Keras predict a number, pass if within a range

New to Keras and ANN. I'm having issues with my epocs and the accuracy of my runs. The accuracy is all over the place and it has to do with the fact that I want to estimate a number. I want the test to pass if the estimated amount is say +/- 2% or something.

I can't find anything in tutorials or articles.

Any help is appreciated

Code:

seed = 7
basepath = '.'

# find the right path for batch ai vs local
outpath = os.path.join (basepath, "out")
if not os.path.exists(outpath):
    os.makedirs(outpath)

# Importing the dataset
dataset = pd.read_csv(os.path.join (basepath, 'data.csv'))

# fix random seed for reproducibility
np.random.seed(seed)

#Encode columns using label encoding 
#use a new label encoder everytime is important!
vixpercentencoder = LabelEncoder()
dataset['VIX Open Percent'] = responsetimeencoder.fit_transform(dataset['VIX Open Percent'])

fiftydayaverageencoder = LabelEncoder()
dataset['50 day average'] = suppliesgroupencoder.fit_transform(dataset['50 day average'])

twohundreddayaverageencoder = LabelEncoder()
dataset['200 day average'] = suppliessubgroupencoder.fit_transform(dataset['200 day average'])

openingencoder = LabelEncoder()
dataset['opening'] = regionencoder.fit_transform(dataset['opening'])

#routetomarketencoder = LabelEncoder()
#dataset['Route To Market'] = routetomarketencoder.fit_transform(dataset['Route To Market'])

#What are the correlations between columns and target
correlations = dataset.corr()['closing'].sort_values()

#Throw out unneeded columns 
dataset = dataset.drop('Date', axis=1)
dataset = dataset.drop('VIX Open', axis=1)
dataset = dataset.drop('VIX Close', axis=1)
dataset = dataset.drop('Ticker', axis=1)
#dataset = dataset.drop('VIX Open Percent', axis=1)

#One Hot Encode columns that are more than binary
# avoid the dummy variable trap
#dataset = pd.concat([pd.get_dummies(dataset['Route To Market'], prefix='Route To Market', drop_first=True),dataset], axis=1)
#dataset = dataset.drop('Route To Market', axis=1)

#Create the input data set (X) and the outcome (y)
X = dataset.drop('closing', axis=1).iloc[:, 0:dataset.shape[1] - 1].values
y = dataset.iloc[:, dataset.columns.get_loc('closing')].values

# Feature Scaling
sc = StandardScaler()
X = sc.fit_transform(X)

# Initilzing the ANN
model = Sequential()

#Adding the input layer
model.add(Dense(units = 8, activation = 'relu', input_dim=X.shape[1], name= 'Input_Layer'))

#Add hidden layer
model.add(Dense(units = 8, activation = 'relu', name= 'Hidden_Layer_1'))

#Add the output layer
model.add(Dense(units = 1, activation = 'sigmoid', name= 'Output_Layer'))

# compiling the ANN
model.compile(optimizer= 'nadam', loss = 'binary_crossentropy', metrics=['accuracy'])

# summary to console
print (model.summary())

#Fit the ANN to the training set
history = model.fit(X, y, validation_split = .20, batch_size = 64, epochs = 25)

# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

from Keras predict a number, pass if within a range