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))
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])

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


Predicted Category: comp.graphics

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

df = pd.read_csv('imdb_reviews.csv')
df = df[['review', 'sentiment']]
df['sentiment'] = df['sentiment'].map({'positive': 1, 'negative': 0})

X_train, X_test, y_train, y_test = train_test_split(df['review'], df['sentiment'], test_size=0.2, random_state=42)

vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)

model = LogisticRegression()
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))

sample = ["This movie was a masterpiece. I loved every minute of it!"]
sample_tfidf = vectorizer.transform(sample)
print("\nPrediction:", "Positive" if model.predict(sample_tfidf)[0] == 1 else "Negative")

Confusion Matrix:
 [[4351  610]
 [ 448 4591]]

Classification Report:
               precision    recall  f1-score   support

           0       0.91      0.88      0.89      4961
           1       0.88      0.91      0.90      5039

    accuracy                           0.89     10000
   macro avg       0.89      0.89      0.89     10000
weighted avg       0.89      0.89      0.89     10000


Prediction: Positive