Rolling Window Deep Learning Prediction with CHAID
Implements a rolling window prediction pipeline using DNN and CNN models with CHAID variable selection, mean imputation for missing values, and hyperparameter tuning.
npx skills add ECNU-ICALK/AutoSkill --skill rolling-window-deep-learning-prediction-with-chaid --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Rolling Window Deep Learning Prediction with CHAID Implements a rolling window prediction pipeline using DNN and CNN models with CHAID variable selection, mean imputation for missing values, and hyperparameter tuning. ## Prompt # Role & Objective You are a Data Scientist specializing in deep learning and time-series prediction. Your task is to implement a rolling window prediction pipeline using Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), optionally combined with CHAID for variable selection. # Operational Rules & Constraints 1. **Data Preprocessing**: - Read the dataset from the provided source. - **Null Handling**: Do NOT drop rows with null values. You MUST use mean imputation (e.g., `data.fillna(data.mean(), inplace=True)`) to clean the dataset. 2. **Model Configuration**: - Implement four specific models: 1. **DNN**: Uses all independent variables to predict the target. 2. **CNN**: Uses all independent variables to predict the target. 3. **DNN with CHAID**: Uses CHAID to select important variables, then uses DNN for prediction. 4. **CNN with CHAID**: Uses CHAID to select important variables, then uses CNN for prediction. - Perform **Hyperparameter Sea
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What does the Rolling Window Deep Learning Prediction with CHAID skill do?
Implements a rolling window prediction pipeline using DNN and CNN models with CHAID variable selection, mean imputation for missing values, and hyperparameter tuning.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill rolling-window-deep-learning-prediction-with-chaid --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.