Turn Your Pickle Files Into Data Stories.
Upload your Python artifact and let BloomEDA automatically uncover its structure, statistics, patterns, anomalies, and machine-learning insights.
The Living Data Garden
Every uploaded DataFrame, Pipeline, and Model blossoms into an interactive botanical dashboard with statistical distributions, correlation heatmaps, and outlier boundaries.
An Elegant Botanical Laboratory for Machine Learning
Designed to combine the aesthetic calm of a botanical garden with the rigorous analytical depth of a modern data science lab.
Deep Automated EDA
Instant numerical distribution histograms, quartiles, IQR boundaries, skewness, kurtosis, and categorical cardinality profiling for tabular artifacts.
Explainable Quality Scoring
No arbitrary black-box numbers. BloomEDA calculates an explainable 0–100 score detailing transparent point deductions for missing cells, duplicates, and outliers.
Pipeline Flowchart Visualizer
Transforms Scikit-Learn Pipeline steps and ColumnTransformers into clean visual graph flowcharts from raw feature inputs to final estimator models.
Zero-Trust Subprocess Isolation
Strict AST class whitelisting via RestrictedUnpickler in an isolated worker subprocess with CPU limits, memory limits, and 15-second execution timeout.
Grounded Mathematical Insights
Curated observations generated strictly from computed statistics. Detects high missingness, collinearity, ID-like cardinality, and potential target columns.
Comprehensive Report Export
Export full structured analysis schemas as JSON for automated pipelines, or generate clean CSV statistic summaries with a single click.
Don’t Have a .pkl File Handy?
Explore BloomEDA instantly with pre-curated Python artifacts. Click any sample below to experience the botanical loading garden and deep exploratory inspection.
Customer Churn DataFrame
1,012 realistic customer rows with numerical tenure, monthly charges, outliers, missing values, and churn target.
Scikit-Learn ML Pipeline
Trained Scikit-Learn Pipeline combining ColumnTransformer (StandardScaler, OneHotEncoder) with a fitted RandomForestClassifier.
Model Artifact Bundle
Nested Python dictionary container containing fitted LogisticRegression model, scaler, feature list, and evaluation metrics.