This repository serves as a comprehensive collection of fundamental machine learning algorithms implemented in Python, along with exploratory data analysis (EDA) techniques and foundational Python concepts essential for ML. The goal is to provide clear, understandable implementations and explanations of these algorithms, focusing on both theoretical understanding and practical application.
Machine learning is a rapidly evolving field, and understanding its core algorithms is crucial for anyone venturing into data science or AI. This repository aims to be a valuable resource for learning and implementing key machine learning algorithms from scratch (where appropriate) or using popular libraries. It's designed to be approachable for beginners while still offering sufficient detail for those looking to deepen their understanding.
This repository is organized into several key notebooks:
Linear_Regression.ipynb: Implementation and explanation of Linear Regression.Decision_Trees_and_Random_Forests.ipynb: Implementation and explanation of Decision Trees and Random Forests.EDA_and_Python_Basics.ipynb: Covers essential Python programming concepts and techniques for Exploratory Data Analysis.
Currently, this repository includes implementations and discussions of the following algorithms:
- Linear Regression: Linear Regression aims to model the relationship between a dependent variable y and one or more independent variables X by fitting a linear equation to the observed data.
- Decision Trees: A non-parametric supervised learning method used for classification and regression.
- Random Forests: An ensemble learning method for classification, regression, and other tasks that operates by constructing a multitude of decision trees at training time.
- Diverse Algorithm Coverage: Implementations of both linear models and tree-based ensemble methods.
- Ordinary Least Squares (OLS): Utilizes the analytical solution for finding optimal coefficients in Linear Regression.
- Evaluation Metrics: Includes common metrics like R-squared, Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) for model evaluation.
- Data Preprocessing: Basic preprocessing steps if necessary (e.g., handling missing values, feature scaling).
- Visualization: Basic plotting capabilities for understanding model behavior and data patterns.
- Clear Codebase: Well-commented and easy-to-understand code across all notebooks.
- Foundational Skills: Dedicated notebook for essential Python basics and EDA.
To run the notebooks in this repository, you'll need the following Python libraries:
- pandas: For data manipulation and analysis.
- numpy: For numerical operations.
- scikit-learn: For comparison, utility functions, and potentially some algorithm implementations (though core models are often built from scratch for learning purposes).
- matplotlib: For creating static, interactive, and animated visualizations.
- seaborn: (Consider adding if you use it for enhanced visualizations in your EDA or other notebooks).
You can install these dependencies using pip:
pip install pandas numpy scikit-learn matplotlib seabornContributing Contributions are highly welcome! If you have any suggestions, bug reports, or want to contribute to the codebase with new algorithms, improved implementations, or better explanations, please feel free to open an issue or submit a pull request.
Please ensure your code adheres to the existing style, includes relevant tests (if applicable), and is well-documented.
License This project is licensed under the MIT License - see the LICENSE file for details.