Sentimental_Analysis_Project

I developed a sentiment analysis model to classify text into Negative, Neutral, and Positive categories. The project involved text preprocessing, feature extraction using TF-IDF, and training an XGBoost classifier. Model performance was evaluated using accuracy, F1-score, and a confusion matrix to ensure reliability. Overall, the workflow demonstrated end-to-end implementation including data cleaning, model building, and insights visualization.

Tools & Technologies: Python, Flask, XGBoost Model, libraries(Matplotlib,NLTK,Seaborn,Scikit-learn).