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"""Kaltstart.195 |
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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/1W1WPsxSyG7efWOHMRIcMxuKWq0GDFdtG |
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""" |
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!pip install nltk |
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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.model_selection import train_test_split |
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from sklearn.feature_extraction.text import CountVectorizer |
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from sklearn.ensemble import RandomForestClassifier |
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nltk.download('stopwords') |
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df = pd.read_csv("spam_ham_dataset.csv") |
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df['text'] = df['text'].apply(lambda x: x.replace('\r\n', ' ')) |
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df |
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df.info() |
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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[1].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) |
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clf = RandomForestClassifier(n_jobs=-1) |
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clf.fit(X_train, y_train) |
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clf.score(X_test, y_test) |
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email_to_classify = df.text.values[16] |
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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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clf.predict(X_email) |
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df.label_num.iloc[16] |
