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Developed as an advanced machine learning project, the Movie Recommendation AI aims to enhance user experience by accurately predicting sentiments from movie reviews. Leveraging the Naive Bayes classifier, this AI system achieved an impressive 92% accuracy in classifying reviews, fostering reliable recommendations for movie enthusiasts.

Key Features:

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• Sentiment Analysis with Naive Bayes: Implemented a sentiment analysis system utilizing Naive Bayes methodology. This approach resulted in a high classification rate of 92% for movie reviews, ensuring robust accuracy in gauging sentiments.

 

• Advanced Training Models: Employed a combination of bag-of-words and uni/bigram models. To tackle common issues like underflow and overfitting, Laplace smoothing techniques were strategically integrated during the training process, refining the model's predictive capabilities.

 

• Preprocessing Techniques: Experimented extensively with preprocessing strategies to enhance model performance. Utilized NLTK for stemming and stop words removal, effectively reducing false positives by an impressive 6%. This meticulous approach significantly improved the accuracy of sentiment analysis, refining the system's ability to make reliable movie recommendations.

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This project not only demonstrates proficiency in machine learning techniques but also highlights the importance of preprocessing in refining the accuracy of sentiment analysis models. The Movie Recommendation AI stands as a testament to the effective utilization of various techniques in crafting a robust solution for sentiment-based movie recommendations.

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