load_data final.py

import tensorflow as tf
import pandas as pd
from sklearn.preprocessing import MinMaxScaler

# Load training data set from CSV file
training_data_df = pd.read_csv("sales_data_training.csv", dtype=float)

# Pull out columns for X (data to train with) and Y (value to predict)
X_training = training_data_df.drop('total_earnings', axis=1).values
Y_training = training_data_df[['total_earnings']].values

# Load testing data set from CSV file
test_data_df = pd.read_csv("sales_data_test.csv", dtype=float)

# Pull out columns for X (data to train with) and Y (value to predict)
X_testing = test_data_df.drop('total_earnings', axis=1).values
Y_testing = test_data_df[['total_earnings']].values

# All data needs to be scaled to a small range like 0 to 1 for the neural
# network to work well. Create scalers for the inputs and outputs.
X_scaler = MinMaxScaler(feature_range=(0, 1))
Y_scaler = MinMaxScaler(feature_range=(0, 1))

# Scale both the training inputs and outputs
X_scaled_training = X_scaler.fit_transform(X_training)
Y_scaled_training = Y_scaler.fit_transform(Y_training)

# It's very important that the training and test data are scaled with the same scaler.
X_scaled_testing = X_scaler.transform(X_testing)
Y_scaled_testing = Y_scaler.transform(Y_testing)

print(X_scaled_testing.shape)
print(Y_scaled_testing.shape)

print("Note: Y values were scaled by multiplying by {:.10f} and adding {:.4f}".format(Y_scaler.scale_[0], Y_scaler.min_[0]))