Autocorrect Misspelled Word Search Engine in Python

This paper presents the development of an intelligent autocorrect system designed to enhance search engine performance by correcting misspelled user queries. Built using Python, the system leverages NLP techniques like Levenshtein Distance, fuzzy matching (via fuzzywuzzy), and probabilistic models to suggest accurate alternatives. The autocorrect engine processes queries by tokenizing them, comparing against a vocabulary, and selecting the most likely correction. Designed to be lightweight and easily integrable, the solution is ideal for real-time applications in education, e-commerce, and healthcare. The results demonstrate high accuracy for common typographical errors with potential for multilingual and domain-specific adaptations.