A full analysis pipeline that turns a dataset into a finished report, not just charts. Hand it a CSV, Excel, Parquet, or JSON file and it cleans the data, scores its quality, runs EDA, and where the data supports it, trains a prediction model with SHAP attribution and a backtested forecast. You get a trust score, ranked findings, and recommendations as a Word doc, an interactive HTML report, and an optional PowerPoint deck. The numbers come from code, so they stay consistent, never invented.
A spreadsheet full of numbers isn't an answer — it's raw material. McKinsey taught me to find the number that matters: start from the decision you're making, cut to the one or two insights that change it, then say what to do. This Skill brings that eye to your data — cleaning and structuring it, surfacing the patterns and outliers worth caring about, and writing findings as clear takeaways. Analysis that ends in a decision, not a dashboard nobody opens.
Identifies outliers and abnormal behavior in data streams, sensor readings, and KPIs — using statistical and ML-based methods to flag genuine faults or risks while filtering out normal variation.
NBA analysis you can actually defend — built from published rosters and minutes, so every number has a reason behind it. Each run ships with its model version, data cutoff, and source season — traceable, not a black box. Player estimates for points, rebounds, assists, threes, and PRA absorb the minutes an official injury report frees up. Built for fans, fantasy players, and analysts who want a defensible read before tip-off.
Expert AI data analyst that analyzes any CSV, Excel or tabular dataset. Paste or upload data and get: 📊 Column profiling & statistics · 💡 Key patterns & anomalies · ⚠️ Anomaly detection · ✅ Data quality score · 🔗 Correlation insights · 🚀 Actionable recommendations. Works with sales, financial, survey, log, and any structured data. Educational purposes only.
A chart dump isn't insight. Paste your data — a CSV, table, or numbers — and get an analyst-grade report in three layers: WHAT the data shows (key figures), WHY it's likely happening (labeled hypotheses, not guesses as facts), and SO WHAT to do (3–5 prioritized actions + what to measure next). Delivered as a clean, self-contained HTML report. It reasons only over your numbers and never invents figures or benchmarks. For operators who need decisions, not dashboards.
プロ野球のデータを統計モデリングをAI推論で強化。試合結果とスコアを客観的かつ勝率ベースで分析し、最新情報を元に解析します。
Turn messy CSV and Excel data into reproducible, decision-ready business analysis.
You ran a test and one variant looks better. Is that a finding or a coin flip? Paste trials and successes per variant. You get Wilson 95% intervals, a two-proportion test, and the check almost nobody runs: an overdispersion gate asking whether your grouping label explains anything. If the spread inside a group beats the spread between groups, no winner is declared. A p-value near zero has died on this gate before. Post-hoc axes get labelled. Underpowered tests get a sample size.