This project was created for personal practice purposes to explore machine learning algorithms using scikit-learn in Python.
The primary objectives include:
- Machine Learning Practice: Experimentation with various machine learning algorithms available in scikit-learn.
- Algorithm Comparison: Comparing the performance of different algorithms on specific datasets and understanding their outputs.
- Data Visualization and Statistics: Employing basic data visualization techniques using Matplotlib and exploring data statistics with NumPy and Pandas.
- Python Scripts: Contains Python scripts demonstrating the implementation of machine learning algorithms, data visualization, and statistical analysis.
- Jupyter Notebooks: Includes Jupyter Notebooks providing step-by-step explanations and visual representations of the machine learning workflows and data analysis.
- Datasets: Sample datasets utilized for training models and practicing data visualization.
- Documentation: Supplementary documentation or notes relevant to the machine learning exercises and insights gained during the project.
- Explore the provided Python scripts and Jupyter Notebooks to understand the implemented machine learning algorithms and data analysis techniques.
- Execute the scripts or notebooks to see the outputs and understand the comparisons between different algorithms.
- Modify and experiment with the code to apply your datasets or explore different algorithms.
This project was created solely for personal learning and experimentation purposes and may not adhere to production-level standards.
This project is licensed under the MIT License.