[Python] How to Accurately Match Company Names and Addresses to Identify Duplicate Companies?...

I am working on a project where I need to determine if two company records represent the same company or not. The data I have for each company includes:

Company Name (which may have minor variations or spelling differences) Address Details: Area Building Number City State Pincode Phone Numbers (available in some cases, but not always)

Challenges: Inconsistent Company Names:

Examples: "ABC Pvt Ltd" vs. "ABC Private Limited" or "XYZ Corp." vs. "X.Y.Z Corporation" Spelling mistakes and abbreviations are also common.

Address Variations:The same address can be written in multiple ways. For example: "123, MG Road" vs. "123 MG Rd" "Building No. 5" vs. "Bldg 5" "Sector-15" vs. "Sec-15"

Partial Data:In some cases, phone numbers are missing or incomplete. Multiple Matching Fields:

Should I give more weight to certain fields (e.g., pincode and building number) than others?

Objective:

I want to accurately match these records to identify duplicates while minimizing false positives (incorrect matches) and false negatives (missed duplicates).

What I Have Tried:

• Exact Matching: Works poorly due to inconsistencies in names and addresses.
• Fuzzy Matching: Helps with minor spelling differences but generates false positives.
• Concatenating Address Fields: Tried concatenating all address fields into a single string, but minor variations still cause mismatches.

What I Need Help With:

• Best Approach or Algorithm: Should I use fuzzy matching, semantic similarity, or a machine learning model?
• Preprocessing Techniques: How to handle abbreviations, spelling errors, and variations in address formatting?
• Matching Strategy: Should I prioritize certain fields (e.g., pincode and building number) over others?
• Libraries or Tools: Any recommended Python/R libraries or third-party APIs that can handle this effectively?

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