subhikshaImputeX


NamesubhikshaImputeX JSON
Version 0.1.0 PyPI version JSON
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home_pagehttps://github.com/subi2404/SubhikshaSmartImpute
SummaryAutomatic missing value imputation with intelligent strategy selection
upload_time2025-10-12 23:14:27
maintainerNone
docs_urlNone
authorSubhiksha_Anandhan
requires_python>=3.8
licenseNone
keywords imputation missing-values machine-learning data-preprocessing pandas scikit-learn knn regression strategy-selection
VCS
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requirements numpy pandas scikit-learn
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # subhikshaImputeX

[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/license-MIT-green)](LICENSE)
[![PyPI](https://img.shields.io/badge/pypi-0.1.0-blue)](https://pypi.org/)

**Automatic missing value imputation with intelligent per-column strategy selection.**

subhikshaImputeX is a production-ready Python library that automatically detects and applies the best imputation method for each column in your dataset using cross-validation.

## ๐ŸŽฏ Key Features

โœ… **Automatic Strategy Selection** - Tests multiple imputation methods per column and selects the best  
โœ… **Multiple Strategies** - Mean, Median, Mode, KNN, Regression, Forward Fill  
โœ… **Cross-Validation** - Evaluates accuracy using known values before imputation  
โœ… **Correlation Detection** - Identifies relationships between features for smarter imputation  
โœ… **Transparent Reporting** - Shows what strategy was chosen and why  
โœ… **Lightweight** - Only depends on NumPy, Pandas, and Scikit-learn  
โœ… **Per-Column Flexibility** - Different strategies for different columns  
โœ… **Type-Aware** - Handles numeric and categorical data appropriately  

## ๐Ÿ“ฆ Installation

### From PyPI (recommended)
```bash
pip install subhikshaImputeX
```

### From source
```bash
git clone https://github.com/subi2404/subhikshaImputeX.git
cd subhikshaImputeX
pip install -e .
```

### Development setup
```bash
pip install -e ".[dev]"
```

## ๐Ÿš€ Quick Start

### Basic Usage

```python
import pandas as pd
from subhikshaImputeX import SmartImputer

# Load data with missing values
df = pd.read_csv('data.csv')

# Create and fit imputer
imputer = SmartImputer(evaluation=True, verbose=True)
df_clean = imputer.fit_transform(df)

# Print report
imputer.print_report()
```

### Advanced Usage

```python
from subhikshaImputeX import SmartImputer

# Custom configuration
imputer = SmartImputer(
    strategy='auto',              # Auto-select best strategy per column
    evaluation=True,              # Evaluate strategies via cross-validation
    n_splits=5,                   # 5-fold cross-validation
    detect_correlations=True,     # Detect feature correlations
    verbose=True,                 # Print progress
    random_state=42               # Reproducibility
)

# Fit on training data
imputer.fit(df_train)

# Transform train and test
df_train_clean = imputer.transform(df_train)
df_test_clean = imputer.transform(df_test)

# Get detailed report
report = imputer.get_report()
print(report)
```

### Using Specific Strategies

```python
from subhikshaImputeX import SmartImputer

# Use only mean imputation
imputer = SmartImputer(strategy='mean')
df_clean = imputer.fit_transform(df)

# Available strategies: 'mean', 'median', 'mode', 'knn', 'regression', 'forward_fill'
```

### Manual Strategy Selection

```python
from subhikshaImputeX import (
    MeanImputer, 
    KNNImputation, 
    RegressionImputer
)

# Use custom strategy directly
imputer = MeanImputer()
imputer.fit(df['column'])
df['column'] = imputer.transform(df['column'])
```

## ๐Ÿ“Š Available Strategies

| Strategy | Type | Best For | Pros | Cons |
|----------|------|----------|------|------|
| **Mean** | Numeric | Quick baseline | Fast, simple | Loses variance |
| **Median** | Numeric | Robust imputation | Handles outliers | Less variance |
| **Mode** | Categorical | Most frequent | Interpretable | Information loss |
| **KNN** | Both | Local patterns | Considers similarity | Slow on large data |
| **Regression** | Numeric | Feature relationships | Preserves correlations | Assumes linearity |
| **Forward Fill** | Time series | Sequential data | Context-aware | Assumes order |

## ๐Ÿ” How It Works

### 1. Automatic Strategy Selection
For each column with missing values:
- Identifies data type (numeric or categorical)
- Selects applicable strategies
- Evaluates each using cross-validation
- Chooses best performer

