simplreg 0.2

A simple machine learning package for forecasting and classification

Simplreg Documentation

Simplreg is a simple machine learning pipeline designed for beginners to quickly perform forecasting and classification tasks on supervised datasets. This package abstracts the complexities of preprocessing, model training, evaluation, and visualization into a user-friendly interface.

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Installation

To install Simplreg, use pip:

pip install simplreg

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Features

• Preprocessing: Automatically splits the dataset into training and testing sets and handles basic preprocessing tasks.
• Model Training: Supports both classification and regression tasks with pre-configured models.
• Evaluation: Provides performance metrics for both classification and regression tasks.
• Visualization: Displays feature importance for classification models.

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Getting Started

1. Import the Package

from simplreg import simplereg_pipeline

2. Prepare Your Dataset

Ensure your dataset is in the form of a Pandas DataFrame. The target column should be specified for predictions.

Example:

import pandas as pd
from sklearn.datasets import load_iris

# Load dataset
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target

3. Run the Pipeline

# Run the pipeline
simplereg_pipeline(df, target_column='target', task_type='classification')

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API Reference

simplereg_pipeline

Definition:

def simplereg_pipeline(df, target_column, task_type):
    """Simple ML pipeline: Preprocess, train, evaluate, visualize."""

Parameters:

• df (DataFrame): Input dataset.
• target_column (str): Name of the column containing the target variable.
• task_type (str): Type of task, either classification or regression.

Returns: None (Outputs results to the console and visualizes feature importance).

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Under the Hood

Modules

1. Preprocessing

   • Splits the dataset into training and testing sets.
   • Handles missing values and scaling (if necessary).

2. Model Training

   • Uses Logistic Regression for classification tasks.
   • Uses Linear Regression for regression tasks.

3. Evaluation

   • Classification: Outputs accuracy, precision, recall, and F1 score.
   • Regression: Outputs mean absolute error, mean squared error, and R-squared value.

4. Visualization

   • Displays feature importance for classification tasks using bar plots.

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Example Usage

Classification Example

from simplreg import simplereg_pipeline
import pandas as pd
from sklearn.datasets import load_iris

# Load dataset
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target

# Run pipeline
simplereg_pipeline(df, target_column='target', task_type='classification')

Regression Example

from simplreg import simplereg_pipeline
import pandas as pd
import numpy as np

# Generate synthetic regression data
data = {
    'feature1': np.random.rand(100),
    'feature2': np.random.rand(100),
    'target': np.random.rand(100) * 10
}
df = pd.DataFrame(data)

# Run pipeline
simplereg_pipeline(df, target_column='target', task_type='regression')

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Contributing

Contributions are welcome! If you'd like to contribute, please:

1. Fork the repository.
2. Create a new branch.
3. Make your changes and commit them.
4. Submit a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Support

If you encounter any issues or have questions, feel free to open an issue on the GitHub repository or contact the maintainer at [e-mail].

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Acknowledgments

• Scikit-learn: For providing robust machine learning utilities.
• Matplotlib: For visualization capabilities.
• Pandas: For data manipulation and analysis.

Thank you for using Simplreg! Happy learning!