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"""spamemailfinder.159 |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/1VK3x8uRt-HA3ZSip5FllNRtdqeRZ-X8y |
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""" |
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import string |
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import numpy as np |
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import pandas as pd |
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import nltk |
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from nltk.corpus import stopwords |
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from nltk.stem.porter import PorterStemmer |
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from sklearn.feature_extraction.text import CountVectorizer |
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from sklearn.model_selection import train_test_split |
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from sklearn.naive_bayes import MultinomialNB,BernoulliNB,GaussianNB |
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nltk.download('stopwords') |
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df = pd.read_csv("spam_ham_dataset.csv") |
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df.head() |
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df.info() |
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df.isna().sum() |
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df['text'] = df['text'].apply(lambda x: x.replace('\r\n', ' ')) |
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df.head() |
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stemmer = PorterStemmer() |
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corpus = [] |
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stopwords_set = set(stopwords.words('english')) |
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for i in range(len(df)): |
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text = df['text'].iloc[i].lower() |
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text = text.translate(str.maketrans('', '',string.punctuation)).split() |
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text = [stemmer.stem(word) for word in text if word not in stopwords_set] |
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text = ''.join(text) |
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corpus.append(text) |
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vectorizer = CountVectorizer() |
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X = vectorizer.fit_transform(corpus).toarray() |
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y = df.label_num |
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) |
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mnb = MultinomialNB() |
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bnb = BernoulliNB() |
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gnb = GaussianNB() |
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mnb.fit(X_train, y_train) |
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bnb.fit(X_train, y_train) |
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gnb.fit(X_train, y_train) |
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mnb.score(X_test, y_test) |
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bnb.score(X_test, y_test) |
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gnb.score(X_test, y_test) |
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email_to_classify = df.text.values[19] |
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email_to_classify |
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email_text = email_to_classify.lower().translate(str.maketrans('', '',string.punctuation)).split() |
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email_text = [stemmer.stem(word) for word in text if word not in stopwords_set] |
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email_text = ''.join(email_text) |
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email_corpus = [email_text] |
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X_email = vectorizer.transform(email_corpus) |
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mnb.predict(X_email) |
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bnb.predict(X_email) |
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gnb.predict(X_email.toarray()) |
