Machine Learning Notebooks Project

Project Description

This project contains a collection of notebooks that implement various Machine Learning problems, categorized into two main groups: supervised learning and unsupervised learning. These problems include classification, regression, and clustering, with each problem solved using different algorithms to provide a comprehensive understanding of Machine Learning techniques and tools.

Directory Structure

notebooks/

Contains all the notebooks of the project, divided into two main groups:

1. supervised/

Includes supervised learning problems, with two main types: classification and regression.

classification/

• decision_tree/:

  • breast_cancer_decision_tree_classifier.ipynb: Decision tree classification for breast cancer data.
  • iris_decision_tree_classifier.ipynb: Iris flower classification using decision tree.
  • wine_decision_tree_classifier.ipynb: Wine classification using decision tree.

• knn/:

  • digit_recognition.ipynb: Handwritten digit recognition using K-Nearest Neighbors (KNN).

• model_comparison_classification/:

  • classifier_comparison.ipynb: Comparison of different classification models on a specific dataset.
  • digit_classifier_comparison.ipynb: Comparison of different classification models on the digit dataset.
  • spam_classifier_comparison.ipynb: Comparison of different models for spam classification.

• neural_network/:

  • mlp_classifier/:

    • diabetes_mlp_classifier.ipynb: Using MLP network for diabetes classification.
    • iris_mlp_classifier.ipynb: Using MLP network for Iris flower classification.

  • perceptron/:

    • diabetes_perceptron.ipynb: Diabetes classification using Perceptron.
    • iris_perceptron.ipynb: Iris flower classification using Perceptron.
    • perceptron_from_scratch.py: Python file implementing Perceptron from scratch without using specialized libraries.
    • perceptron_from_scratch_demo.ipynb: Demo implementation of Perceptron from scratch.
    • spam_perceptron.ipynb: Spam classification using Perceptron.

regression/

• linear/:
  • advertising.ipynb: Predicting advertising effectiveness using linear regression.
  • boston_house_price.ipynb: Predicting Boston house prices using linear regression.
  • linear_regression_from_scratch.ipynb: Linear regression implemented from scratch without using specialized libraries.
  • student_performance.ipynb: Predicting student performance using linear regression.

2. unsupervised/

Contains unsupervised learning problems, including clustering algorithms.

clustering/

• dbscan/:

  • custom_dbscan_implementation.ipynb: Custom implementation of DBSCAN from scratch without using specialized libraries.
  • fresh_milk_clustering.ipynb: Clustering the Fresh and Milk dataset using DBSCAN.

• kmeans/:

  • kmeans_from_scratch.ipynb: Implementation of the K-means algorithm from scratch without using specialized libraries.
  • news_group_clustering.ipynb: Clustering news articles into topic groups using K-means.

data/

Contains datasets used in the project.

1. classification/

• SMSSpamCollection: Dataset for spam classification.
• and_gate_datasets.csv: Dataset for AND gate classification.

2. regression/

• advertising.csv: Dataset for advertising effectiveness prediction.
• housing.csv: Dataset for housing prices.
• student_performance.csv: Dataset for student performance prediction.

Usage

To use the notebooks in this project, follow these steps:

1. Clone the Repository: Clone the repository to your local machine using:

   git clone https://github.com/hoduy511/Machine-Learning-Projects.git
   cd Machine-Learning-Projects

2. Set Up the Environment: It is recommended to use a virtual environment to manage dependencies. You can create and activate a virtual environment using the following commands:

   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install Required Libraries: Install the required libraries listed in the requirements.txt file:

   pip install -r requirements.txt

4. Run Notebooks: Open Jupyter Notebook and navigate to the notebooks directory to run the desired notebooks:

   jupyter notebook

Requirements

This project requires the following:

• Python 3.10.12
• Jupyter Notebook
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn

You can find a complete list of libraries in the requirements.txt file, which can be installed using pip.

Contributing

Contributions to this project are welcome! If you would like to contribute, please follow these steps:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature-branch-name

3. Make your changes and commit them:

   git commit -m "Add a meaningful commit message"

4. Push your branch:

   git push origin feature-branch-name

5. Open a pull request.