predictive-analyticslisted

A complete, reusable methodology for end-to-end machine learning analysis of structured (tabular) data — data import and cleaning, exploratory analysis, feature engineering, classification/regression modeling, model diagnosis, clustering, interpretability, and final reporting. Use this skill WHENEVER the user wants to analyze a tabular dataset (CSV/Excel), build a predictive model (classification or regression), do EDA, engineer features, evaluate or diagnose a model, cluster/segment records, or produce a data-analysis report or thesis — even if they ask for just one piece (e.g. "build a churn model", "do EDA on this CSV", "what features should I use", "cluster my stores"). Trigger for any "I have a dataset and want to predict/understand Y" task: customer churn, sales/demand forecasting, risk/default prediction, site selection, sensor/monitoring data, and similar. Domain-agnostic; a city-noise dataset is the running example.
jiachengwang-punch/predictive-analytics-skill · ★ 1 · Data & Documents · score 72

Install: claude install-skill jiachengwang-punch/predictive-analytics-skill

# 预测分析方法论 / Predictive Analytics Methodology > A platform-neutral, language-adaptive methodology for taking any structured (tabular) dataset through a rigorous, complete machine-learning analysis. Suitable for coursework, theses, and real business analysis. ## ⚠️ HIGHEST-PRIORITY INSTRUCTION: Output language **Detect the language of the user's request and produce the ENTIRE analysis in that language** — section titles, explanations, result interpretations, chart text/labels, and the final report. If the user writes in Chinese, output everything in Chinese (including matplotlib chart labels). If in English, output in English. If in another language, match it. Code comments should also follow the user's language where practical. Do not switch languages mid-analysis. This instruction overrides any default. ## What this methodology delivers A complete analysis pipeline that turns raw tabular data into an interpretable, validated predictive model plus an honest, decision-oriented report. It covers both **classification** and **regression**, plus **unsupervised clustering** as a complementary view. It bakes in research-grade rigor: data-leakage detection, baseline comparison, cross-validation, residual diagnosis, and data-driven (not guessed) thresholds. ## The 7-stage workflow Work through these stages in order. Each has a dedicated deep-dive guide in `references/`. Read the relevant guide before executing that stage — the guides contain the concrete techniques, code patter