### 2. Cross-Validation Evaluation
- Randomly masks known values
- Applies strategy to predict masked values
- Compares predictions to true values
- Calculates RMSE (numeric) or accuracy (categorical)
- Repeats across multiple splits

### 3. Correlation Detection
- Identifies relationships between features
- Prioritizes correlated features for regression/KNN
- Provides feature importance ranking

## ๐Ÿ“ˆ Performance

### Evaluation Metrics

**Numeric Columns:**
- Uses RMSE (Root Mean Squared Error)
- Lower RMSE = Better imputation

**Categorical Columns:**
- Uses Accuracy
- Higher accuracy = Better imputation

### Cross-Validation
- Default: 5-fold cross-validation
- Configurable via `n_splits` parameter
- Prevents overfitting to training data

## ๐Ÿ’ก Examples

### Example 1: Auto Imputation with Report

```python
import pandas as pd
from subhikshaImputeX import SmartImputer
import numpy as np

# Create sample data
df = pd.DataFrame({
    'age': [25, np.nan, 35, 45, np.nan, 30],
    'income': [50000, 60000, np.nan, 80000, 90000, np.nan],
    'category': ['A', 'B', np.nan, 'A', 'C', 'B']
})

print("Before imputation:")
print(df)
print("\nMissing values:")
print(df.isnull().sum())

# Impute
imputer = SmartImputer(evaluation=True, verbose=True)
df_clean = imputer.fit_transform(df)

print("\n\nAfter imputation:")
print(df_clean)

# Report
imputer.print_report()
```

### Example 2: Train-Test Split

```python
import pandas as pd
from subhikshaImputeX import SmartImputer

# Load data
df = pd.read_csv('data.csv')

# Split
train = df.iloc[:800]
test = df.iloc[800:]

# Fit on train, transform both
imputer = SmartImputer(evaluation=True)
imputer.fit(train)

train_clean = imputer.transform(train)
test_clean = imputer.transform(test)

# Use for model training
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()
model.fit(train_clean.drop('target', axis=1), train_clean['target'])
```

### Example 3: Correlation Analysis

```python
from subhikshaImputeX import CorrelationDetector
import pandas as pd

df = pd.read_csv('data.csv')

# Detect correlations
detector = CorrelationDetector(min_correlation=0.5)
correlations = detector.detect(df)

print("Strong correlations:")
for col, corrs in correlations.items():
    print(f"\n{col}:")
    for corr_col, corr_val in corrs[:3]:  # Top 3
        print(f"  {corr_col}: {corr_val:.3f}")

# Visualize (requires matplotlib, seaborn)
# detector.plot_correlation_heatmap()
```

## ๐Ÿ”ง Configuration

### SmartImputer Parameters

```python
SmartImputer(
    strategy='auto',              # Strategy selection mode
                                  # Options: 'auto', 'mean', 'median', 'mode', 'knn', 'regression', 'forward_fill'
    
    evaluation=True,              # Enable cross-validation evaluation
    
    n_splits=5,                   # Number of CV folds for evaluation
    
    detect_correlations=True,     # Detect feature correlations
    
    verbose=True,                 # Print progress and results
    
    random_state=42               # Random seed for reproducibility
)
```

## ๐Ÿ“‹ API Reference

### SmartImputer

**Methods:**
- `fit(X)` - Fit on training data
- `transform(X)` - Apply imputation
- `fit_transform(X)` - Fit and transform
- `get_report()` - Get imputation report (dict)
- `print_report()` - Print formatted report

### CorrelationDetector

**Methods:**
- `detect(df)` - Find correlations
- `get_correlated_features(column, top_n=3)` - Get correlated features
- `get_correlation_pairs()` - Get all correlation pairs
- `get_feature_importance_for_imputation(column, df)` - Rank features
- `plot_correlation_heatmap()` - Visualize correlations

### Individual Strategies

All strategies follow the scikit-learn API:
- `fit(series, X=None)`
- `transform(series)`
- `fit_transform(series, X=None)`

## ๐Ÿค Contributing

Contributions are welcome! Here's how:

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Make your changes
4. Run tests (`pytest tests/`)
5. Commit changes (`git commit -m 'Add amazing feature'`)
6. Push to branch (`git push origin feature/amazing-feature`)
7. Open a Pull Request

### Development Setup

```bash
git clone https://github.com/subi2404/subhikshaImputeX.git
cd subhikshaImputeX
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e ".[dev]"
pytest tests/
```

## ๐Ÿงช Testing

Run the test suite:

```bash
pytest tests/                    # Run all tests
pytest tests/ -v                 # Verbose output
pytest tests/ --cov              # With coverage report
```

