featureflow-ml 0.1.0

Automated preprocessing and feature selection pipelines for classification and regression datasets.

FeatureFlow-ML

A comprehensive machine learning framework providing optimized classification and regression pipelines with automated feature engineering, preprocessing, and model evaluation capabilities.

🎯 Overview

FeatureFlow-ML streamlines machine learning workflows by providing production-ready pipelines for both classification and regression tasks. It abstracts away boilerplate code while maintaining full flexibility for custom configurations and advanced use cases.

Key Strengths:

• ⚡ Fast Implementation: Build ML models with minimal code
• 🔄 Automated Pipelines: Complete data-to-prediction workflows
• 📊 Multiple Algorithms: Ensemble and traditional models
• 🎛️ Configurable: Fine-grained control over pipeline stages
• 📈 Performance Tracking: Built-in evaluation and metrics
• 🔍 Interpretability: Feature importance and model explanations

📋 Table of Contents

• Quick Start
• Installation
• Core Modules
• Classification Pipeline
• Regression Pipeline
• Advanced Usage
• API Reference
• Algorithm Comparison
• Performance Optimization
• Troubleshooting
• Best Practices
• Examples
• Contributing

🚀 Quick Start

Classification in 5 Lines

from classification_pipeline import ClassificationPipeline

pipeline = ClassificationPipeline(model_type='random_forest')
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)
accuracy = pipeline.evaluate(X_test, y_test)
print(f"Accuracy: {accuracy:.4f}")

Regression in 5 Lines

from regression_pipeline import RegressionPipeline

pipeline = RegressionPipeline(model_type='xgboost')
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)
r2_score = pipeline.evaluate(X_test, y_test)
print(f"R² Score: {r2_score:.4f}")

📦 Installation

Prerequisites

• Python 3.7+
• pip or conda

From Source

git clone https://github.com/lovekaushik899/FeatureFlow-ML.git
cd FeatureFlow-ML
pip install -r requirements.txt

Dependencies

pip install numpy pandas scikit-learn xgboost lightgbm matplotlib seaborn

Dependency Breakdown:

• numpy - Numerical computations
• pandas - Data manipulation and analysis
• scikit-learn - Machine learning algorithms and utilities
• xgboost - Gradient boosting framework
• lightgbm - Light gradient boosting (alternative to XGBoost)
• matplotlib, seaborn - Visualization

📚 Core Modules

ClassificationPipeline

Handles multi-class and binary classification tasks with automated preprocessing and multiple algorithm support.

Supported Algorithms:

• Logistic Regression
• Random Forest
• Gradient Boosting (XGBoost)
• Light Gradient Boosting (LightGBM)
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)

RegressionPipeline

Handles continuous target prediction with support for linear and non-linear models.

Supported Algorithms:

• Linear Regression
• Ridge/Lasso Regression
• Random Forest Regression
• Gradient Boosting (XGBoost)
• Light Gradient Boosting (LightGBM)
• Support Vector Regression (SVR)

🔧 Classification Pipeline

Basic Usage

from classification_pipeline import ClassificationPipeline
import pandas as pd
from sklearn.model_selection import train_test_split

# Load data
data = pd.read_csv('data.csv')
X = data.drop('target', axis=1)
y = data['target']

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Initialize pipeline
pipeline = ClassificationPipeline(
    model_type='random_forest',
    n_estimators=100,
    max_depth=10,
    random_state=42
)

# Train and evaluate
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)
accuracy = pipeline.evaluate(X_test, y_test)

print(f"Accuracy: {accuracy:.4f}")
print(f"Predictions: {predictions}")

Advanced Configuration

pipeline = ClassificationPipeline(
    model_type='xgboost',
    # Preprocessing
    handle_missing='mean',           # 'mean', 'median', 'drop'
    scale_features=True,              # Standardize features
    encode_categorical=True,          # One-hot encoding
    # Model parameters
    n_estimators=150,
    learning_rate=0.1,
    max_depth=8,
    subsample=0.8,
    colsample_bytree=0.8,
    # Training
    validation_split=0.2,
    early_stopping_rounds=20,
    random_state=42
)

# Cross-validation
cv_scores = pipeline.cross_validate(X_train, y_train, cv=5)
print(f"CV Scores: {cv_scores}")

# Feature importance
importance = pipeline.feature_importance()
print(f"Top features: {importance.head()}")

📈 Regression Pipeline

Basic Usage

from regression_pipeline import RegressionPipeline
import pandas as pd
from sklearn.model_selection import train_test_split

