The missing middle layer between EDA and AutoML.
Deep data understanding meets model comparison -- the full journey from "What is my data?" to "Which model is best and WHY?", in as few as 3 function calls.
Quick Start | Features | Installation | Documentation | Contributing
Most data science workflows look the same: run pandas-profiling for a quick summary, switch to scikit-learn for preprocessing, try a handful of models with PyCaret or LazyPredict, then stitch SHAP plots together in a notebook. By the time you have answers, you have imported 3-5 separate libraries, written hundreds of lines of glue code, and lost the thread that connects your data findings to your modelling decisions.
DissectML (dissectml) closes that gap. It is a single, unified pipeline that
runs deep exploratory data analysis, pre-model intelligence checks (leakage
detection, readiness scoring, algorithm recommendations), a multi-model battle
arena, cross-model statistical comparison, and publication-ready HTML report
generation -- all driven by a consistent API. Three function calls replace three
notebooks.
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Unified correlation matrix -- Pearson, Cramer's V, and point-biserial correlation computed together and rendered in a single heatmap, regardless of column types.
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Missing data intelligence -- Little's MCAR test plus MAR/MNAR classification, with automatic imputation strategy recommendations tailored to each column.
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Statistical test battery -- Normality, independence, and variance tests auto-selected based on data type and sample size. No manual test selection required.
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Auto cluster discovery -- K-Means and DBSCAN with automatically tuned parameters (elbow method, silhouette scoring) to surface natural groupings in your data.
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Feature interaction and non-linearity detection -- Identifies non-linear relationships and interaction effects that linear models would miss.
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Target leakage detection -- Four-pronged analysis covering correlation leakage, mutual information leakage, temporal leakage, and derived-feature leakage.
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Data readiness score -- A 0-100 composite score with waterfall breakdown showing exactly what is dragging your data quality down (missing values, cardinality, class balance, outliers, and more).
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Algorithm recommendations -- A rules engine that maps your EDA findings (data size, feature types, non-linearity, multicollinearity) to a ranked list of recommended model families.
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36-model battle arena -- 19 classifiers and 17 regressors (plus optional XGBoost, LightGBM, and CatBoost) trained and evaluated with parallel cross-validation in a single call.
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Cross-model error analysis -- Identifies the hardest samples, builds a model complementarity matrix, and highlights where ensemble strategies could improve performance.
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Statistical significance testing -- McNemar's test for classifiers and corrected repeated k-fold paired t-test for regressors, so you know which performance differences are real.
- Publication-ready HTML reports -- Interactive Plotly charts, narrative summaries, and structured sections covering every stage of the pipeline, exportable as a single self-contained HTML file.
import dissectml as dml
# Load a built-in dataset
df = dml.load_titanic()eda = dml.explore(df)
eda.overview.show() # Shape, dtypes, memory usage
eda.correlations.heatmap() # Unified correlation matrix
eda.missing.patterns() # Missing data analysis with MCAR test
eda.outliers.plot() # Outlier detection across numeric columns
eda.clusters.summary() # Auto-discovered clustersmodels = dml.battle(df, target="survived")
models.leaderboard() # Ranked models with CV scores
models.timing() # Training time comparisonreport = dml.analyze(df, target="survived", task="classification")
report.summary() # High-level findings
report.export("report.html") # Self-contained interactive reportThe analyze function runs all five stages end-to-end: EDA, intelligence
checks, model training, cross-model comparison, and report generation. For
fine-grained control, call each stage individually.
pip install dissectmlpip install dissectml[boost] # XGBoost, LightGBM, CatBoost
pip install dissectml[explain] # SHAP explainability
pip install dissectml[report] # PDF export (WeasyPrint + Kaleido)
pip install dissectml[scale] # Polars backend + Optuna tuning
pip install dissectml[full] # Everything abovegit clone https://github.com/rupeshbharambe24/dissectML.git
cd DissectML
pip install -e ".[dev]"Requirements: Python 3.10 or later.
| Feature | DissectML | PyCaret | LazyPredict | YData Profiling |
|---|---|---|---|---|
| Deep EDA | Yes | -- | -- | Yes |
| Statistical Tests | Yes | -- | -- | Partial |
| Model Training | Yes | Yes | Yes | -- |
| Model Comparison | Yes | Yes | Partial | -- |
| SHAP Analysis | Yes | Yes | -- | -- |
| Interactive Reports | Yes | -- | -- | Yes |
| Target Leakage Detection | Yes | -- | -- | -- |
| Data Readiness Score | Yes | -- | -- | -- |
DissectML is the only library that covers the full spectrum from statistical data profiling through model comparison with a single, coherent API. Other tools excel at individual stages but leave you to bridge the gaps yourself.
DissectML is organized into five pipeline stages, each backed by a dedicated subpackage:
Stage 1: EDA dissectml.eda 9 sub-modules (overview, correlations,
missing, outliers, univariate, bivariate,
clusters, interactions, statistical_tests)
Stage 2: Intelligence dissectml.intelligence Leakage detection, multicollinearity,
feature importance, readiness scoring,
algorithm recommendations
Stage 3: Battle dissectml.battle Model catalog, preprocessing pipeline,
parallel CV runner, hyperparameter tuner
Stage 4: Compare dissectml.compare Metrics tables, significance tests,
error analysis, Pareto frontiers,
ROC/PR curves, SHAP comparison
Stage 5: Report dissectml.report Jinja2 HTML builder, narrative generator,
section renderers, PDF export
DissectML uses a global configuration object for controlling default behavior:
import dissectml as dml
# View current config
print(dml.get_config())
# Temporarily override settings
with dml.config_context(n_jobs=4, cv_folds=10):
report = dml.analyze(df, target="price")Two datasets are bundled for quick experimentation:
df_titanic = dml.load_titanic() # Binary classification (survival)
df_housing = dml.load_housing() # Regression (house prices)Full documentation, API reference, and tutorials are available at:
https://dissectml.readthedocs.io
Contributions are welcome. Please see CONTRIBUTING.md for guidelines on setting up a development environment, running the test suite, and submitting pull requests.
If you find a bug or have a feature request, please open an issue on the GitHub issue tracker.
DissectML is released under the MIT License.
Built by Rupesh Bharambe