After exploring the dataset, it's time to build our deep neural network model, so as to predict the quality of concrete from the characteristics of its ingredients. We prepare the data before proceeding. We split the starting data into two sets: the training set and test set. The training set is used to train a classification model and the test set to test model performance.
To split the data, scikit-learn library has been used. More specifically, the sklearn.model_selection.train_test_split() function has been used. This function quickly computes a random split into training and test sets. Let's start by importing the function:
from sklearn.model_selection import train_test_split
Let's start by splitting the DataFrame into predictors and response:
Predictors = pd.DataFrame(DataScaled.iloc[:,:8])
Response = pd.DataFrame...
