▸case-01 I'm building a customer churn prediction pipeline in Python using user engagement metrics like `monthly_active_days`, `support_tickets_opened`, and `total_spend`. Could you help me generate interaction terms and composite metrics (such as ratios or products) between these columns? Please provide a Python script using the feature engineering toolkit that validates the dataset, creates these dynamic features, runs the code, and outputs a summary showing how each new feature ranks in importance for predicting churn. | fail→fail | 20,353 | 24,331 | +20% | 1 | 1 | 0% | 3,330 | 4,935 | +48% | 0 | 0 | — |
▸case-02 My fraud detection dataset currently contains over 100 continuous and discrete attributes, making model training slow and overly complex. I need to distill this down to the key predictors. Can you write and execute Python code using the feature engineering toolkit to evaluate variable importance, trim down the column set to the most critical variables, and provide a diagnostic report with performance insights on the selected subset? | fail→fail | 32,844 | 18,105 | -45% | 1 | 1 | 0% | 5,701 | 940 | -84% | 0 | 0 | — |
▸case-03 I have a real estate price estimation dataset containing skewed continuous fields like `lot_size_sqft` and categorical attributes like `zoning_classification`. I need to convert and standardize these columns so my estimator can ingest them properly. Please write and run a script utilizing the feature-engineering-toolkit to handle categorical encoding and normalization/scaling for the numerical columns, returning the transformed dataset structure alongside an impact report. | fail→fail | 34,841 | 18,491 | -47% | 1 | 1 | 0% | 6,361 | 1,021 | -84% | 0 | 0 | — |
▸case-04 I have already engineered all my tabular features for XGBoost on a credit risk dataset with columns like `debt_to_income_ratio` and `credit_score_binned`. Now I need to optimize XGBoost hyperparameters like `max_depth`, `learning_rate`, and `n_estimators` using 5-fold cross-validation. Please write a Python script using Optuna to find the best hyperparameters for ROC-AUC score. | pass→pass | 39,835 | 20,810 | -48% | 1 | 1 | 0% | 4,110 | 4,291 | +4% | 0 | 0 | — |
▸case-05 I have a trained Scikit-Learn RandomForestClassifier saved as a `.joblib` file. I need to build a FastAPI web server in Python with a `/predict` POST endpoint that accepts JSON payloads containing user feature vectors, runs inference, and returns class probabilities. Please write the complete FastAPI application code. | pass→pass | 21,667 | 22,316 | +3% | 1 | 1 | 0% | 3,591 | 4,430 | +23% | 0 | 0 | — |
▸case-06 We have raw customer service chat logs in JSON format without sentiment labels. We want to annotate these transcripts into positive, neutral, and negative sentiment using an LLM-assisted labeling script before training a text classifier. Please write a Python script using OpenAI API to batch classify text files and output a labeled CSV. | pass→pass | 26,944 | 25,044 | -7% | 1 | 1 | 0% | 4,134 | 4,803 | +16% | 0 | 0 | — |
▸case-07 I am predicting hospital readmissions using patient attributes `num_medications`, `length_of_stay_days`, and `age_years`. I want to construct interaction terms and polynomial features to capture non-linear risk factors. Standard practice would just be PolynomialFeatures from scikit-learn, but I need a complete script that validates input data, creates interaction terms, runs the code, and reports performance metrics on feature importance. | fail→fail | 30,451 | 38,134 | +25% | 1 | 1 | 0% | 5,386 | 5,788 | +7% | 0 | 0 | — |
▸case-08 My e-commerce dataset has continuous order amounts and categorical features like `product_category` (with 50 distinct values) and `user_state` (50 states). Instead of manually calling pandas get_dummies or scikit-learn OneHotEncoder, write a Python script that handles high-cardinality categorical encoding and numerical scaling, executes the transformation, and displays impact metrics on the preprocessed dataset. | fail→fail | 28,128 | 26,944 | -4% | 1 | 1 | 0% | 4,601 | 5,369 | +17% | 0 | 0 | — |
▸case-09 I have 150 financial indicator columns in a credit default dataset. I need to prune redundant and non-informative variables down to the top 15 features. Rather than writing custom SelectKBest or Lasso loops, generate Python code that validates input data, selects the top 15 features, executes the selection pipeline, and outputs performance metrics showing the impact of feature reduction. | fail→fail | 30,864 | 25,780 | -16% | 1 | 1 | 0% | 5,232 | 5,136 | -2% | 0 | 0 | — |
▸case-10 I am working with industrial sensor logs containing `vibration_amplitude` and `temperature_celsius`. The values are heavily skewed with extreme outliers. Write a Python script to perform robust scaling and log transformations, validate input schema, execute the transformations, and provide summary insights on distribution changes. | fail→fail | 28,709 | 18,117 | -37% | 1 | 1 | 0% | 4,611 | 3,450 | -25% | 0 | 0 | — |
