{{ charts.plotly_js|safe }}
PyCSR_ML · Decision Intelligence

{{ source.name }}

Automated data quality, descriptive analytics, and machine-learning benchmark

Executive overview

Rows
{{ profile.rows|comma }}
Columns
{{ profile.columns|comma }}
Missing data
{{ profile.missing_pct }}%
Duplicates
{{ profile.duplicate_rows|comma }}
Quality score
{{ profile.quality_score }}/100
Memory
{{ profile.memory_mb }} MB
Source: {{ source.path }}Loaded as: {{ source.load_mode }} Encoding: {{ source.encoding }}Generated: {{ profile.generated_at }}

Business insights

A decision-oriented layer connecting data quality, exploration, modeling, and recommended investigation.

{% for step in insights.pipeline %}{{ step }}{% if not loop.last %}→{% endif %}{% endfor %}

Key findings

    {% for item in insights.key_findings %}
  • {{ item }}
    • {% endfor %}

Modeling

{{ insights.modeling }}

Recommended investigation

    {% for item in insights.recommendations %}
  • {{ item }}
    • {% endfor %}

Interpretation boundary

{{ insights.note }}

Data quality findings

{% for item in profile.findings %}
{{ item.title }}{{ item.text }}
{% endfor %}
{% if charts.missing %}

Missing-data concentration

Hover over any bar to see its variable, missing count, and missing rate.

{{ charts.missing|safe }}
{% endif %}

Column intelligence

Types are semantic estimates in addition to the source storage dtype.

{% for col in profile.column_profiles %}{% endfor %}
ColumnSemantic typeStorageCompleteMissingUniqueKey statistics
{{ col.name }}{{ col.kind }}{{ col.dtype }} {{ col.non_null|comma }}{{ col.missing|comma }} ({{ col.missing_pct }}%){{ col.unique|comma }} {% if col.kind.startswith('numeric') %}
{% if charts.boxplots.get(col.name) %}
{{ charts.boxplots.get(col.name)|safe }}
{% endif %}
Min{{ col.stats.min }}Q1{{ col.stats.q1 }}Median{{ col.stats.median }}Q3{{ col.stats.q3 }}Max{{ col.stats.max }}
Mean {{ col.stats.mean }} · SD {{ col.stats.std }}
{% else %}top “{{ col.stats.top }}” ({{ col.stats.top_frequency }}), avg length {{ col.stats.avg_length }}{% endif %}
{% if charts.variables %}

Interactive variable explorer

Every variable is represented in a Seaborn-inspired style. Values are printed on the chart; hover over a colored bar or marker for the full tooltip. Use the toolbar to zoom, pan, or download an image.

{% for chart in charts.variables %}
{{ chart.kind }}{{ chart.html|safe }}
{% endfor %}
{% endif %} {% if charts.correlation %}

Relationship analysis

Hover over any cell for the exact variable pair and correlation. Pearson correlation indicates linear association, not causation.

{{ charts.correlation|safe }}
{% endif %}

Automatic ML benchmark

{% if ml.status == 'complete' %}
Problem
{{ ml.problem_type|title }}
Target
{{ ml.target }}
Recommended
{{ ml.best_model }}
Validation
{% if ml.cv_folds %}{{ ml.cv_folds }}-fold CV{% else %}Holdout{% endif %}
{% if ml.balance %}
Target balance
{{ ml.balance.label|title }} · {{ ml.balance.ratio }}:1
{% endif %}

{{ ml.target_reason|capitalize }}. {{ ml.sampling_note }} {{ ml.evaluation_note }}

{% if charts.models %}
{{ charts.models|safe }}
{% endif %}
{% for model in ml.models %}{% endfor %}
Model{{ ml.primary_metric }}Additional metricsFit time
{{ model.name }}{% if model.recommended %} Best{% endif %}{{ model.primary_score }}{% if model.primary_std is not none %} ± {{ model.primary_std }}{% endif %}{% for key,value in model.metrics.items() %}{{ key }}: {{ value }}{% if not loop.last %} · {% endif %}{% endfor %}{{ model.seconds }} sec
{% if ml.feature_importance %}

Feature importance

{{ ml.importance_method }}. Values are normalized across source variables.

{% if charts.feature_importance %}
{{ charts.feature_importance|safe }}
{% endif %}
{% for item in ml.feature_importance %}
{{ item.name }}
{{ '%.3f'|format(item.importance) }}
{% endfor %}
{% endif %}

Business recommendation

{{ ml.recommendation }}
{% if ml.balance %}
Target class distribution
    {% for name,count,pct in ml.balance.distribution %}
  • {{ name }}: {{ count|comma }} ({{ pct }}%)
    • {% endfor %}
{% endif %} {% if ml.dropped_features %}

Excluded identifier/constant features: {{ ml.dropped_features|join(', ') }}

{% endif %}

{{ ml.disclaimer }}

{% else %}{% endif %}

Data preview

First {{ profile.preview|length }} records; values are escaped for safe HTML rendering.

{% for name in profile.preview_columns %}{% endfor %} {% for row in profile.preview %}{% for name in profile.preview_columns %}{% endfor %}{% endfor %}
{{ name }}
{{ row[name] }}