Natural language processing (NLP) is a branch of artificial intelligence focused on enabling computers to understand and communicate in human language through the conversion of raw text data into a more refined form. Key to this process is the cleaning of text data, which involves several steps to ensure data quality and utility. These steps include normalizing text to lowercase to avoid capitalization issues, eliminating extra spaces, and removing unwanted elements such as HTML tags, emails, URLs, accented characters, abbreviations, special symbols, and stopwords, which do not contribute to data analysis or model building. Furthermore, stemming and lemmatization are employed to reduce words to their root form, enhancing the efficiency of NLP models by decreasing vocabulary size and improving pattern recognition. These techniques collectively enable more accurate and insightful analysis of text data, thus facilitating better NLP model development.