In the State-wise Stock Market Sentiment Analysis project, we used web scraping and natural language processing to understand how people feel about the stock market in different regions. By utilizing KeyBERT and WordCloud algorithms, we transformed large amounts of data into valuable insights for shaping targeted advertising strategies• Extracted and cleaned data from various platforms using Python and Pandas, ensuring high-quality datasets.• Organized keywords with their corresponding regions into .txt files, demonstrating attention to detail. • Applied the KeyBERT NLP algorithm for region-specific keyword extraction and used WordCloud and Matplotlib for visualizing keyword frequencies. • Conducted a 25-day data collection cycle with a 5-day buffer for validation, showcasing strong project management and analytical skills in machine learning.