Course curriculum

  • 1

    1 - Introduction & Problem Specification

  • 2

    2 - Data Collection and Data Management

  • 3

    3 - Model Building

    • 3.1 - Setting up Train and Test Splits

    • 3.2 - The 'Dummy' Estimator

    • 3.3 - The 'Dummy' Estimator with Scikit-learn

    • 3.4 - Automating the Process

    • 3.5 - The Generalised Linear Model

    • 3.6 - Random Forest Model

  • 4

    4 - Conclusion

    • 4.1 - Visualisations

    • 4.2 - Conclusion

    • 4.3 - Next Steps

  • 5

    5. Appendix & Further Reading

    • Blank Python Notebook (Optional)