Mani Shankar Simhadri

Sentiment analysis

• Developed a sentiment analysis model using NLP to classify user feedback into positive, negative, and neutral categories.
• Utilized Python libraries such as NLTK and scikit-learn for preprocessing, tokenization, and feature extraction.
• Implemented TF-IDF and Word2Vec embeddings to enhance model performance.
• Trained and optimized ML models including Logistic Regression, Random Forest, and SVM for sentiment prediction.
• Conducted model evaluation using precision, recall, F1-score, and ROC-AUC metrics.
• Deployed the sentiment analysis application using Streamlit for interactive user access.
• Generated visual sentiment reports to assist marketing and customer support teams.

Inventory management

• Designed and implemented an inventory management application using Python for stock monitoring and demand forecasting.
• Created interactive dashboards with Streamlit and Matplotlib for real-time inventory tracking.
• Developed automated scripts for daily stock updates and performance monitoring.
• Applied predictive analytics to forecast demand using historical sales trends.
• Optimized reorder levels to prevent stockouts and overstocking issues.
• Integrated role-based access control to ensure secure and efficient usage.
• Collaborated with stakeholders to define KPIs and enhance operational efficiency.
• Documented processes and methodologies for future reference and training.