# subhikshaImputeX
[](https://www.python.org/downloads/)
[](LICENSE)
[](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.
## ๐ฏ Key Features
โ
**Automatic Strategy Selection** - Tests multiple imputation methods per column and selects the best
โ
**Multiple Strategies** - Mean, Median, Mode, KNN, Regression, Forward Fill
โ
**Cross-Validation** - Evaluates accuracy using known values before imputation
โ
**Correlation Detection** - Identifies relationships between features for smarter imputation
โ
**Transparent Reporting** - Shows what strategy was chosen and why
โ
**Lightweight** - Only depends on NumPy, Pandas, and Scikit-learn
โ
**Per-Column Flexibility** - Different strategies for different columns
โ
**Type-Aware** - Handles numeric and categorical data appropriately
## ๐ฆ 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]"
```
## ๐ 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'])
```
## ๐ 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 |
## ๐ 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
## ๐ 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
## ๐ก 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()
```
## ๐ง 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
)
```
## ๐ 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)`
## ๐ค 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/
```
## ๐งช Testing
Run the test suite:
```bash
pytest tests/ # Run all tests
pytest tests/ -v # Verbose output
pytest tests/ --cov # With coverage report
```
## ๐ License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## ๐ Support
- **Issues**: [GitHub Issues](https://github.com/subi2404/subhikshaImputeX/issues)
- **Discussions**: [GitHub Discussions](https://github.com/subi2404/subhikshaImputeX/discussions)
- **Email**: subhiksha2404@gmail.com
## ๐ References
- Scikit-learn Documentation: https://scikit-learn.org/
- Pandas Documentation: https://pandas.pydata.org/
- Missing Data Handling: https://en.wikipedia.org/wiki/Missing_data
## ๐ 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 โค๏ธ by Subhiksha_Anandhan**
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"description": "# subhikshaImputeX\r\n\r\n[](https://www.python.org/downloads/)\r\n[](LICENSE)\r\n[](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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