🩺 MLDoctor Dataset Report

📊 Dataset Health Score

{{ report["health"]["score"] }}/100

{{ report["health"]["status"] }}

Dataset quality is evaluated using:

  • ✔ Missing Values
  • ✔ Duplicate Rows
  • ✔ Feature Correlation
  • ✔ Outliers

🚀 Machine Learning Readiness

{{ report["ml_readiness"]["score"] }}/100

{{ report["ml_readiness"]["status"] }}

Dataset Shape

Rows : {{ report["shape"][0] }}
Columns : {{ report["shape"][1] }}

📊 Missing Values

Total Missing: {{ report["missing"]["total_missing"] }}

📄 Duplicate Rows

{% if report["duplicates"]["duplicates"] == 0 %}

✅ No Duplicate Rows Found

Your dataset does not contain duplicate records.

{% else %}

âš  Duplicate Rows Detected

Total Duplicate Rows: {{ report["duplicates"]["duplicates"] }}

{% endif %}

📋 Datatypes

Numeric Features: {{ report["datatypes"]["numeric_columns"]|length }}     Categorical Features: {{ report["datatypes"]["categorical_columns"]|length }}


{% for column, dtype in report["datatypes"]["datatypes"].items() %} {% endfor %}
Column Datatype Category
{{ column }} {{ dtype }} {% if column in report["datatypes"]["numeric_columns"] %} 🔢 Numeric {% else %} 🔤 Categorical {% endif %}

📋 Column Health

{% for item in report["column_health"] %} {% endfor %}
Column Score Status Issues
{{ item.column }} {{ item.score }}/100 {{ item.status }} {{ item.issues }}

📈 Histograms

📈 Correlation Heatmap

{% if report["correlation"]["high_correlation_pairs"] %}

Highly Correlated Features

{% for pair in report["correlation"]["high_correlation_pairs"] %} {% endfor %}
Column 1 Column 2 Correlation
{{ pair["column_1"] }} {{ pair["column_2"] }} {{ pair["correlation"] }}
{% else %}

✅ No highly correlated features found.

{% endif %}

📦 Boxplots

📦 Outlier Analysis

{% for column, info in report["outliers"].items() %} {% endfor %}
Column Outliers Lower Limit Upper Limit Status
{{ column }} {{ info["outliers"] }} {{ info["lower_limit"] }} {{ info["upper_limit"] }} {% if info["outliers"] == 0 %} 🟢 None {% elif info["outliers"] < 20 %} 🟢 Low {% elif info["outliers"] < 100 %} 🟠 Medium {% else %} 🔴 High {% endif %}

🤖 Recommended Machine Learning Models

Prediction Task: {{ report["model_recommendation"]["task"] }}

{% for model in report["model_recommendation"]["recommended_models"] %} {% endfor %}
Rank Model
{{ loop.index }} {{ model }}

🛠 Recommended Preprocessing Steps

{% for item in report["preprocessing_plan"] %} {% endfor %}
Column Action Reason
{{ item.column }} {{ item.action }} {{ item.reason }}

📌 Executive Summary

MLDoctor Report

🩺 MLDoctor Dataset Report

📊 Dataset Health Score

{{ report["health"]["score"] }}/100

{{ report["health"]["status"] }}

Dataset quality is evaluated using:

  • ✔ Missing Values
  • ✔ Duplicate Rows
  • ✔ Feature Correlation
  • ✔ Outliers

🚀 Machine Learning Readiness

{{ report["ml_readiness"]["score"] }}/100

{{ report["ml_readiness"]["status"] }}

Dataset Shape

Rows : {{ report["shape"][0] }}
Columns : {{ report["shape"][1] }}

📊 Missing Values

Total Missing: {{ report["missing"]["total_missing"] }}

📄 Duplicate Rows

{% if report["duplicates"]["duplicates"] == 0 %}

✅ No Duplicate Rows Found

Your dataset does not contain duplicate records.

{% else %}

âš  Duplicate Rows Detected

Total Duplicate Rows: {{ report["duplicates"]["duplicates"] }}

{% endif %}

📋 Datatypes

Numeric Features: {{ report["datatypes"]["numeric_columns"]|length }}     Categorical Features: {{ report["datatypes"]["categorical_columns"]|length }}


{% for column, dtype in report["datatypes"]["datatypes"].items() %} {% endfor %}
Column Datatype Category
{{ column }} {{ dtype }} {% if column in report["datatypes"]["numeric_columns"] %} 🔢 Numeric {% else %} 🔤 Categorical {% endif %}

📋 Column Health

{% for item in report["column_health"] %} {% endfor %}
Column Score Status Issues
{{ item.column }} {{ item.score }}/100 {{ item.status }} {{ item.issues }}

📈 Histograms

📈 Correlation Heatmap

{% if report["correlation"]["high_correlation_pairs"] %}

Highly Correlated Features

{% for pair in report["correlation"]["high_correlation_pairs"] %} {% endfor %}
Column 1 Column 2 Correlation
{{ pair["column_1"] }} {{ pair["column_2"] }} {{ pair["correlation"] }}
{% else %}

✅ No highly correlated features found.

{% endif %}

📦 Boxplots

📦 Outlier Analysis

{% for column, info in report["outliers"].items() %} {% endfor %}
Column Outliers Lower Limit Upper Limit Status
{{ column }} {{ info["outliers"] }} {{ info["lower_limit"] }} {{ info["upper_limit"] }} {% if info["outliers"] == 0 %} 🟢 None {% elif info["outliers"] < 20 %} 🟢 Low {% elif info["outliers"] < 100 %} 🟠 Medium {% else %} 🔴 High {% endif %}

🤖 Recommended Machine Learning Models

Prediction Task: {{ report["model_recommendation"]["task"] }}

{% for model in report["model_recommendation"]["recommended_models"] %} {% endfor %}
Rank Model
{{ loop.index }} {{ model }}

🛠 Recommended Preprocessing Steps

{% for item in report["preprocessing_plan"] %} {% endfor %}
Column Action Reason
{{ item.column }} {{ item.action }} {{ item.reason }}

📌 Executive Summary

  • Dataset Size: {{ report["shape"][0] }} rows × {{ report["shape"][1] }} columns
  • Health Score: {{ report["health"]["score"] }}/100 ({{ report["health"]["status"] }})
  • ML Readiness: {{ report["ml_readiness"]["score"] }}/100 ({{ report["ml_readiness"]["status"] }})
  • Missing Values: {{ report["missing"]["total_missing"] }}
  • Duplicate Rows: {{ report["duplicates"]["duplicates"] }}
  • Highly Correlated Features: {{ report["correlation"]["high_correlation_pairs"]|length }}
  • Columns Requiring Preprocessing: {{ report["preprocessing_plan"]|length }}
  • Recommended Task: {{ report["model_recommendation"]["task"] }}

Recommendations

    {% for item in report["recommendations"] %}
  • {{ item }}
  • {% endfor %}

Generated by MLDoctor v0.4.0

Dataset Analysis • ML Readiness • Visualization • Preprocessing Guidance

Generated by MLDoctor v0.4.0

Dataset Analysis • ML Readiness • Visualization • Preprocessing Guidance <