data-visualizer

# Data Visualizer

## Overview

Automated visualization generation for exploratory data analysis, model performance reporting, and stakeholder communication. Creates publication-quality plots, interactive dashboards, and business-friendly reports—all integrated with SpecWeave's increment workflow.

## Visualization Categories

### 1. Exploratory Data Analysis (EDA)

**Automated EDA Report**:
```python
from specweave import EDAVisualizer

visualizer = EDAVisualizer(increment="0042")

# Generates comprehensive EDA report
report = visualizer.generate_eda_report(df)

# Creates:
# - Dataset overview (rows, columns, memory, missing values)
# - Numerical feature distributions (histograms + KDE)
# - Categorical feature counts (bar charts)
# - Correlation heatmap
# - Missing value pattern
# - Outlier detection plots
# - Feature relationships (pairplot for top features)
```

**Individual EDA Plots**:
```python
# Distribution plots
visualizer.plot_distribution(
    data=df['age'],
    title="Age Distribution",
    bins=30
)

# Correlation heatmap
visualizer.plot_correlation_heatmap(
    data=df[numerical_columns],
    method='pearson'  # or 'spearman', 'kendall'
)

# Missing value patterns
visualizer.plot_missing_values(df)

# Outlier detection (boxplots)
visualizer.plot_outliers(df[numerical_columns])
```

### 2. Model Performance Visualizations

**Classification Performance**:
```python
from specweave import ClassificationVisualizer

viz = ClassificationVisualizer(increment="0042")

# Confusion matrix
viz.plot_confusion_matrix(
    y_true=y_test,
    y_pred=y_pred,
    classes=['Negative', 'Positive']
)

# ROC curve
viz.plot_roc_curve(
    y_true=y_test,
    y_proba=y_proba
)

# Precision-Recall curve
viz.plot_precision_recall_curve(
    y_true=y_test,
    y_proba=y_proba
)

# Learning curves (train vs val)
viz.plot_learning_curve(
    train_scores=train_scores,
    val_scores=val_scores
)

# Calibration curve (are probabilities well-calibrated?)
viz.plot_calibration_curve(