# Load data
data = pd.read_csv('housing_data.csv')
X = data.drop('price', axis=1)
y = data['price']

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Initialize pipeline
pipeline = RegressionPipeline(
    model_type='xgboost',
    n_estimators=100
)

# Train and evaluate
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)
r2 = pipeline.evaluate(X_test, y_test, metric='r2')
rmse = pipeline.evaluate(X_test, y_test, metric='rmse')
mae = pipeline.evaluate(X_test, y_test, metric='mae')

print(f"R² Score: {r2:.4f}")
print(f"RMSE: {rmse:.4f}")
print(f"MAE: {mae:.4f}")

Advanced Configuration

pipeline = RegressionPipeline(
    model_type='lightgbm',
    # Preprocessing
    handle_missing='median',
    scale_features=True,
    outlier_removal=True,           # Remove statistical outliers
    outlier_threshold=3,             # Standard deviations
    # Model parameters
    n_estimators=200,
    learning_rate=0.05,
    max_depth=10,
    num_leaves=31,
    # Training
    validation_split=0.2,
    early_stopping_rounds=30,
    random_state=42,
    n_jobs=-1                        # Parallel processing
)

# Evaluation with multiple metrics
metrics = pipeline.evaluate_all(X_test, y_test)
print(metrics)  # {'r2': ..., 'rmse': ..., 'mae': ..., 'mape': ...}

🔬 Advanced Usage

1. Custom Feature Engineering

def custom_features(X):
    X_new = X.copy()
    X_new['feature_ratio'] = X['col1'] / (X['col2'] + 1)
    X_new['feature_interaction'] = X['col1'] * X['col3']
    return X_new

pipeline = ClassificationPipeline(model_type='random_forest')
X_train_engineered = custom_features(X_train)
X_test_engineered = custom_features(X_test)
pipeline.fit(X_train_engineered, y_train)
predictions = pipeline.predict(X_test_engineered)

2. Hyperparameter Tuning

from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [5, 10, 15],
    'learning_rate': [0.01, 0.1, 0.5]
}

pipeline = ClassificationPipeline(model_type='xgboost')
best_params = pipeline.grid_search(X_train, y_train, param_grid, cv=5)
print(f"Best parameters: {best_params}")

3. Class Imbalance Handling

from imblearn.over_sampling import SMOTE

pipeline = ClassificationPipeline(
    model_type='random_forest',
    handle_imbalance=True,           # Enable SMOTE
    imbalance_sampling_strategy='auto'
)

pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

4. Model Persistence

import pickle

# Save model
pipeline.save('my_model.pkl')

# Load model
loaded_pipeline = pickle.load(open('my_model.pkl', 'rb'))
predictions = loaded_pipeline.predict(X_test)

📖 API Reference

ClassificationPipeline

ClassificationPipeline(
    model_type='random_forest',      # str: Algorithm choice
    n_estimators=100,                 # int: Number of trees/boosting rounds
    max_depth=None,                   # int or None: Max tree depth
    learning_rate=0.1,                # float: Learning rate for boosting
    random_state=None,                # int or None: Random seed
    scale_features=True,              # bool: Standardize features
    handle_missing='mean',            # str: 'mean', 'median', 'drop'
    encode_categorical=True,          # bool: One-hot encode
    validation_split=0.2,             # float: Validation set fraction
    n_jobs=-1                         # int: Parallel jobs (-1 = all cores)
)

Key Methods:

• fit(X, y) - Train the pipeline
• predict(X) - Make predictions
• predict_proba(X) - Get prediction probabilities
• evaluate(X, y, metric='accuracy') - Evaluate performance
• feature_importance() - Get feature rankings
• cross_validate(X, y, cv=5) - K-fold cross-validation
• save(filepath) - Save trained model
• load(filepath) - Load trained model

RegressionPipeline

RegressionPipeline(
    model_type='xgboost',             # str: Algorithm choice
    n_estimators=100,                 # int: Number of boosting rounds
    learning_rate=0.1,                # float: Learning rate
    max_depth=6,                      # int: Max tree depth
    random_state=None,                # int or None: Random seed
    scale_features=True,              # bool: Standardize features
    handle_missing='median',          # str: 'mean', 'median', 'drop'
    outlier_removal=False,            # bool: Remove outliers
    outlier_threshold=3,              # float: Std deviation threshold
    validation_split=0.2,             # float: Validation fraction
    n_jobs=-1                         # int: Parallel jobs
)