## ๐Ÿ“ License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## ๐Ÿ™‹ Support

- **Issues**: [GitHub Issues](https://github.com/subi2404/subhikshaImputeX/issues)
- **Discussions**: [GitHub Discussions](https://github.com/subi2404/subhikshaImputeX/discussions)
- **Email**: subhiksha2404@gmail.com

## ๐Ÿ“š References

- Scikit-learn Documentation: https://scikit-learn.org/
- Pandas Documentation: https://pandas.pydata.org/
- Missing Data Handling: https://en.wikipedia.org/wiki/Missing_data

## ๐ŸŽ“ Citation

If you use subhikshaImputeX in academic research, please cite:

```bibtex
@software{subhikshaImputeX2025,
  title=subhikshaImputeX: Automatic Missing Value Imputation,
  author=Subhiksha_Anandhan,
  year=2025,
  url={https://github.com/subi2404/subhikshaImputeX}
}
```

---

**Made with โค๏ธ by Subhiksha_Anandhan**

            

Raw data

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    "description": "# subhikshaImputeX\r\n\r\n[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue)](https://www.python.org/downloads/)\r\n[![License](https://img.shields.io/badge/license-MIT-green)](LICENSE)\r\n[![PyPI](https://img.shields.io/badge/pypi-0.1.0-blue)](https://pypi.org/)\r\n\r\n**Automatic missing value imputation with intelligent per-column strategy selection.**\r\n\r\nsubhikshaImputeX is a production-ready Python library that automatically detects and applies the best imputation method for each column in your dataset using cross-validation.\r\n\r\n## \ud83c\udfaf Key Features\r\n\r\n\u2705 **Automatic Strategy Selection** - Tests multiple imputation methods per column and selects the best  \r\n\u2705 **Multiple Strategies** - Mean, Median, Mode, KNN, Regression, Forward Fill  \r\n\u2705 **Cross-Validation** - Evaluates accuracy using known values before imputation  \r\n\u2705 **Correlation Detection** - Identifies relationships between features for smarter imputation  \r\n\u2705 **Transparent Reporting** - Shows what strategy was chosen and why  \r\n\u2705 **Lightweight** - Only depends on NumPy, Pandas, and Scikit-learn  \r\n\u2705 **Per-Column Flexibility** - Different strategies for different columns  \r\n\u2705 **Type-Aware** - Handles numeric and categorical data appropriately  \r\n\r\n## \ud83d\udce6 Installation\r\n\r\n### From PyPI (recommended)\r\n```bash\r\npip install subhikshaImputeX\r\n```\r\n\r\n### From source\r\n```bash\r\ngit clone https://github.com/subi2404/subhikshaImputeX.git\r\ncd subhikshaImputeX\r\npip install -e .\r\n```\r\n\r\n### Development setup\r\n```bash\r\npip install -e \".[dev]\"\r\n```\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Basic Usage\r\n\r\n```python\r\nimport pandas as pd\r\nfrom subhikshaImputeX import SmartImputer\r\n\r\n# Load data with missing values\r\ndf = pd.read_csv('data.csv')\r\n\r\n# Create and fit imputer\r\nimputer = SmartImputer(evaluation=True, verbose=True)\r\ndf_clean = imputer.fit_transform(df)\r\n\r\n# Print report\r\nimputer.print_report()\r\n```\r\n\r\n### Advanced Usage\r\n\r\n```python\r\nfrom subhikshaImputeX import SmartImputer\r\n\r\n# Custom configuration\r\nimputer = SmartImputer(\r\n    strategy='auto',              # Auto-select best strategy per column\r\n    evaluation=True,              # Evaluate strategies via cross-validation\r\n    n_splits=5,                   # 5-fold cross-validation\r\n    detect_correlations=True,     # Detect feature correlations\r\n    verbose=True,                 # Print progress\r\n    random_state=42               # Reproducibility\r\n)\r\n\r\n# Fit on training data\r\nimputer.fit(df_train)\r\n\r\n# Transform train and test\r\ndf_train_clean = imputer.transform(df_train)\r\ndf_test_clean = imputer.transform(df_test)\r\n\r\n# Get detailed report\r\nreport = imputer.get_report()\r\nprint(report)\r\n```\r\n\r\n### Using Specific Strategies\r\n\r\n```python\r\nfrom subhikshaImputeX