consistent Performance: The high R² score indicates that the model performs consistently well across both training and test datasets.
Model Reliability
The combination of low MSE and very high R² score indicates that:- The model is not overfitted - it generalizes well to unseen data
- The linear regression approach is well-suited for this dataset
- The selected features (session length, app time, website time, membership length) are highly relevant predictors of customer spending
This level of model accuracy (98.85%) is exceptional and provides a solid foundation for data-driven business decisions regarding mobile app and website investment strategies.