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Automating Company Data Collection Using Companies House API

Objective:

The project aimed to automate the collection of company data using the Companies House API to generate a detailed dataset for further analysis. The key focus was on identifying companies based on specific keywords and storing the data for operational purposes.

Challenges:

  • 1. Keyword-Based Filtering: Fetch data for companies relevant to specific keywords, ensuring accuracy.
  • 2. API Rate Limits and Pagination: Handle API limitations and retrieve data efficiently for thousands of companies.
  • 3. Error Handling: Implement robust mechanisms for retrying failed API requests.
  • 4. Data Deduplication: Avoid duplicate entries to maintain dataset integrity.
  • 5. Output File: Generate a structured CSV file containing essential information for each company.

Implementation:

1. Code Logic:

  • A Python script was created to interact with the Companies House API.
  • Key libraries used:
  • requests for API communication.
  • pandas for data manipulation and CSV generation.
  • time for managing delays between API calls.

2. Features of the Script:

  • Dynamic Keyword-Based Search: The script accepted multiple keywords as input to fetch data matching specified criteria.
  • Pagination Support: It managed API responses with pagination, ensuring all relevant records were retrieved.
  • Data Deduplication: Utilized a set to track and eliminate duplicate entries.
  • Retry Logic: Implemented retry logic to handle transient errors with exponential backoff for failed API calls.
  • Customizable Parameters: Allowed maximum results and retry limits to be specified.

3. Execution Steps:

  • The script iteratively called the Companies House API for each keyword.
  • Progress updates displayed the count of companies retrieved.
  • Generated a consolidated dataset and exported it as a CSV file.

Results:

1. Data Retrieval Efficiency:

  • Successfully fetched details for 850 companies using the API.
  • The process handled pagination and rate limits without interruptions.

2. Dataset Details:

  • Structured CSV output contained:
  • Company Name
  • Company Number
  • Address
  • Status (e.g., Active, Dissolved, Liquidation)

3. Execution Summary:

  • API requests executed in batches of 50 records.
  • No duplicates observed in the final dataset.
  • Clear progress tracking during execution.

Screenshots & Outputs:

1.Python Script: Displays the robust logic for API interaction, retry handling, and dataset generation.

  • Highlights reusable code structure for similar tasks.

2. Execution Logs:

  • Logs show the incremental retrieval of data in batches, confirming the script's efficiency.
  • Example log messages:
  • "Fetched details for 50 companies so far..."
  • "Fetched details for 850 companies so far..."

3. CSV Output: Contains tabular data with structured information:

  • Columns include Name, Company Number, Address, and Status.
  • Visual inspection confirms the completeness and accuracy of data.

Conclusion:

  • The automation script effectively streamlined the process of collecting company data from the Companies House API, saving significant manual effort. With its modular and reusable codebase, the solution can easily be adapted to other data collection projects requiring API integrations.

Future Enhancements:

1.Integration with Databases:

  • Store data directly into SQL databases for easier querying and analysis.

2.Advanced Filtering:

  • Incorporate additional API filters to refine results based on criteria like incorporation dates or industry codes.

3.Parallel Processing:

  • Implement threading or multiprocessing to expedite data retrieval for larger datasets.
  • This case study demonstrates a scalable and robust solution for automating data retrieval tasks, enhancing operational efficiency for business intelligence and analysis. Gallery:
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