This project performs sentiment analysis on product reviews using machine learning algorithms — Naive Bayes and Random Forest. It classifies textual reviews into positive or negative sentiments based on natural language processing (NLP) and supervised learning.

• Naive Bayes Classifier: A probabilistic model based on Bayes’ Theorem, efficient for text classification.
• Random Forest Classifier: An ensemble model that builds multiple decision trees and combines their output for better accuracy and robustness.

• Preprocessing of textual data (cleaning, tokenization, stopword removal, lemmatization)
• Feature extraction using TF-IDF
• Model training and evaluation
• Comparison of Naive Bayes and Random Forest performance
• Performance metrics: Accuracy, Precision, Recall, F1-score

• Source: Kaggle
• Attributes: Review Text, Sentiment Label (Positive/Negative)

• Python
• Pandas, NumPy
• Scikit-learn
• NLTK
• Matplotlib / Seaborn (optional for visualization)

1. Convert text to lowercase
2. Remove punctuation and special characters
3. Tokenize text into words
4. Remove stopwords
5. Lemmatize words
6. Apply TF-IDF vectorization

1. Clone the Repository

   git clone https://github.com/yourusername/sentiment-analysis-ml.git
   cd sentiment-analysis-ml