Make Tensorflow predictions on time series data? (Regression Model)

Question

I've tried this same exercise with a shallow Machine Learning algorithm (Ridge Regression), but I wanted to give a Neural Network a try.

I have access to raw sensor data in the following format. (+100k rows)

Timestamp    OilPressure     OilTemperature     RPM     FilterRestriction
0001         145             90                 1100    15
0002         140             92                 1100    15
0003         134             93                 1123    16
0004         143             91                 1135    14

The label for this exercise is OilPressure, and I've trained the following model on it.

model = keras.Sequential([
   layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),
   layers.Dense(64, activation='relu'),
   layers.Dense(1)
])

I split my original data set 25% and 75%, since I want to train and test the model on the original 25% an use that as the baseline for my "healthy" equipment. So I sorted the data set ascending by date and split it accordingly.

That 25% was further randomly split 80%/20% for train test.

The resulting model gives me a MSE of 3 PSI roughly...

What I would like to do now is populate the original dataframe with another column where I can compare the actual OilPressure to the predicted OilPressure. The predictions will be generated by the model trained off the 25% but will apply to the 85% that wasn't trained or tested.

I was thinking of applying a lambda function but I'm not sure if that would work, can I define some other sort of function that takes in OilTemperature, RPM and FilterRestriction, passes it to my model and spits out a PredictedOilPressure?

This is what I'm trying to achieve:

Timestamp    OilPressure     OilTemperature     RPM     FilterRestriction    PredictedOilPressure
1000         139             89                 1203    11                   141
1001         142             89                 1109    10                   142
1002         146             87                 1177    10                   147
1003         143             84                 1205    12                   144

Answer 1

Create a pandas dataframe for every record you would like to make predictions on. (X_features) and then just use the model.predict() function in order to make the predictions. save the results as a column in the dataset.

dataset['PredictedOilPressure'] = model.predict(X_features)