This repository contains sample implementations of various supervised machine learning algorithms. Each algorithm is implemented using Python and popular machine learning libraries such as NumPy and Scikit-learn. These implementations serve as basic examples to demonstrate how each algorithm works and can be used for educational purposes and as a starting point for more complex projects.
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- k-Nearest Neighbors (k-NN)
- Naive Bayes
- Neural Networks
- Gradient Boosting Machines (GBM)
- Support Vector Regression (SVR)
- Clone the repository to your local machine:
- Navigate to the cloned directory:
- Install the required dependencies (NumPy, Scikit-learn, TensorFlow) using pip:
- Open each algorithm's Python script and run it using your preferred Python interpreter (e.g., Anaconda, Jupyter Notebook, or a Python IDE).
The sample data used in each algorithm's implementation is kept simple for demonstration purposes. If you want to test the algorithms with your own dataset, make sure it is properly formatted and preprocessed before using it.
Contributions to this repository are welcome! If you find any issues or have improvements to suggest, feel free to open a pull request.
These implementations are meant for educational purposes and might not be optimized for real-world scenarios. For production use or critical applications, it is recommended to use well-established machine learning libraries and frameworks that have been thoroughly tested and optimized.Please note that these implementations serve as basic examples to understand how each algorithm works. For real-world applications, it's essential to preprocess data properly, handle missing values, and perform hyperparameter tuning to achieve better results. Additionally, different libraries and frameworks might offer additional options and functionalities for these algorithms.