Real analyses on real data. Each one was generated by the MCP Analytics team — interactive in the browser, exportable as PDF, citable, and reproducible. Click any card to open the full live report. Want to see the same question as a fast Snapshot and a full Atlas, side by side? See the examples.
Logistic regression with feature importance, ranking the behavioral and demographic factors that predict whether a customer will churn.
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Isolation forest anomaly scoring on transaction features, surfacing the highest-risk outliers with explanation by feature contribution.
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Logistic regression on attrition drivers — tenure, role, compensation, satisfaction — with odds ratios and a ranked driver list for retention planning.
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Regression analysis on order data identifying the delivery, timing, and fulfillment factors that most affect customer satisfaction scores.
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Price elasticity analysis on transaction data, estimating demand sensitivity by product category and identifying optimal pricing thresholds.
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K-Means clustering on Recency, Frequency, Monetary value — bands customers into actionable retention and upsell segments with named profiles.
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Random forest + logistic regression on behavioral and transactional features — outputs per-customer churn probability and ranked predictive drivers.
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Log-log multiple regression on historical price and volume data — reports elasticity coefficients per category with confidence intervals.
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Statistical EDA across 28 PCA features plus amount and time — distributional comparisons, temporal heatmaps, and correlation matrices that surface the strongest fraud signals.
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SMOTE-balanced training of logistic regression, random forest, and XGBoost — reports F1, AUC, precision, recall plus a ROC diagnostic and feature-importance ranking.
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Seasonal decomposition plus ARIMA on monthly FMCG sales — produces point forecasts with prediction intervals and breaks out trend, seasonality, and residuals.
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Distributional analysis of customer LTV with segment-by-contract breakdowns — surfaces high-value and at-risk cohorts and what subscription patterns drive each.
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Exploratory analysis of IBM HR engagement and attrition data — satisfaction-by-department, tenure patterns, work-life balance, and how each dimension co-moves with attrition risk.
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Multi-class classification on patient clinical features, comorbidities, labs and vitals — predicts length-of-stay bin and ranks the strongest predictors for care planning.
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Time-series ARIMA forecasts per SKU/warehouse with seasonal components — informs reorder timing and inventory levels under varying supplier lead times.
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Supervised classification on signal statistical features — flags telemetry segments with anomalous behavior and explains them via feature contribution.
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Personality and demographic profiling against spend across categories, purchase channels, and campaign response — produces actionable customer segments for client work.
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Automated profiling: distributions, correlations, missing-data audit, and a category-by-numeric overview that lands the analyst on the meaningful patterns in minutes.
View report →Every analysis includes the same components designed for real-world decision making.
Zoom, pan, and hover over charts. Residual plots, Q-Q plots, feature importance, time-series decompositions, and correlation matrices.
R², RMSE, MAE, p-values, confidence intervals, AIC/BIC — every relevant statistic for the analysis type. Nothing hidden.
Plain-English interpretation that explains what the numbers mean. Key findings, recommendations, and actionable next steps.
APA, MLA, Chicago, BibTeX in one click — paste straight into a paper, deck, or compliance filing. Methodology and source travel with the citation.
Validated R source code is embedded in every report. A skeptical reader can rerun it and get the same answer.
Fixed seeds, Docker isolation, validated R. Same input → same output on any machine, any day, forever.
Upload a CSV or connect a live source. Describe what you want to know. The team builds, validates, and ships the report.