A comprehensive collection of data mining algorithms, techniques, and educational resources for machine learning and data science.
- Overview
- Repository Structure
- Python Programming
- Data Structures
- Exploratory Data Analysis (EDA)
- Supervised Learning
- Unsupervised Learning
- Extra Packages & Tools
- Getting Started
- Prerequisites
- Installation
- Usage
- Contributing
- License
- Contact
This repository contains a comprehensive collection of data mining and machine learning implementations, tutorials, and examples. It covers fundamental concepts from basic Python programming to advanced machine learning algorithms, making it suitable for both beginners and advanced practitioners.
Data-Mining/
├── Python/ # Python programming fundamentals
├── Data_Structure/ # Data structure implementations
├── EDA/ # Exploratory Data Analysis
├── Supervised_Learning/ # Supervised ML algorithms
├── Unsupervised_Learning/ # Unsupervised ML algorithms
├── Extra_Packages/ # Additional tools and packages
├── NetworkX/ # Network analysis examples
├── LinearProgramming/ # Linear programming solutions
├── MonteCarlo/ # Monte Carlo simulations
└── Temp/ # Temporary and experimental code
- Python Basics: Variables, data types, control structures
- Python Intermediate: Functions, classes, error handling
- Python Advanced: Object-oriented programming, file I/O
- NumPy: Numerical computing and array operations
- Pandas: Data manipulation and analysis
- Matplotlib: Data visualization and plotting
- Seaborn: Statistical data visualization
- SciPy: Scientific computing and optimization
- Filter, map, and reduce operations
- Functional programming paradigms
- Trie: Efficient string searching and prefix matching
- PDF Parsing: Document processing with PyMuPDF and pdfplumber
- Advanced data structure implementations
- Data Cleaning: Handling missing values and outliers
- Data Transformation: Scaling, normalization, encoding
- Feature Engineering: Creating and selecting relevant features
- Polars: High-performance DataFrame operations
- Statistical Analysis: Descriptive and inferential statistics
- Data Imputation: Techniques for handling missing data
- Decision Trees: Tree-based classification with entropy and gini criteria
- K-Nearest Neighbors (KNN): Instance-based learning algorithm
- Support Vector Machines (SVM): Kernel-based classification
- Logistic Regression: Linear classification for binary and multiclass problems
- Naive Bayes: Probabilistic classification based on Bayes' theorem
- Random Forest: Ensemble method using multiple decision trees
- Cross-validation techniques
- Performance metrics (accuracy, precision, recall, F1-score)
- ROC curves and AUC analysis
- K-Means: Centroid-based clustering
- Agglomerative Clustering: Hierarchical clustering approach
- Affinity Propagation: Message-passing clustering
- Mean Shift: Mode-seeking clustering algorithm
- Principal Component Analysis (PCA)
- Feature selection techniques
- Data visualization methods
- Cryptography Package: Encryption and decryption implementations
- Security best practices and examples
- Request Package: HTTP requests and API interactions
- EarthAccess: NASA Earth data access tools
- PyVis: Interactive network visualizations
- Xarray: Multi-dimensional data analysis
- NetworkX: Graph theory and network analysis
- Environment Management: Virtual environment setup
- Console Output: Logging and output management
- PyCharm Integration: IDE configuration and tips
Before running the code in this repository, ensure you have the following installed:
- Python 3.7 or higher
- pip (Python package installer)
- Git
-
Clone the repository
git clone https://github.com/username/Data-Mining.git cd Data-Mining -
Create a virtual environment (recommended)
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
-
Install required packages
pip install -r requirements.txt
Navigate to any directory of interest and run the Python scripts:
# Example: Running a decision tree example
cd Supervised_Learning/Decision_Tree/DT
python Sample_DT_Example_balance.py
# Example: Running a clustering algorithm
cd Unsupervised_Learning/Kmean
python kmean_example.py
# Example: Basic Python programming
cd Python/01-Python-Programming/1-Lecture_1\(Python\ Basics\)/Lecture\ Code
python 1-Integer_values.py- Start with Python Programming basics
- Learn NumPy and Pandas for data manipulation
- Explore Matplotlib and Seaborn for visualization
- Practice with EDA techniques
- Dive into Supervised Learning algorithms
- Experiment with Unsupervised Learning methods
- Work on Data Structures implementations
- Explore NetworkX for graph analysis
- Study Linear Programming optimization
- Implement Monte Carlo simulations
- Contribute to Extra Packages development
- Create custom algorithms and improvements
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Author: Amir Jafari
- Email: ajafari@gwu.edu
- GitHub: @amir-jafari
- Thanks to all contributors who have helped improve this repository
- Special thanks to the open-source community for the amazing libraries used
- Educational institutions and resources that inspired this collection
⭐ Star this repository if you find it helpful! ⭐