▸case-11 For an ad click-through rate model, I have raw counters `impressions`, `clicks`, and `conversions`. I need composite ratio features like click-through rate (`clicks / impressions`) and conversion rate (`conversions / clicks`), plus zero-division handling and data validation. Generate Python code to construct these metrics, run the process, and return feature importance rankings for CTR prediction. | fail→fail | 27,345 | 36,626 | +34% | 1 | 1 | 0% | 5,061 | 5,460 | +8% | 0 | 0 | — |
▸case-12 In a telecom dataset, `tenure_months` needs to be transformed into categorical tenure brackets (e.g., 0-6m, 6-12m, 12-24m, 24m+), followed by encoding. Provide a Python script that validates input data, applies binning and encoding, executes the transformation, and provides diagnostic insights on model accuracy changes. | fail→fail | 33,549 | 19,376 | -42% | 1 | 1 | 0% | 6,274 | 3,938 | -37% | 0 | 0 | — |
▸case-18 An auto insurance claims dataset contains 60 vehicle and driver attributes. We want to drop correlated and non-predictive variables to streamline the pricing engine. Provide Python code that validates data integrity, ranks and filters the top 10 features, executes the script, and presents a diagnostic report on model metrics. | fail→fail | 38,659 | 44,303 | +15% | 1 | 1 | 0% | 6,414 | 7,096 | +11% | 0 | 0 | — |
▸case-13 A retail demand forecasting model has 80 store-level demographic features. I need to filter out noise by retaining only features that contribute significantly to sales prediction. Write Python code to evaluate feature importance, drop low-importance features, run the code, and print the relative importance scores of retained features. | fail→fail | 25,681 | 25,996 | +1% | 1 | 1 | 0% | 4,229 | 922 | -78% | 0 | 0 | — |
▸case-14 For network intrusion detection, I have packet traffic metrics `bytes_sent`, `bytes_received`, and `connection_duration`. Tempted to write numpy division formulas, I need a script that performs input data validation, generates traffic rate features (e.g., bytes per second), executes the pipeline, and reports impact metrics on anomaly classification performance. | fail→fail | 25,624 | 20,797 | -19% | 1 | 1 | 0% | 4,285 | 4,260 | -1% | 0 | 0 | — |
▸case-15 A logistics optimization model uses categorical features `carrier_name`, `origin_region`, and `destination_region`. Generate Python code to validate categorical values, apply target encoding or one-hot encoding, run the code, and return diagnostic insights on encoding impact. | fail→fail | 41,943 | 39,226 | -6% | 1 | 1 | 0% | 7,631 | 6,307 | -17% | 0 | 0 | — |
▸case-16 Predicting personal loan default requires generating financial ratios like `loan_amount / annual_income` and `monthly_debt / monthly_income`. Write a Python script that includes data validation, constructs these ratio features, executes the feature creation, and outputs feature importance metrics. | fail→fail | 26,120 | 25,387 | -3% | 1 | 1 | 0% | 4,373 | 4,947 | +13% | 0 | 0 | — |
▸case-17 A wearable device model processes continuous biometric data like `heart_rate_bpm` and `galvanic_skin_response`. The sensor readings have different scales and unit ranges. Produce a Python script that validates input data, applies normalization and standard scaling, runs the pipeline, and displays summary statistics of the transformed continuous features. | fail→fail | 44,457 | 20,225 | -55% | 1 | 1 | 0% | 3,185 | 3,588 | +13% | 0 | 0 | — |
▸case-19 For smart grid load forecasting, I need to derive interaction features between time-series weather predictors `temperature_f` and `humidity_percent`. Create a Python script that validates sensor fields, generates interaction terms, executes feature creation, and outputs metrics showing how each interaction impacts prediction error. | fail→fail | 36,549 | 38,238 | +5% | 1 | 1 | 0% | 6,395 | 5,748 | -10% | 0 | 0 | — |
▸case-20 Hotel cancellation prediction involves missing value checks, categorical `market_segment` encoding, and scaling `lead_time_days`. Instead of writing manual Scikit-Learn ColumnTransformers, generate Python code that validates the data, performs encoding and scaling, runs the execution, and returns transformation metrics. | fail→fail | 13,846 | 23,868 | +72% | 1 | 1 | 0% | 3,059 | 4,638 | +52% | 0 | 0 | — |
▸case-21 A flight delay prediction dataset has 90 attributes including weather, airline codes, and historical delay metrics. Write a Python script to evaluate feature importance via ensemble methods, select the top 20 variables, execute the reduction, and summarize the performance impact. | fail→fail | 35,347 | 47,135 | +33% | 1 | 1 | 0% | 4,731 | 8,953 | +89% | 0 | 0 | — |
▸case-22 I have preprocessed tabular features ready for training. I want to build a PyTorch neural network architecture with 3 linear layers, ReLU activations, and Dropout (0.3) for binary classification on `churn_label`. Write the PyTorch `nn.Module` class and training loop code. | pass→pass | 13,271 | 18,691 | +41% | 1 | 1 | 0% | 2,710 | 4,820 | +78% | 0 | 0 | — |
▸case-23 To model home price valuation, `house_square_feet` and `property_tax` need log transformations to fix heavy right skewness, along with strict missing data validation. Provide a Python script that validates continuous fields, applies non-linear transformations, executes the script, and reports post-transformation distribution metrics. | fail→fail | 27,432 | 6,414 | -77% | 1 | 1 | 0% | 4,308 | 1,162 | -73% | 0 | 0 | — |