02. Data Collection

PRDTM2-785 AI Trading C2 L1 Vid2 Finding Data

Steps for Effective Data Collection in ML Projects

Embarking on a machine learning (ML) project begins with gathering data to answer your research questions. Here’s a simplified guide:

Key Stages in Data Collection:

  1. Identifying Data Sources

    • Explore options like Cloud files, accessible downloads, or APIs.
    • Recognize formats: CSV, Excel, and more can be processed effectively.

  2. Acquiring Financial Data

    • Premium Options: Chargeable brokers for real-time updates.
    • Free Alternatives: Access historical data from sources like Yahoo Finance.

  3. Macroeconomic Data Access

    • Obtain free from government or academic resources.

Tips for Data Handling:

  • Opt for CSV files over Excel for easier processing.
  • Utilize Python libraries to transform lesser-known file formats.
  • Secure an API key/token for access and protect it like a password.

Cost-efficiency

  • Free APIs exist for basic needs; extensive data use may incur costs. Efficiently manage downloads to maintain budget control.

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