This project provides a collection of Jupyter Notebook exercises for practicing scikit-learn, a popular machine learning library in Python. Scikit-learn provides a wide range of machine learning algorithms, tools for data preprocessing, model evaluation, and more. Through this project, we aim to enhance our skills in scikit-learn and gain hands-on experience with various machine learning tasks.
Before running the code, make sure you have the following dependencies installed:
- Python (3.x)
- Jupyter Notebook
- NumPy
- pandas
- scikit-learn
To get started with the project, follow the steps below:
- Clone the repository:
git clone https://github.com/shaadclt/Scikit-learn-Exercises.git- Change into the project directory:
cd Scikit-learn-Exercises-
Install the required dependencies:
-
Run Jupyter Notebook:
jupyter notebook-
Open the Jupyter Notebook files (*.ipynb) in Jupyter.
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Follow the instructions in the notebooks to practice and explore different scikit-learn exercises.
The project covers various scikit-learn exercises, including but not limited to:
- Data Preprocessing: Handling missing values, encoding categorical variables, scaling numerical features, and splitting data into training and testing sets.
- Supervised Learning: Applying classification and regression algorithms, such as decision trees, logistic regression, support vector machines, or random forests.
- Unsupervised Learning: Implementing clustering algorithms, such as k-means clustering or hierarchical clustering, and dimensionality reduction techniques like principal component analysis (PCA).
- Model Evaluation: Assessing model performance using evaluation metrics, cross-validation, and learning curves.
- Pipelines: Constructing and using machine learning pipelines for streamlined data preprocessing and model training.
Each notebook includes code snippets, and practice exercises to reinforce the understanding of scikit-learn concepts.
The emphasis of this project is on practicing and implementing scikit-learn exercises rather than providing specific results or insights. Each notebook contains exercises and examples to apply the concepts learned and gain a deeper understanding of machine learning using scikit-learn. Feel free to experiment with different datasets, modify the exercises, or explore additional scikit-learn functionalities beyond the provided exercises.
You can customize the project by adding your own exercises, creating additional notebooks for specific topics, or expanding the exercises with more advanced scikit-learn concepts. This project serves as a starting point for you to practice and enhance your skills in machine learning using scikit-learn.
This project is licensed under the MIT License. See the LICENSE file for more information.
- This project is created for the purpose of practicing scikit-learn exercises using Jupyter Notebook.
Contributions are welcome! If you find any issues, have suggestions for improvements, or want to add more exercises, please open an issue or submit a pull request.