import SmartImputer\r\n\r\n# Use only mean imputation\r\nimputer = SmartImputer(strategy='mean')\r\ndf_clean = imputer.fit_transform(df)\r\n\r\n# Available strategies: 'mean', 'median', 'mode', 'knn', 'regression', 'forward_fill'\r\n```\r\n\r\n### Manual Strategy Selection\r\n\r\n```python\r\nfrom subhikshaImputeX import (\r\n    MeanImputer, \r\n    KNNImputation, \r\n    RegressionImputer\r\n)\r\n\r\n# Use custom strategy directly\r\nimputer = MeanImputer()\r\nimputer.fit(df['column'])\r\ndf['column'] = imputer.transform(df['column'])\r\n```\r\n\r\n## \ud83d\udcca Available Strategies\r\n\r\n| Strategy | Type | Best For | Pros | Cons |\r\n|----------|------|----------|------|------|\r\n| **Mean** | Numeric | Quick baseline | Fast, simple | Loses variance |\r\n| **Median** | Numeric | Robust imputation | Handles outliers | Less variance |\r\n| **Mode** | Categorical | Most frequent | Interpretable | Information loss |\r\n| **KNN** | Both | Local patterns | Considers similarity | Slow on large data |\r\n| **Regression** | Numeric | Feature relationships | Preserves correlations | Assumes linearity |\r\n| **Forward Fill** | Time series | Sequential data | Context-aware | Assumes order |\r\n\r\n## \ud83d\udd0d How It Works\r\n\r\n### 1. Automatic Strategy Selection\r\nFor each column with missing values:\r\n- Identifies data type (numeric or categorical)\r\n- Selects applicable strategies\r\n- Evaluates each using cross-validation\r\n- Chooses best performer\r\n\r\n### 2. Cross-Validation Evaluation\r\n- Randomly masks known values\r\n- Applies strategy to predict masked values\r\n- Compares predictions to true values\r\n- Calculates RMSE (numeric) or accuracy (categorical)\r\n- Repeats across multiple splits\r\n\r\n### 3. Correlation Detection\r\n- Identifies relationships between features\r\n- Prioritizes correlated features for regression/KNN\r\n- Provides feature importance ranking\r\n\r\n## \ud83d\udcc8 Performance\r\n\r\n### Evaluation Metrics\r\n\r\n**Numeric Columns:**\r\n- Uses RMSE (Root Mean Squared Error)\r\n- Lower RMSE = Better imputation\r\n\r\n**Categorical Columns:**\r\n- Uses Accuracy\r\n- Higher accuracy = Better imputation\r\n\r\n### Cross-Validation\r\n- Default: 5-fold cross-validation\r\n- Configurable via `n_splits` parameter\r\n- Prevents overfitting to training data\r\n\r\n## \ud83d\udca1 Examples\r\n\r\n### Example 1: Auto Imputation with Report\r\n\r\n```python\r\nimport pandas as pd\r\nfrom subhikshaImputeX import SmartImputer\r\nimport numpy as np\r\n\r\n# Create sample data\r\ndf = pd.DataFrame({\r\n    'age': [25, np.nan, 35, 45, np.nan, 30],\r\n    'income': [50000, 60000, np.nan, 80000, 90000, np.nan],\r\n    'category': ['A', 'B', np.nan, 'A', 'C', 'B']\r\n})\r\n\r\nprint(\"Before imputation:\")\r\nprint(df)\r\nprint(\"\\nMissing values:\")\r\nprint(df.isnull().sum())\r\n\r\n# Impute\r\nimputer = SmartImputer(evaluation=True, verbose=True)\r\ndf_clean = imputer.fit_transform(df)\r\n\r\nprint(\"\\n\\nAfter imputation:\")\r\nprint(df_clean)\r\n\r\n# Report\r\nimputer.print_report()\r\n```\r\n\r\n### Example 2: Train-Test Split\r\n\r\n```python\r\nimport pandas as pd\r\nfrom subhikshaImputeX import SmartImputer\r\n\r\n# Load data\r\ndf = pd.read_csv('data.csv')\r\n\r\n# Split\r\ntrain = df.iloc[:800]\r\ntest = df.iloc[800:]\r\n\r\n# Fit on train, transform both\r\nimputer = SmartImputer(evaluation=True)\r\nimputer.fit(train)\r\n\r\ntrain_clean = imputer.transform(train)\r\ntest_clean = imputer.transform(test)\r\n\r\n# Use for model training\r\nfrom sklearn.ensemble import RandomForestClassifier\r\n\r\nmodel = RandomForestClassifier()\r\nmodel.fit(train_clean.drop('target', axis=1), train_clean['target'])\r\n```\r\n\r\n### Example 3: Correlation Analysis\r\n\r\n```python\r\nfrom subhikshaImputeX import CorrelationDetector\r\nimport pandas as pd\r\n\r\ndf = pd.read_csv('data.csv')\r\n\r\n# Detect