Code exercise with verifiable output

Compute per-article purchase counts from transactions, identify the 75th-percentile threshold, and create a binary 'high_frequency' target in the articles dataset; return the threshold value and the count of high-frequency articles.

Apply log, Box-Cox, and Yeo-Johnson transformations to the 'price' column; return skewness values before and after each transformation.

Compute VIF for all numerical features in the articles dataset; iteratively remove the highest-VIF feature until all VIFs fall below 5; return the list of removed features and the final VIF table.

Label-encode all categorical features in the articles dataset; compute mutual information scores against the 'high_frequency' target; return the top-10 ranked features with their MI scores.

Formulate a hypothesis about a spurious correlation, then compute partial correlations controlling for a confounding variable; return the raw correlation, partial correlation, and confounder name.

Engineer degree-2 polynomial and pairwise interaction features from the top-5 MI-ranked numerical/ordinal features; train a logistic regression with and without them; return the accuracy lift.

Implement adversarial validation between stratified 50K samples of 2018 and 2019 transactions using LightGBM; return the ROC-AUC score and the top-5 most discriminative features.

Implement 5-fold target encoding with additive smoothing for 'product_group_name' predicting the 'high_frequency' target; return the encoded values for the first 10 categories and the smoothing parameter.

Compute Weight of Evidence and Information Value for 'department_name' against the 'high_frequency' target; return the IV value and its predictive power classification.

Train entity embeddings for 'product_type_name' using a shallow PyTorch neural network; return the embedding matrix shape and cosine similarity between the two most similar product types.

Apply feature hashing with 2^10 buckets to 'prod_name'; return the hashed matrix shape and the collision rate.

Aggregate raw transactions into weekly per-product sales totals; return the resulting DataFrame shape and a checksum of the sales column.

Compute rolling mean (7-day, 30-day), rolling std, and autocorrelation at lags 1, 7, and 30 for per-product weekly sales; return the feature matrix shape and the mean lag-7 autocorrelation across all products.

Encode 'month', 'day_of_week', and 'week_of_year' from transaction dates using sine/cosine transformations; return the six encoded values for January 1 and July 1.

Engineer transaction-intensity features (buy-rate, velocity, mean inter-purchase time) from raw transaction data at hourly granularity for the top-5 products; return the mean velocity per product.

Engineer document-level features from product descriptions: text length, Flesch readability score, and fashion-keyword density; return the feature matrix shape and column-wise means.

Generate document-level embeddings for all product descriptions using a pre-trained sentence-transformer; return the embedding matrix shape and cosine similarity between two specified article IDs.

Extract NER counts by category and POS-tag distributions from product descriptions using spaCy; return the top-5 NER category counts and the mean POS distribution for nouns, verbs, and adjectives.

Extract features from H&M product images using a pre-trained ResNet-50 at its final average pooling layer; return the feature tensor shape and cosine similarity between two specified article images.

Extract ViT features using a pre-trained model; compare the [CLS] embedding with mean-pooled patch embeddings for a specified image; return both embedding dimensions.

Apply PCA (to 50-d) and t-SNE (to 2-d) to the ResNet-50 embeddings for a 10K sample; return the cumulative explained variance at 50 components.

Extract handcrafted image features (256-bin HSV color histogram and Canny edge density) for a 10K-image subset; return the resulting feature matrix shape.

Using same-product-group as ground truth, evaluate retrieval precision@5 for handcrafted features vs ResNet-50 embeddings on the 10K subset; return both precision scores.

Train a standard autoencoder on the H&M tabular feature matrix; return reconstruction MSE after 50 epochs and the bottleneck dimensionality.

Train a Variational Autoencoder (VAE) on the same tabular features; return reconstruction loss, KL divergence, and total ELBO loss after 50 epochs.

Interpolate between two product latent vectors in VAE space at 5 equally spaced points; decode each and return the reconstructed feature vectors at all 5 points.

Train a TabNet model for product-group classification; return validation accuracy and the feature-attention masks for 3 specified articles.

Train a 1D CNN on the tabular features for the same classification task; return validation accuracy and a comparison with TabNet.

Implement VIME self-supervised pre-training on the tabular features; return the mask-estimation accuracy and the pre-trained encoder embedding matrix shape.

Set up an MLflow benchmarking harness on DagsHub to evaluate autoencoder and VAE features on product-group classification; return the experiment URL, all run IDs, and accuracies.

Evaluate TabNet, 1D CNN, and VIME features in the MLflow harness; compile a final comparison table across all deep learning methods; return the full comparison table.

Apply chi-squared, ANOVA F-value, and mutual information filter methods on a 50K sample of the full feature set; return the top-20 features from each method and their three-way overlap count.

Run Recursive Feature Elimination with a Random Forest on a 50K sample of the feature set; return the optimal feature count and the selected feature names.

Compute Lasso regularization paths across 50 alpha values on a 50K sample; return the alpha at which 50% of coefficients are zero and the 10 most robust features.

Compute permutation importance from a trained Random Forest on a 50K sample; return the top-10 features.

Compute SHAP importance values from the same Random Forest; return the top-10 features and the top-3 SHAP interaction pairs.

Perform agglomerative hierarchical clustering on the correlation matrix of the top-50 features from prior selection steps; return the cluster labels and a representative feature from each cluster.

