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.
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.
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.
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.