import os
\nimport pandas as pd
\nimport math<\/p>\n
import stellargraph as sg
\nfrom stellargraph import StellarGraph
\nfrom stellargraph.layer import DeepGraphCNN
\nfrom stellargraph.mapper import FullBatchNodeGenerator
\nfrom stellargraph.mapper import PaddedGraphGenerator
\nfrom stellargraph.layer import GCN<\/p>\n
from tensorflow.keras import layers, optimizers, losses, metrics, Model
\nfrom sklearn import preprocessing, model_selection
\nfrom IPython.display import display, HTML
\nimport matplotlib.pyplot as plt
\n%matplotlib inline
\nfrom tensorflow.keras.layers import Dense, Conv1D, MaxPool1D, Dropout, Flatten
\nfrom tensorflow import keras<\/p>\n
#how to read data from csv files
\ndf_s = pd.read_csv(".\/data\/" + data_file, low_memory = False);
\ndf_s.head()<\/p>\n
df_s["Date"] = df_s["Date"].astype(‘datetime64[ns]’)<\/p>\n
df_s = df_s.sort_values( by = ‘Date’, ascending = True )
\ndf_s.head()<\/p>\n
df_s_transpose = df_s_transpose.dropna(axis = 1);<\/p>\n","protected":false},"excerpt":{"rendered":"
import libraries import os import pandas as pd import math Import Libraries for Graph, GNN (Graph Neural Network), and GCN (Graph Convolutional Network) import stellargraph as sg from stellargraph import StellarGraph from stellargraph.layer import DeepGraphCNN from stellargraph.mapper import FullBatchNodeGenerator from stellargraph.mapper import PaddedGraphGenerator from stellargraph.layer import GCN Machine Learning related library Imports (Tensorflow) from tensorflow.keras … <\/p>\n