The Google Autocomplete Project is a Python-based search system that mimics the functionality of Google's autocomplete feature. The project uses the Levenshtein Distance Algorithm to provide suggestions for user inputs based on a pre-defined dataset of search terms. The system efficiently predicts and completes user queries as they type, offering a fast and accurate user experience.

• Trie Data Structure: Utilizes a Trie for efficient insertion and search operations to handle autocomplete suggestions.
• Levenshtein Distance: Implements the Levenshtein Distance Algorithm to provide intelligent and context-aware suggestions, even when user input contains typos or misspellings.
• Auto-update Trie: Continuously updates the Trie with new search queries, adapting to changing user behavior.
• Efficient Search: Provides fast query suggestions by optimizing both the search and autocomplete operations.

• Programming Language: Python
• Algorithm: Levenshtein Distance for fuzzy matching
• Data Structure: Trie (Prefix Tree)

1. Input Processing: The system accepts user input in real-time as a query string.
2. Search Matching: It uses the Trie data structure to look up words that match the prefix of the input string.
3. Fuzzy Matching: For incomplete or incorrect entries, the Levenshtein algorithm is applied to calculate the edit distance between the user input and existing terms, providing relevant suggestions.
4. Real-Time Updates: New search terms are dynamically added to the Trie and the search space is continuously updated.