Upload fake_news_model.py

fake_news_model.py

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+import nltk
+import tensorflow as tf 
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Embedding, Dense
+from tensorflow.keras.layers import LSTM, Activation, SpatialDropout1D
+from tensorflow.keras.preprocessing.text import one_hot
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from sklearn.model_selection import train_test_split
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+import matplotlib.pyplot as plt 
+import pandas as pd 
+import numpy as np 
+import re
+
+train_dir = "data/train.csv"
+df = pd.read_csv(train_dir)
+
+df = df.dropna()
+df = df.reset_index()
+X = df.drop(labels=['label', 'id'], axis=1)
+y = df['label']
+
+xdata = X.copy()
+xdata.reset_index(inplace=True)
+
+lemmatizer = WordNetLemmatizer()
+stop_words = stopwords.words('english')
+
+xtitle = []
+for i in range(len(xdata)):
+	sent = re.sub('[^a-zA-Z]', ' ', xdata['title'][i])
+	sent = sent.lower().split()
+	sent = [lemmatizer.lemmatize(word) for word in sent if word not in set(stop_words)]
+	sent = " ".join(sent)
+	xtitle.append(sent)
+
+vocab_size = 5000
+embedding_feature_len = 30
+max_sent_len = 20
+batch_size = 32
+epochs = 10
+
+one_hot_representation = [one_hot(words, vocab_size) for words in xtitle]
+padded_sequences = pad_sequences(one_hot_representation, truncating="post",
+				padding="post", maxlen=max_sent_len)
+
+X = np.array(padded_sequences)
+y = np.array(y)
+
+x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = Sequential()
+model.add(Embedding(vocab_size, embedding_feature_len, input_length=max_sent_len))
+model.add(SpatialDropout1D(rate=0.2))
+model.add(LSTM(units=128))
+model.add(Dense(units=1))
+model.add(Activation("sigmoid"))
+model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
+print(model.summary())
+
+hist = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test))
+
+y_pred = model.predict_classes(x_test)