08_vibes.py

# Vibes 🌀
# Codédex

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from textblob import TextBlob

# Sample movie reviews
reviews = [
    "This movie was fantastic! Amazing, iconic",
    "I loved it!", "Amazing story line and great acting!",
    "The plot was cringe.",
    "Loved the acting! Highly recommended."
]
# Labels for the reviews
labels = ["positive", "positive", "positive", "negative", "positive"]

# (Optional) Correct any spelling mistakes in the reviews using TextBlob
corrected_reviews = [str(TextBlob(review).correct()) for review in reviews]

# Convert the text data into numerical format using CountVectorizer
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corrected_reviews)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X,labels,test_size=0.2,random_state=42)

# Create a Naive Bayes classifier
model = MultinomialNB()

# Train the model
model.fit(X_train, y_train)

# Use the trained model to make predictions on the test data
y_pred = model.predict(X_test)

# Calculate the accuracy of the model
accuracy = accuracy_score(y_test, y_pred)

# Print the accuracy
print("Accuracy:", accuracy)

if accuracy > 0.5:
  print("The vibes are great, book the tickets!")
else:
  print("The vibes are iffy")