Metadata-Version: 2.1
Name: MLEase
Version: 0.0.2
Summary: Simplify machine learning tasks with easy-to-use tools and utilities.
Home-page: https://github.com/harikrishnad1997/MLEase
Author: Harikrishna Dev
Author-email: harikrish0607@gmail.com
Keywords: python,machine learning,data science
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: scikit-learn
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: joblib
Requires-Dist: python-dateutil
Requires-Dist: pytz
Requires-Dist: six
Requires-Dist: threadpoolctl
Requires-Dist: xgbimputer
Requires-Dist: xgboost


MLEase is a comprehensive Python package that simplifies machine learning tasks by providing many easy-to-use tools and utilities. It aims to streamline the entire machine learning workflow, from data preprocessing to model evaluation, making it accessible to both beginners and experienced practitioners.

Key features include:

- Missing value imputation: Handle missing data in your dataset using various strategies such as mean, median, mode imputation, or advanced techniques like KNN imputation.
- Outlier detection and removal: Identify and remove outliers from your dataset using methods like standard deviation, IQR, Mahalanobis distance, or anomaly detection algorithms such as Isolation Forest, One-Class SVM, Elliptic Envelope, and Local Outlier Factor.
- Feature scaling: Scale your features to a standard range using methods like Min-Max scaling or standardization.
- and more.

MLEase aims to simplify the machine learning process and empower users to build more robust and accurate models with ease.