Create naziya ybi

naziya ybi6

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import accuracy_score, classification_report
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import GridSearchCV
+
+# Load the dataset
+data = pd.read_csv('reviews.csv')
+
+# Preprocess the data (you can expand this as needed)
+data['ReviewText'] = data['ReviewText'].str.lower()  # Convert to lowercase
+data['ReviewText'] = data['ReviewText'].str.replace(r'[^\w\s]', '')  # Remove punctuation
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(data['ReviewText'], data['Sentiment'], test_size=0.2, random_state=42)
+# Create a pipeline with TfidfVectorizer and MultinomialNB
+pipeline = Pipeline([
+    ('tfidf', TfidfVectorizer()),
+    ('nb', MultinomialNB())
+])
+
+# Define a grid of parameters for GridSearchCV
+param_grid = {
+    'tfidf__max_df': [0.9, 0.95, 1.0],
+    'tfidf__min_df': [1, 2, 3],
+    'tfidf__ngram_range': [(1, 1), (1, 2)],
+    'nb__alpha': [0.5, 1.0, 1.5]
+}
+
+# Use GridSearchCV to find the best parameters
+grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy')
+grid_search.fit(X_train, y_train)
+
+# Best parameters and score
+print("Best parameters: ", grid_search.best_params_)
+print("Best score: ", grid_search.best_score_)
+
+# Predict on the test set
+y_pred = grid_search.predict(X_test)
+
+# Evaluate the model
+print("Accuracy: ", accuracy_score(y_test, y_pred))
+print("Classification Report: \n", classification_report(y_test, y_pred))
+# Evaluate the model
+accuracy = accuracy_score(y_test, y_pred)
+print(f'Accuracy: {accuracy * 100:.2f}%')
+print('Classification Report:')
+print(classification_report(y_test, y_pred))