Basic Features: Start with basic features that are directly available from your data.
Derived Features: Try to create new features that might be more informative. For example, if you have date/time data, you might derive features like hour of the day, day of the week, etc.
Encoding Categorical Variables: If your data includes categorical variables, decide on an encoding strategy (one-hot encoding, label encoding, etc.).
Documentation & Training
Developer README: setup, architecture diagram, API spec, testing instructions.
Runbook: recovery steps for common failure modes (stuck jobs, DB migration rollback, etc.).
User-facing guide: short how-to and FAQ for users Z.
Optional: short internal demo and recorded walkthrough.
4. Feature Generation Techniques
Polynomial Features: For numeric features, consider generating polynomial and interaction features (e.g., x1*x2, x1^2, etc.) if you have reasons to believe non-linear relationships are important.
Text Features: If you're dealing with text data, consider techniques like TF-IDF, word embeddings (Word2Vec, GloVe), or even more advanced models like BERT.
Image Features: For image data, techniques could involve pre-trained CNNs (like VGG16, ResNet50) to extract meaningful features.
Risks & Mitigations
Risk: Component B’s latency could cause timeouts — Mitigation: async processing with retries and circuit breaker.
Risk: Data inconsistency during partial failures — Mitigation: use idempotent operations and versioning; implement compensating transactions.
Risk: Unclear UX causing user mistakes — Mitigation: user testing for the UI flow and clearer inline help.
Risk: Release causes performance regression — Mitigation: staged rollout + feature flag + load test baseline.
Common diagnostics
LED indicators: Power (solid), Status (blinking = running, rapid = fault).
Multimeter checks: Verify VCC rails and key voltages.
Loopback test: For UART, short TX→RX and send data to confirm adapter and port.
I2C scanner: Run a bus scan to detect attached I2C devices.
2. Exploratory Data Analysis (EDA)
Perform EDA to understand the distribution of your data, correlations between different variables, and identify any missing values.