Import Libraries
from keras.models import Sequential
import pandas as pd
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense, Flatten, LSTM, Conv1D, MaxPooling1D, Dropout, Activation
from keras.layers.embeddings import Embedding
from keras.preprocessing.text import text_to_word_sequenceMount Drive
from google.colab import drive
drive.mount('/content/drive')Read Data Path
loc = '/content/drive/My Drive/fake/'Read Dataset
# loading the a train set
tr = pd.read_csv(loc+'train.csv')
tr.dropna(inplace=True)
tr.head()output:
Loading Test Data
#loading the test set
ts = pd.read_csv(loc+'test.csv')
ts.dropna(inplace=True)
ts.head()Ouput:
Length of both dataset:
len(tr),len(ts)(18285, 4575)
# using only 3000 record to reduce memory usage , increase the numbers below to increase the data.
tr = tr.iloc[:2000,:]
ts = ts.iloc[:1000,:]tr.label.value_counts()len(tr)# cleaning data
tr.replace(r'\b\w{1,3}\b','', regex =True, inplace = True)neuralNetwork.fit(emb, y_tr, epochs=1)
tr.replace(r'\b\w{1,3}\b','', regex =True, inplace = True)tr.head()Output:
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