AI Case Study Regression Help
Budget: $250 – $750 USD
I’m midway through an AI-focused case study that poses four separate questions on tackling a categorical-data regression problem. The work is not tied to a particular industry such as healthcare, finance, or marketing; it sits squarely in the broader AI domain itself, with the goal of demonstrating sound methodology, clear reasoning, and reproducible results.
Here’s what I need from you:
• Four concise, fully worked-through answers—one per question—covering data preparation, model choice, training, evaluation, and interpretation.
• Any Python code (NumPy, pandas, scikit-learn, or similar) used to reach the answers, commented well enough for me to rerun and tweak.
• A brief explanation of why regression on purely categorical features was approached the way you did (encoding method, handling of multicollinearity, metrics chosen, etc.).
Acceptance criteria
1. Each answer directly resolves the stated question and references the relevant part of the case study.
2. Code executes error-free on a standard Jupyter notebook and produces the same figures or metrics you cite.
3. Assumptions and limitations are noted so I can discuss them during the final presentation.
If you’re comfortable explaining regression techniques on categorical data and enjoy crafting clear, defensible analyses, let’s get started.
Here’s what I need from you:
• Four concise, fully worked-through answers—one per question—covering data preparation, model choice, training, evaluation, and interpretation.
• Any Python code (NumPy, pandas, scikit-learn, or similar) used to reach the answers, commented well enough for me to rerun and tweak.
• A brief explanation of why regression on purely categorical features was approached the way you did (encoding method, handling of multicollinearity, metrics chosen, etc.).
Acceptance criteria
1. Each answer directly resolves the stated question and references the relevant part of the case study.
2. Code executes error-free on a standard Jupyter notebook and produces the same figures or metrics you cite.
3. Assumptions and limitations are noted so I can discuss them during the final presentation.
If you’re comfortable explaining regression techniques on categorical data and enjoy crafting clear, defensible analyses, let’s get started.