Update README.md

README.md

@@ -21,15 +21,18 @@ The model analyzed in this card estimates someone's age. This project has been d
 
 To determine the performance of the model, the following metrics have been used:
 - MSE, this metric measures how close the regression line is to the data points.
-<br> &ensp; - *Our model's MSE:*
+<br> &ensp; - *Our model's MSE:* 60.9
 - RMSE, this metric measures the mean error that can be made.
-<br> &ensp; - *Our model's RMSE:*
+<br> &ensp; - *Our model's RMSE:* 7.8
 - MAE, this is a measure for model accuracy. The MAE is the average error that the model's predictions have in comparison with their corresponding actual targets.
-<br> &ensp; - *Our model's MAE:*
+<br> &ensp; - *Our model's MAE:* 5.2
 
 Ideally, the RMSE and the MAE should be close to each other. When there is a big difference in these two numbers, it is an indication of variance in the individually errors.
 
-Our results show that the prediction model can be ... years off of the actual age of a person.
+Our results show that the prediction model can be around 8 years off of the actual age of a person.
+
+We also looked at how the model performs in different age, gender and race classes. It seemed the model predicted the ages of people between 20 and 30 better than the rest. The model could also predict the ages of females better than males. The race that the model can predict the best is East Asian.
+
 
 ## Limitations
 - **Lighting**
@@ -46,9 +49,9 @@ Train data: 70%
 Test data: 30%
 
 Our model has been made by trial and error. The following architecture is the outcome:
-- Hidden layers:
-- Batch size
-- Epochs: 
-- Optimizer:
-- Activation: 
+- Hidden layers: 7
+- Batch size: 128
+- Epochs: 65
+- Optimizer: adam
+- Activation: ReLu & Linear