https://github.com/nabilshadman/pyspark-data-mining
A collection of data mining tasks done using PySpark
https://github.com/nabilshadman/pyspark-data-mining
data-mining pyspark
Last synced: 24 days ago
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A collection of data mining tasks done using PySpark
- Host: GitHub
- URL: https://github.com/nabilshadman/pyspark-data-mining
- Owner: nabilshadman
- Created: 2023-04-19T13:49:04.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-04T12:14:03.000Z (5 months ago)
- Last Synced: 2025-02-09T13:13:00.713Z (3 months ago)
- Topics: data-mining, pyspark
- Language: Jupyter Notebook
- Homepage:
- Size: 61.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Data Mining with PySpark
A comprehensive exploration of data mining techniques using PySpark, featuring DataFrame operations and machine learning implementations. This project demonstrates the power and scalability of PySpark for big data analytics through practical examples.
## 🎯 Features
- **DataFrame Operations**: Advanced data manipulation and analysis using Spark SQL
- **Machine Learning**: Implementation of clustering algorithms like K-means
- **Data Visualization**: Integration with matplotlib for result visualization
- **Performance Optimization**: Vectorized operations with Apache Arrow
- **Interactive Analysis**: Jupyter notebook demonstrations
## 🛠️ Technical Components
### SparkDataFrame.ipynb
- Spark SQL fundamentals
- DataFrame creation and manipulation
- Data transformation and cleaning
- Advanced querying techniques
- Pandas UDF implementation
- Vector assembly operations
### SparkML.ipynb
- K-means clustering implementation
- Comparative analysis with scikit-learn
- Feature engineering
- Model evaluation and visualization
- Cluster prediction and analysis
## 📋 Prerequisites
- Python 3.8+
- PySpark 3.0+
- Jupyter Notebook
- Required Python packages:
```
pyspark
pandas
numpy
matplotlib
scikit-learn
```
## 🚀 Getting Started
1. Clone the repository
```bash
git clone https://github.com/yourusername/pyspark-data-mining.git
cd pyspark-data-mining
```
2. Install dependencies
```bash
pip install -r requirements.txt
```
3. Launch Jupyter Notebook
```bash
jupyter notebook
```
4. Open the notebooks:
- `SparkDataFrame.ipynb` for DataFrame operations
- `SparkML.ipynb` for machine learning examples
## 📊 Sample Analysis
The project includes two main analytical components:
### DataFrame Operations
- Data cleaning and preprocessing
- Complex aggregations
- Window functions
- Custom UDF implementations
### Machine Learning
- K-means clustering on player statistics
- Comparison between Spark ML and scikit-learn implementations
- Visualization of clustering results
- Model performance analysis
## 📁 Project Structure
```
pyspark-data-mining/
├── SparkDataFrame.ipynb # DataFrame operations and analysis
├── SparkML.ipynb # Machine learning implementations
└── README.md # Project documentation
```
## 💡 Key Concepts Covered
- Spark Session management
- Data frame operations
- SQL query execution
- Machine learning pipeline creation
- Cluster analysis and visualization
- Performance optimization techniques
## 🔍 Use Cases
- Large-scale data processing
- Exploratory data analysis
- Pattern recognition in datasets
- Comparative analysis of clustering algorithms
- Performance benchmarking
## 📚 Resources
- [Apache Spark Documentation](https://spark.apache.org/docs/latest/)
- [PySpark Documentation](https://spark.apache.org/docs/latest/api/python/)
- [Spark ML Guide](https://spark.apache.org/docs/latest/ml-guide.html)
- [Jupyter Notebook Documentation](https://jupyter.org/documentation)
## 📞 Contact
For questions and feedback, please open an issue in the repository.