Key Methods:

• fit(X, y) - Train the pipeline
• predict(X) - Make predictions
• evaluate(X, y, metric='r2') - Evaluate performance
• evaluate_all(X, y) - Get all metrics (R², RMSE, MAE, MAPE)
• feature_importance() - Get feature rankings
• cross_validate(X, y, cv=5) - K-fold cross-validation
• residuals(X, y) - Get prediction errors
• save(filepath) - Save trained model
• load(filepath) - Load trained model

📊 Algorithm Comparison

Classification Models

Algorithm	Speed	Accuracy	Interpretability	Memory	Best For
Logistic Regression	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Linear separable data
Random Forest	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	Balanced performance
XGBoost	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	Competitive performance
LightGBM	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	Large datasets
SVM	⭐⭐	⭐⭐⭐⭐	⭐⭐	⭐⭐	High-dimensional data
KNN	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐	Small to medium data

Regression Models

Algorithm	Speed	Accuracy	Interpretability	Memory	Best For
Linear Regression	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Linear relationships
Ridge/Lasso	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Regularized prediction
Random Forest	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	Non-linear patterns
XGBoost	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	Competitive datasets
LightGBM	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	Large datasets
SVR	⭐⭐	⭐⭐⭐⭐	⭐⭐	⭐⭐⭐	Non-linear regression

⚡ Performance Optimization

1. Data Preprocessing

# Efficient feature scaling
pipeline = ClassificationPipeline(
    scale_features=True,           # Much faster with scaled features
    handle_missing='drop'          # Faster than 'mean'
)

2. Parallel Processing

# Use all available cores
pipeline = ClassificationPipeline(
    n_jobs=-1,                     # Parallelization
    model_type='xgboost'
)

3. Early Stopping

# Stop training when validation performance plateaus
pipeline = ClassificationPipeline(
    model_type='xgboost',
    validation_split=0.2,
    early_stopping_rounds=20       # Stop after 20 rounds without improvement
)

4. Model Selection by Dataset Size

Small Dataset (< 10K samples):     Use Logistic Regression or SVM
Medium Dataset (10K - 1M):          Use Random Forest or XGBoost
Large Dataset (> 1M):               Use LightGBM with parallel processing

5. Memory Optimization

# Reduce memory footprint
pipeline = RegressionPipeline(
    model_type='lightgbm',
    num_leaves=31,                 # Lower = less memory
    max_depth=10,
    n_jobs=-1
)

🔧 Troubleshooting

Issue: Out of Memory Error

Solution 1: Use LightGBM instead of XGBoost

pipeline = ClassificationPipeline(model_type='lightgbm')

Solution 2: Reduce model complexity

pipeline = ClassificationPipeline(
    n_estimators=50,               # Fewer trees
    max_depth=5,                   # Shallower trees
    subsample=0.5                  # Use fraction of data
)

Issue: Low Accuracy / R² Score

Check 1: Verify data quality

# Check for missing values
print(X_train.isnull().sum())

# Check for class imbalance (classification)
print(y_train.value_counts())

Check 2: Try different algorithms

for model in ['random_forest', 'xgboost', 'lightgbm']:
    pipeline = ClassificationPipeline(model_type=model)
    pipeline.fit(X_train, y_train)
    score = pipeline.evaluate(X_test, y_test)
    print(f"{model}: {score:.4f}")

Check 3: Increase model complexity

pipeline = ClassificationPipeline(
    n_estimators=200,              # More trees
    max_depth=15,                  # Deeper trees
    learning_rate=0.05             # Lower rate for finer tuning
)

Issue: Overfitting

Solution 1: Regularization

pipeline = ClassificationPipeline(
    model_type='xgboost',
    reg_alpha=1.0,                 # L1 regularization
    reg_lambda=1.0,                # L2 regularization
    subsample=0.8,
    colsample_bytree=0.8
)

Solution 2: Early stopping

pipeline = ClassificationPipeline(
    validation_split=0.2,
    early_stopping_rounds=15
)

Issue: Underfitting

Solution: Increase model capacity

pipeline = ClassificationPipeline(
    n_estimators=300,
    max_depth=15,
    learning_rate=0.1               # Faster learning
)

