The dataset ”[amazon alexa.tsv]” obtained from Kaggle offers a comprehensive overview of user ratings and feed�back for various Amazon Alexa products. It provides insights into how these products are perceived by customers through verified reviews and feedback.The dataset includes attributes such as product variation, review text, rating, and feedback. The dataset primarily consists of product reviews, where users provide suggestions and feedback based on the quality of the product. Reviews cover a range of products, such as charcoal fabric, walnut fabric, heater gray fabric, etc., with corre�sponding ratings reflecting user satisfaction levels. Overall, this dataset serves as a valuable resource for analyzing user sentiments, offering ratings, and gathering comments based on user feedback.Our dataset is diverse and encompasses a wide range of reviews, allowing for comprehensive sentiment analysis.

Overall, text mining and NLP techniques are used primarily during the data preprocessing stage to clean and transform the raw text data into a format suitable for machine learning models.In summary, the XGBoost classifier model is applied in the training and evaluation phase to classify text data based on the features extracted using text mining and natural language processing techniques.

In conclusion, this project underscores the significance of leveraging machine learning and NLP techniques to extract valuable insights from text data. By enabling businesses to make informed decisions and foster meaningful interactions with their customers, these techniques play a crucial role in driving success and innovation