Two-Part Machine Learning Take-Home

Part 1 — Binary Classification: "Can Buy" vs "Cannot Buy"

Given applicant and market data, design a binary classifier to predict whether an applicant can buy a house (labels: can buy, cannot buy). Specify the following:

1. Data assumptions and label definition.
2. Preprocessing steps (missing values, outliers, leakage guards, encoding, scaling).
3. Feature design (engineered affordability features, credit/market features).
4. Model choice and rationale (including interpretability/calibration if applicable).
5. Validation strategy (splits, class imbalance handling, threshold selection).
6. Evaluation metrics and decision thresholding (including business-aligned metrics).

Part 2 — Regression: Wind-Farm Power Output

You are provided three files: train.csv, test.csv, sample_submission.csv. The target column is power output in train.csv. For each record in test.csv, predict power output and produce a submissions.csv with exactly two columns and a header: id, power output.

Describe your end-to-end approach:

1. Data checks and exploratory analysis (schema, leakage risks, target sanity checks).
2. Feature engineering (physics-aware, statistical, temporal, and interaction features).
3. Time-aware validation to avoid leakage (rolling/sliding windows; lag construction).
4. Model selection and tuning approach (baselines through advanced models).
5. Metrics (primary/secondary) and error analysis.
6. Training and inference pipeline, including how you would generate submissions.csv.