Setting Up a Development Environment

edu Kanea > Lessons > Setting Up a Development Environment

Objective: Learn how to set up an AI development environment, access MTN’s sample datasets, and follow best practices for coding and documentation.

• Installing Python Libraries:
  • Key Libraries:
    • pandas: Data manipulation (e.g., analyzing MTN’s customer data).
    • scikit-learn: Machine learning models (e.g., churn prediction).
    • numpy: Numerical computations for network data.
  • Setup Process: Use pip to install libraries in Google Colab or local environments (e.g., pip install pandas scikit-learn).
  • MTN-Specific Example: MTN Ghana analysts install pandas to process anonymized Mobile Money transaction data for fraud detection models.
• Accessing MTN’s Sample Datasets:
  • Description: Anonymized datasets provided by MTN (e.g., call logs, transaction records, network performance metrics).
  • Access: Securely shared via MTN’s internal cloud or course platform, compliant with Ghana’s Data Protection Act.
  • Telecom Example: A dataset with customer IDs, data usage, and churn labels for training a churn prediction model.
  • MTN-Specific Example: MTN Ghana provides a sample dataset of 5G signal strength readings to optimize tower placement.
• Best Practices:
  • Version Control: Use Git to track code changes (e.g., for collaborative AI projects).
  • Documentation: Comment code and use Jupyter Notebook text cells to explain analyses.
  • Example: Documenting a Python script for churn prediction to ensure team members understand the model.
  • MTN Context: MTN Ghana’s data team documents AI models for Mobile Money fraud detection, ensuring transparency and reproducibility.