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.
Input two Premier League team names and get a professional pre-match data report — win/draw/loss likelihoods, expected goals (xG), key statistical comparisons, and recent form context, based on 16 seasons of historical match data. Research 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.