Creating and training a Multilayer perceptron model with few data

Question

Are there any ways to create a deep multilayer perceptron model that is capable of making accurate regression predictions based on the training done using around 1000 unique data?

I'm currently working on a Kaggle challenge for predicting the amount Followers gained using the top 1000 streamers on Twitch 2020 dataset.
- The X value would be every columns excluding Followers gained;
- The y value would be the amount of Followers gained - the model will make predictions regarding this.

In general, the data values for the amount of followers gained contain around 6 to 7 digits; currently my RMSE loss value is almost near to become 5 digits, yet still 6.
There are limited quantity of data; I'm aiming for a 5 digit RMSE value.

Here's an overview structure of my MLP model; this one showed the best result by so far. Do let me know if you have any recommendations. Thanks.

Answer 1

Looks the model is good,
If training accuracy is 100% then try increasing the dropout percentage in the initial layers or reduce some hidden layers. If training accuracy is still less than 100% then try decreasing the dropout percentage in the last few layers of nodes with count as 32.
Refer this page https://stats.stackexchange.com/questions/417055/dropping-outliers-based-on-2-5-times-the-rmse you may get more insights. Thanks