Skip to content

Latest commit

 

History

History
49 lines (31 loc) · 2.28 KB

Case_Study.md

File metadata and controls

49 lines (31 loc) · 2.28 KB

Case Study: Data Retrieval and Analysis of Company Information

Introduction

This project focuses on retrieving and analyzing company data from a comprehensive dataset containing information on over 5,000 companies. The goal was to provide insights into company structures, financial health, and industry classification.

Problem Statement

The challenge was to efficiently manage and analyze a large dataset that was initially over 9,000 entries, reduced to 5,000 for better performance and manageability. The objective was to extract meaningful insights regarding company operations and financial status.

Technologies Used

  • Python: For data manipulation and analysis.
  • CSV: The format used for storing the dataset.

Data Overview

The dataset comprises:

  • 5,000+ companies with detailed information on their structure and financials.
  • Key attributes include company registration number, company names, addresses, SIC codes, directors, and annual turnover.

Implementation

  1. Data Retrieval: The data was sourced from a CSV file, which was cleaned and processed to ensure accuracy.

  2. Data Processing:

  • The dataset was filtered to remove duplicates and irrelevant entries.
  • Missing values were handled appropriately to maintain data integrity.
  1. Analysis:
  • Analyzed the distribution of companies across different SIC codes to identify industry trends.
  • Calculated average annual turnover by industry to assess financial health.

Results

The analysis revealed significant insights, including:

  • The most common SIC codes among the companies.
  • Average turnover figures that provide a benchmark for financial performance in various industries.

Challenges Faced

  • Handling missing or inconsistent data entries was a significant challenge, requiring careful cleaning and validation.
  • Ensuring that the analysis remained accurate after reducing the dataset size was crucial.

Conclusion

This project successfully demonstrated the ability to retrieve, clean, and analyze company data, providing valuable insights into industry trends and company performance. Future work may include expanding the dataset or integrating additional data sources for a more comprehensive analysis.

Appendix