Solving a regresion problem using ANN, predict the Concrete compressive strength.
The solution can be found in CCS_Pytorch.ipynb and CCS_TensorKeras.ipynb with the implementation of the ANN in pytorch or tensorflow (with keras).
The Final model is a fully connected Network with an 8 node input layer, 3 hidden layers (30, 20, 10 nodes) with a relu activation and a output layer with 1 node with a sigmoid activation
| Atributes | Values |
|---|---|
| Number of instances | 1030 |
| Number of Attributes | 9 |
| Features | 8 |
| Outputs | 1 |
| Missing Attribute Values | None |
| Variable | Unit |
|---|---|
| Cement | kg in a m3 mixture |
| Blast Furnace Slag | kg in a m3 mixture |
| Fly Ash | kg in a m3 mixture |
| Water | kg in a m3 mixture |
| Superplasticizer | kg in a m3 mixture |
| Coarse Aggregate | kg in a m3 mixture |
| Fine Aggregate | kg in a m3 mixture |
| Age | 1 - 365 |
| Concrete compressive strength | MPa |
The dataset is also availabe from the source: http://archive.ics.uci.edu/ml/datasets/concrete+compressive+strength