import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report, confusion_matrix

df = pd.read_csv('spam.csv', encoding='latin-1')[['v1', 'v2']]

df.columns = ['label', 'text']

df['label'] = df['label'].map({'ham': 0, 'spam': 1})
X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42)
vectorizer = TfidfVectorizer(stop_words='english')
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)
model = MultinomialNB()

model.fit(X_train_tfidf, y_train)
y_pred = model.predict(X_test_tfidf)
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred))

Confusion Matrix:
 [[965   0]
 [ 37 113]]

Classification Report:
               precision    recall  f1-score   support

           0       0.96      1.00      0.98       965
           1       1.00      0.75      0.86       150

    accuracy                           0.97      1115
   macro avg       0.98      0.88      0.92      1115
weighted avg       0.97      0.97      0.96      1115

sample = ["Congratulations! You won a $1000 prize. Click here to claim."]
sample_tfidf = vectorizer.transform(sample)
print("\nPrediction:", "Spam" if model.predict(sample_tfidf)[0] == 1 else "Ham")

Prediction: Spam

from sklearn.datasets import fetch_20newsgroups
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.metrics import classification_report, confusion_matrix

categories = ['rec.sport.baseball', 'sci.space', 'comp.graphics', 'talk.politics.mideast']  
newsgroups = fetch_20newsgroups(subset='all', categories=categories, remove=('headers', 'footers', 'quotes'))
X_train, X_test, y_train, y_test = train_test_split(newsgroups.data, newsgroups.target, test_size=0.2, random_state=42)
vectorizer = TfidfVectorizer(stop_words='english', max_df=0.5)
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)

model = LinearSVC()
model.fit(X_train_tfidf, y_train)
y_pred = model.predict(X_test_tfidf)
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("\nClassification Report:\n", classification_report(y_test, y_pred, target_names=categories))

Confusion Matrix:
 [[199  13   8   0]
 [  3 169   3   3]
 [  6  12 165   6]
 [  3   9   1 179]]

Classification Report:
                        precision    recall  f1-score   support

   rec.sport.baseball       0.94      0.90      0.92       220
            sci.space       0.83      0.95      0.89       178
        comp.graphics       0.93      0.87      0.90       189
talk.politics.mideast       0.95      0.93      0.94       192

             accuracy                           0.91       779
            macro avg       0.91      0.91      0.91       779
         weighted avg       0.92      0.91      0.91       779

sample = ["NASA successfully launched a new satellite into orbit last night."]
sample_tfidf = vectorizer.transform(sample)
predicted_category = model.predict(sample_tfidf)[0]
print("\nPredicted Category:", categories[predicted_category])

Predicted Category: comp.graphics

import pandas as pd
import numpy as np
import requests
from io import BytesIO
import re
from bs4 import BeautifulSoup
import nltk
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score, classification_report

nltk.download('stopwords')
from nltk.corpus import stopwords

[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\stefi\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!