Modular-Orchestration-and-Dependency-Inversion-Phase-2-

Phase 2 of the SDA Project: A Python-based data transformation and analytics engine for exploring global GDP trends.

Features

• Data Ingestion: Reads data from CSV (and optionally JSON) files.
• Data Cleaning: Cleans raw data and filters invalid entries based on ISO country codes.
• Analytics Engine: Performs various analytical tasks on GDP data, including:
  • Top 10 and Bottom 10 countries by GDP
  • GDP Growth Rate analysis
  • Average GDP by Continent
  • Total Global GDP Trend over a specified period
  • Identifying the Fastest Growing Continent
  • Finding countries with Consistent Decline in GDP
  • GDP Contribution by Continent
• Flexible Output: Supports multiple output destinations via plugins (e.g., Console output, Charts).

Configuration

The analysis is configured via config.json. You can specify:

• Input data source (type and path)
• Output destination (type like console or chart)
• Analysis parameters (continent, year, start_year, end_year, etc.)
• Specific tasks to execute

Setup and Execution

1. Install the required dependencies:

   pip install pycountry pandas matplotlib seaborn

2. Run the main script:

   python main.py