In this project, I successfully improved the address coverage rate for an insurance company from 63.39% to 92.32%. By implementing advanced data processing techniques and optimizing the address matching algorithm, I significantly enhanced the company's ability to accurately locate and identify client addresses. This improvement not only enhances operational efficiency but also contributes to better customer service and risk assessment.
The project utilizes various Python libraries and modules to achieve its objectives. These libraries are categorized based on their primary purposes:
- pandas: Data manipulation and analysis library.
- numpy: Numerical and mathematical operations.
- matplotlib: Data visualization.
- nltk (Natural Language Toolkit): NLP-specific library.
- spellchecker: Spell checking and text correction.
- difflib: Text sequence comparison.
- geotext: Library for extracting geographical locations from text.
- geopy: Geocoding and location information.
- pycountry: Country information.
- re (Regular Expressions): Text pattern matching.
- requests: Making HTTP requests.
- bs4 (Beautiful Soup): Parsing HTML and web scraping.
- fuzzywuzzy: Text similarity, which can be used in web scraping and matching.
- termcolor: Text formatting for terminal output.
- us: Handling U.S. state data.
- IPython.display: Displaying content in IPython environments.
These libraries and modules work together to preprocess and analyze data, handle text and geographical information, and perform web scraping and HTTP requests.
The data used in this project contains sensitive or private information. For this reason, I am unable to share the data files on this public repository.
I understand the importance of data transparency and reproducibility. If you wish to replicate the results or collaborate on this project, please contact me through the provided contact information or by opening an issue. I will do my best to assist you in accessing the necessary data for your research purposes.