preprocessing on data

Data Preprocessing

Performed essential preprocessing steps to improve data quality and model performance, including data cleaning, handling missing values, normalization, encoding categorical variables, and text preprocessing (tokenization, stopword removal, stemming/lemmatization). This ensured structured, consistent, and reliable input for machine learning and NLP models