Issue: Slow Training

Solution: Use LightGBM and parallelization

pipeline = ClassificationPipeline(
    model_type='lightgbm',
    n_jobs=-1                       # All CPU cores
)

💡 Best Practices

1. Data Splitting Strategy

from sklearn.model_selection import train_test_split

# Always use stratified split for classification
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

2. Preprocessing Before Modeling

# Fit preprocessing on training data only
pipeline = ClassificationPipeline()
pipeline.fit(X_train, y_train)         # Scales/encodes based on training data
predictions = pipeline.predict(X_test)  # Applies same transformations

3. Cross-Validation for Robustness

# Use k-fold CV to assess model stability
cv_scores = pipeline.cross_validate(X, y, cv=5)
print(f"Mean: {cv_scores.mean():.4f} ± {cv_scores.std():.4f}")

4. Feature Importance Analysis

# Understand which features drive predictions
importance = pipeline.feature_importance()
importance.plot(kind='barh')
plt.title('Feature Importance')
plt.show()

5. Monitor for Class Imbalance

# Check training data
if y.value_counts().min() < y.value_counts().max() * 0.1:
    print("⚠️ Severe class imbalance detected")
    pipeline = ClassificationPipeline(handle_imbalance=True)

6. Document Your Pipeline

# Save pipeline configuration for reproducibility
config = {
    'model_type': 'xgboost',
    'n_estimators': 100,
    'max_depth': 8,
    'random_state': 42
}

import json
with open('pipeline_config.json', 'w') as f:
    json.dump(config, f)

📋 Examples

Example 1: Binary Classification (Customer Churn)

from classification_pipeline import ClassificationPipeline
import pandas as pd
from sklearn.model_selection import train_test_split

# Load data
df = pd.read_csv('churn_data.csv')
X = df.drop('churn', axis=1)
y = df['churn']

# Split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Train
pipeline = ClassificationPipeline(
    model_type='xgboost',
    n_estimators=150,
    handle_imbalance=True
)
pipeline.fit(X_train, y_train)

# Evaluate
accuracy = pipeline.evaluate(X_test, y_test)
prob = pipeline.predict_proba(X_test)

print(f"Accuracy: {accuracy:.4f}")
print(f"Sample probabilities: {prob[:5]}")

Example 2: Multi-Class Classification (Iris Dataset)

from classification_pipeline import ClassificationPipeline
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load
iris = load_iris()
X, y = iris.data, iris.target

# Split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Train
pipeline = ClassificationPipeline(model_type='random_forest')
pipeline.fit(X_train, y_train)

# Evaluate
accuracy = pipeline.evaluate(X_test, y_test)
importance = pipeline.feature_importance()

print(f"Accuracy: {accuracy:.4f}")
print(f"Top feature: {importance.index[0]}")

Example 3: Regression (Housing Price Prediction)

from regression_pipeline import RegressionPipeline
import pandas as pd
from sklearn.model_selection import train_test_split

# Load
df = pd.read_csv('housing.csv')
X = df.drop('price', axis=1)
y = df['price']

# Split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train
pipeline = RegressionPipeline(
    model_type='lightgbm',
    n_estimators=200,
    n_jobs=-1
)
pipeline.fit(X_train, y_train)

# Evaluate
metrics = pipeline.evaluate_all(X_test, y_test)
predictions = pipeline.predict(X_test)

print(f"R² Score: {metrics['r2']:.4f}")
print(f"RMSE: {metrics['rmse']:.2f}")
print(f"MAE: {metrics['mae']:.2f}")

🤝 Contributing

Contributions are welcome! Please follow these guidelines:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Commit changes (git commit -am 'Add feature')
4. Push to branch (git push origin feature/improvement)
5. Submit a Pull Request

Code Standards

• Follow PEP 8 style guide
• Add docstrings to all functions
• Include unit tests for new features
• Update README with changes

📄 License

This project is open-source and available under the MIT License.

📚 Resources

• scikit-learn Documentation: https://scikit-learn.org/stable/
• XGBoost Docs: https://xgboost.readthedocs.io/
• LightGBM Docs: https://lightgbm.readthedocs.io/
• Pandas Guide: https://pandas.pydata.org/docs/

🆘 Support

For issues, questions, or suggestions:

• Open a GitHub issue
• Check existing documentation
• Review example notebooks

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Version: 2.0.0
Last Updated: 2026-03-09
Maintainer: Love Kaushik (@lovekaushik899)