subhikshaImputeX 0.1.0
Automatic missing value imputation with intelligent strategy selection
Requires Python: >=3.8
Keywords: imputation, missing-values, machine-learning, data-preprocessing, pandas, scikit-learn, knn, regression, strategy-selection

# 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.

## 🎯 Key Features

✅ **Automatic Strategy Selection** - Tests multiple imputation methods per column and selects the best  
✅ **Multiple Strategies** - Mean, Median, Mode, KNN, Regression, Forward Fill  
✅ **Cross-Validation** - Evaluates accuracy using known values before imputation  
✅ **Correlation Detection** - Identifies relationships between features for smarter imputation  
✅ **Transparent Reporting** - Shows what strategy was chosen and why  
✅ **Lightweight** - Only depends on NumPy, Pandas, and Scikit-learn  
✅ **Per-Column Flexibility** - Different strategies for different columns  
✅ **Type-Aware** - Handles numeric and categorical data appropriately  

## 📦 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]"
```

## 🚀 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'])
```

## 📊 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 |

## 🔍 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

## 📈 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

## 💡 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()
```

## 🔧 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
)
```

## 📋 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)`

## 🤝 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/
```

## 🧪 Testing

Run the test suite:

```bash
pytest tests/                    # Run all tests
pytest tests/ -v                 # Verbose output
pytest tests/ --cov              # With coverage report
```

## 📝 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙋 Support

- **Issues**: [GitHub Issues](https://github.com/subi2404/subhikshaImputeX/issues)
- **Discussions**: [GitHub Discussions](https://github.com/subi2404/subhikshaImputeX/discussions)
- **Email**: subhiksha2404@gmail.com

## 📚 References

- Scikit-learn Documentation: https://scikit-learn.org/
- Pandas Documentation: https://pandas.pydata.org/
- Missing Data Handling: https://en.wikipedia.org/wiki/Missing_data

## 🎓 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}
}
```

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**Made with ❤️ by Subhiksha_Anandhan**