Google Places API Address Normalization Enhancement
Budget: $250 – $750 USD
Address Data Normalization & Matching Enhancement for Google Places API
Job Description:
Our company operates a tourism and transfer service platform. We have a data type called Transfer-v4, where departure and arrival locations are stored in the format "District/City" (e.g., "Beşiktaş/Istanbul").
When users enter an address using Google Places API, our system extracts district and city information from the formatted_address field and matches it with records in our Transfer-v4 database to provide pricing.
However, some addresses in Azerbaijan lack district information, causing matching failures and missing price calculations. To resolve this, we are looking for a developer to enhance our system with address normalization and automatic district completion.
Responsibilities:
Analyze Google Places API address data and develop an algorithm to fill in missing district information.
Identify missing districts and propose solutions to correctly match them.
Use GPT or alternative AI models to predict missing districts or integrate with external address databases for comparison.
Improve Google Places API address component processing to ensure that missing districts are accurately identified.
Required Skills:
Experience with Google Places API
Knowledge of address data processing and normalization
Proficiency in Python, JavaScript, or Bubble.io for system integration
Familiarity with AI-based or regex-based data completion techniques
How to Apply:
Interested candidates should submit their applications along with their portfolio of previous projects related to address processing.
Job Description:
Our company operates a tourism and transfer service platform. We have a data type called Transfer-v4, where departure and arrival locations are stored in the format "District/City" (e.g., "Beşiktaş/Istanbul").
When users enter an address using Google Places API, our system extracts district and city information from the formatted_address field and matches it with records in our Transfer-v4 database to provide pricing.
However, some addresses in Azerbaijan lack district information, causing matching failures and missing price calculations. To resolve this, we are looking for a developer to enhance our system with address normalization and automatic district completion.
Responsibilities:
Analyze Google Places API address data and develop an algorithm to fill in missing district information.
Identify missing districts and propose solutions to correctly match them.
Use GPT or alternative AI models to predict missing districts or integrate with external address databases for comparison.
Improve Google Places API address component processing to ensure that missing districts are accurately identified.
Required Skills:
Experience with Google Places API
Knowledge of address data processing and normalization
Proficiency in Python, JavaScript, or Bubble.io for system integration
Familiarity with AI-based or regex-based data completion techniques
How to Apply:
Interested candidates should submit their applications along with their portfolio of previous projects related to address processing.