Run null-importance sampling with 100 target permutations on a 50K sample; return the count of features passing at p < 0.05.

Synthesize results from all selection methods to create a final feature set; save the feature names and the corresponding data as a DVC-tracked Parquet file on DagsHub; return the DagsHub link to the .dvc file.

Implement early fusion (concatenation) of tabular, text, and image features for product-group classification on a 30K sample; return accuracy and F1-score.

Implement late fusion (averaging predictions) of models trained on tabular, text, and image features separately for the same task; return accuracy and F1-score.

Build a multimodal model combining a frozen DistilBERT (text) with a tabular MLP in PyTorch for product-group classification; return validation accuracy and total parameter count.

Build a multimodal model combining a frozen ResNet-50 (image) with a tabular MLP; return validation accuracy and comparison with the text-based model.

Implement cross-modal attention between image patch embeddings and text token embeddings in PyTorch; return the attention matrix shape for a specified article.

Compile a comparison table of all fusion strategies with accuracy, F1, and parameter count; select the best-performing model for deployment.

Fine-tune the selected best model on the full training set; log the final model artifact to MLflow on DagsHub; return the MLflow run ID and final validation accuracy.

Design the feature store architecture: define entities (customer, article), data sources, and feature groups; return the architecture as a structured JSON document.

Categorize every engineered feature as offline-only or online-required based on recommendation-serving latency needs; return the categorization dictionary.

Set up a Feast project: create feature_store.yaml pointing to a local file source, define entity schemas and data source configs; return the full project tree and YAML contents.

Register Feast FeatureViews for the tabular and temporal feature groups; return the Python definitions and the output of `feast feature-views list`.

Register Feast FeatureViews for the text, image, and final selected feature groups; return the updated complete list of all registered views.

Build a point-in-time-correct training dataset using `get_historical_features` joining customer and article features with transaction timestamps; return the dataset shape.

Materialize features to the online store (SQLite) and benchmark online retrieval for a customer-article pair; return the mean latency over 100 calls.

Integrate Feast online retrieval into the Flask /recommend endpoint to fetch live features before model scoring; return the updated endpoint code.

Orchestrate the tabular, temporal, and NLP feature engineering steps as a Prefect flow with one task per feature group; run end-to-end; return the flow-run URL and task durations.

Extend the Prefect flow to orchestrate the CV, deep feature, and selection pipelines, creating a full batch feature pipeline; return the updated flow-run URL.

Add a Prefect task that commits and pushes all engineered feature Parquet files to DagsHub via DVC upon successful pipeline completion; return the task code and a DagsHub commit link.

Initialize a Great Expectations Data Context and create a validation suite for the raw `articles.csv` with at least 5 expectations; return the JSON validation results summary.

Create a second validation suite for the final engineered feature Parquet file with at least 10 expectations (e.g., value ranges, no nulls); return the JSON results summary.

Integrate the Great Expectations validation as a task in the Prefect flow, failing the flow if validation fails; trigger a failure and return the Prefect log.

Implement data drift detection (PSI and KS test) as a Prefect task comparing a new batch of data to a reference; simulate drift and return drift scores.

Implement concept drift detection (model AUC monitoring) as a Prefect task; simulate drift by perturbing labels in a validation set and return the new AUC.

Set up a local Kafka broker and a 'transactions' topic using a Docker Compose file; write a Python producer script to send transaction events; return container status and the first 5 JSON messages.

Implement a tumbling-window aggregation over the Kafka stream in Spark Structured Streaming to compute total purchases per customer in 10-minute windows; return aggregated values for a test customer.

Implement a sliding-window aggregation (10-minute windows, 5-minute slide) over the same stream to compute rolling purchase counts; return aggregated values for the same test customer.

Implement a session-window aggregation to compute session duration and per-session purchase count with a 30-minute inactivity gap; return values for a test customer.

Add event-time watermarking to the streaming job to handle late data; inject 50 deliberately out-of-order events; return the watermark progression log and the count of late events dropped.

Extend the Spark consumer to compute real-time features: rolling 1-hour purchase count, average basket size, and seconds-since-last-purchase.

Write the computed real-time features from Spark Streaming to the Feast online store; return confirmation that features for 3 customers have been updated after processing 1000 events.

Run cProfile on the end-to-end batch feature pipeline; identify the top bottleneck function by cumulative time; return the profiling summary for that function.

Rewrite the bottleneck function using vectorized NumPy/Pandas operations or Numba; profile memory with `memory_profiler`; return wall-clock time and peak memory before and after.

Parallelize a significant portion of the feature computation (e.g., across product categories) using Python's `multiprocessing`; return the speedup factor versus sequential execution.

Containerize the feature pipeline and Flask backend with Docker; create a `docker-compose.yml` orchestrating the pipeline, backend, and MongoDB services; return `docker-compose ps` output.

Wire the deployed model into the Flask /recommend endpoint, which now uses Feast for feature retrieval; return the top-5 recommendations for 3 specified customers and the mean response latency.

Configure a GCP project with Artifact Registry; write a `cloudbuild.yaml` that builds and pushes the backend Docker image triggered on GitHub push; return the YAML contents.

Deploy the Flask backend application to GCP Cloud Run via the CI/CD pipeline.

Run smoke tests on the live /health and /recommend endpoints on Cloud Run; return the live application URL and the smoke-test response JSONs.