jugalbandi /bjp
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| import pandas as pd | |
| import numpy as np | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.naive_bayes import MultinomialNB | |
| from sklearn.metrics import classification_report | |
| df = pd.read_csv('spam.csv', encoding="latin-1") | |
| df.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True) | |
| df['label'] = df['class'].map({'ham': 0, 'spam': 1}) | |
| X = df['message'] | |
| y = df['label'] | |
| cv = CountVectorizer() | |
| X = cv.fit_transform(X) # Fit the Data | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) | |
| #Naive Bayes Classifier | |
| clf = MultinomialNB() | |
| clf.fit(X_train,y_train) | |
| clf.score(X_test,y_test) | |
| y_pred = clf.predict(X_test) | |
| print(classification_report(y_test, y_pred)) |
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