main.py
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Examples
6
BudiBadu
Online Scikit-learn Compiler
Scikit-learn is Python's most accessible and widely-used machine learning library, offering simple yet powerful tools for predictive data analysis. As the de facto standard for machine learning in Python, Scikit-learn is used by Fortune 500 companies, leading research institutions, and individual developers worldwide. Our dedicated online Scikit-learn compiler provides a complete browser environment for building, training, and evaluating machine learning models without any installation or setup requirements. The library's consistent API design makes it easy to learn and apply various machine learning algorithms to solve real-world problems.
This compiler includes Scikit-learn 1.7+ with NumPy, powered by Pyodide WebAssembly technology, providing instant access to classification, regression, clustering, and dimensionality reduction algorithms. The platform supports both supervised learning with algorithms for labeled data and unsupervised learning for pattern discovery. You can perform model selection using cross-validation and grid search for hyperparameter tuning, apply preprocessing techniques like data scaling and normalization, use feature selection tools, experiment with ensemble methods including Random Forests and Gradient Boosting, and evaluate models using comprehensive performance metrics such as accuracy, precision, recall, and F1-score. You can also upload and use files or folders directly in your code for machine learning workflows and data processing. This compiler is online and completely free to use.
Our interactive examples cover a wide range of machine learning algorithms including Linear Regression, Logistic Regression for classification, K-Means Clustering, Decision Trees, Random Forest ensemble learning, Support Vector Machines, and Principal Component Analysis (PCA). Each example demonstrates key concepts like training and evaluating models, regression analysis and prediction, clustering and pattern recognition, feature engineering and selection, cross-validation techniques, and handling overfitting and underfitting. The examples are designed to help you understand both the theory and practical implementation of machine learning algorithms.
Target Audience
- Machine learning beginners and students learning fundamentals
- Data scientists prototyping models before production deployment
- Researchers experimenting with different algorithms and techniques
- Educators teaching machine learning courses with hands-on examples
- Developers building predictive applications and AI features
- Anyone interested in understanding and applying ML algorithms
Part of the BudiBadu Ecosystem
Specialized Online Python compiler powered by Pyodide WebAssembly. Run Python Library directly in your browser with zero setup.
Pyodide
WebAssembly
Monaco Editor
Python 3.13