correlations\r\ndetector = CorrelationDetector(min_correlation=0.5)\r\ncorrelations = detector.detect(df)\r\n\r\nprint(\"Strong correlations:\")\r\nfor col, corrs in correlations.items():\r\n    print(f\"\\n{col}:\")\r\n    for corr_col, corr_val in corrs[:3]:  # Top 3\r\n        print(f\"  {corr_col}: {corr_val:.3f}\")\r\n\r\n# Visualize (requires matplotlib, seaborn)\r\n# detector.plot_correlation_heatmap()\r\n```\r\n\r\n## \ud83d\udd27 Configuration\r\n\r\n### SmartImputer Parameters\r\n\r\n```python\r\nSmartImputer(\r\n    strategy='auto',              # Strategy selection mode\r\n                                  # Options: 'auto', 'mean', 'median', 'mode', 'knn', 'regression', 'forward_fill'\r\n    \r\n    evaluation=True,              # Enable cross-validation evaluation\r\n    \r\n    n_splits=5,                   # Number of CV folds for evaluation\r\n    \r\n    detect_correlations=True,     # Detect feature correlations\r\n    \r\n    verbose=True,                 # Print progress and results\r\n    \r\n    random_state=42               # Random seed for reproducibility\r\n)\r\n```\r\n\r\n## \ud83d\udccb API Reference\r\n\r\n### SmartImputer\r\n\r\n**Methods:**\r\n- `fit(X)` - Fit on training data\r\n- `transform(X)` - Apply imputation\r\n- `fit_transform(X)` - Fit and transform\r\n- `get_report()` - Get imputation report (dict)\r\n- `print_report()` - Print formatted report\r\n\r\n### CorrelationDetector\r\n\r\n**Methods:**\r\n- `detect(df)` - Find correlations\r\n- `get_correlated_features(column, top_n=3)` - Get correlated features\r\n- `get_correlation_pairs()` - Get all correlation pairs\r\n- `get_feature_importance_for_imputation(column, df)` - Rank features\r\n- `plot_correlation_heatmap()` - Visualize correlations\r\n\r\n### Individual Strategies\r\n\r\nAll strategies follow the scikit-learn API:\r\n- `fit(series, X=None)`\r\n- `transform(series)`\r\n- `fit_transform(series, X=None)`\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nContributions are welcome! Here's how:\r\n\r\n1. Fork the repository\r\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\r\n3. Make your changes\r\n4. Run tests (`pytest tests/`)\r\n5. Commit changes (`git commit -m 'Add amazing feature'`)\r\n6. Push to branch (`git push origin feature/amazing-feature`)\r\n7. Open a Pull Request\r\n\r\n### Development Setup\r\n\r\n```bash\r\ngit clone https://github.com/subi2404/subhikshaImputeX.git\r\ncd subhikshaImputeX\r\npython -m venv venv\r\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\r\npip install -e \".[dev]\"\r\npytest tests/\r\n```\r\n\r\n## \ud83e\uddea Testing\r\n\r\nRun the test suite:\r\n\r\n```bash\r\npytest tests/                    # Run all tests\r\npytest tests/ -v                 # Verbose output\r\npytest tests/ --cov              # With coverage report\r\n```\r\n\r\n## \ud83d\udcdd License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\r\n\r\n## \ud83d\ude4b Support\r\n\r\n- **Issues**: [GitHub Issues](https://github.com/subi2404/subhikshaImputeX/issues)\r\n- **Discussions**: [GitHub Discussions](https://github.com/subi2404/subhikshaImputeX/discussions)\r\n- **Email**: subhiksha2404@gmail.com\r\n\r\n## \ud83d\udcda References\r\n\r\n- Scikit-learn Documentation: https://scikit-learn.org/\r\n- Pandas Documentation: https://pandas.pydata.org/\r\n- Missing Data Handling: https://en.wikipedia.org/wiki/Missing_data\r\n\r\n## \ud83c\udf93 Citation\r\n\r\nIf you use subhikshaImputeX in academic research, please cite:\r\n\r\n```bibtex\r\n@software{subhikshaImputeX2025,\r\n  title=subhikshaImputeX: Automatic Missing Value Imputation,\r\n  author=Subhiksha_Anandhan,\r\n  year=2025,\r\n  url={https://github.com/subi2404/subhikshaImputeX}\r\n}\r\n```\r\n\r\n---\r\n\r\n**Made with \u2764\ufe0f by Subhiksha_Anandhan**\r\n",
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