Why DissectML?

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.

Key Features

Exploratory Data Analysis

• Unified correlation matrix -- Pearson, Cramer's V, and point-biserial correlation computed together and rendered in a single heatmap, regardless of column types.

• Missing data intelligence -- Little's MCAR test plus MAR/MNAR classification, with automatic imputation strategy recommendations tailored to each column.

• Statistical test battery -- Normality, independence, and variance tests auto-selected based on data type and sample size. No manual test selection required.

• Auto cluster discovery -- K-Means and DBSCAN with automatically tuned parameters (elbow method, silhouette scoring) to surface natural groupings in your data.

• Feature interaction and non-linearity detection -- Identifies non-linear relationships and interaction effects that linear models would miss.

Pre-Model Intelligence

• Target leakage detection -- Four-pronged analysis covering correlation leakage, mutual information leakage, temporal leakage, and derived-feature leakage.

• 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).

• 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.

Model Comparison

• 38-model battle arena -- 19 classifiers and 19 regressors (including optional XGBoost, LightGBM, and CatBoost when the boost extra is installed) trained and evaluated with parallel cross-validation in a single call.

• Cross-model error analysis -- Identifies the hardest samples, builds a model complementarity matrix, and highlights where ensemble strategies could improve performance.

• 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.

Reporting

• 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.

Quick Start

import dissectml as dml

# Load a built-in dataset
df = dml.load_titanic()

1. Deep Exploratory Data Analysis

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 clusters

2. Model Battle Arena

models = dml.battle(df, target="survived")

models.leaderboard()          # Ranked models with CV scores
models.timing()               # Training time comparison

3. Full Pipeline (EDA + Intelligence + Battle + Compare + Report)

report = dml.analyze(df, target="survived", task="classification")

report.summary()              # High-level findings
report.export("report.html")  # Self-contained interactive report

The 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.

Installation

Core package

pip install dissectml

Optional extras

pip 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 above

Development

git clone https://github.com/rupeshbharambe24/dissectML.git
cd DissectML
pip install -e ".[dev]"

Requirements: Python 3.10 or later.

Comparison with Alternatives

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.

Architecture

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

Configuration

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")

Built-in Datasets

Two datasets are bundled for quick experimentation:

df_titanic = dml.load_titanic()    # Binary classification (survival)
df_housing = dml.load_housing()    # Regression (house prices)

Documentation

Full documentation, API reference, and tutorials are available at:

https://dissectml.readthedocs.io

Contributing

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.

License

DissectML is released under the MIT License.