https://github.com/mohammadreza-mohammadi94/pyspark-analytics-hub
A PySpark repository for data analysis, machine learning projects, and hands-on exercises. Explore scalable data processing and advanced ML workflows with Spark.
https://github.com/mohammadreza-mohammadi94/pyspark-analytics-hub
large-scale-pretraining machine-learning pyspark pyspark-mllib pyspark-python python
Last synced: 3 months ago
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A PySpark repository for data analysis, machine learning projects, and hands-on exercises. Explore scalable data processing and advanced ML workflows with Spark.
- Host: GitHub
- URL: https://github.com/mohammadreza-mohammadi94/pyspark-analytics-hub
- Owner: mohammadreza-mohammadi94
- Created: 2024-12-28T21:27:29.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-01-20T21:07:27.000Z (4 months ago)
- Last Synced: 2025-01-20T22:21:35.585Z (4 months ago)
- Topics: large-scale-pretraining, machine-learning, pyspark, pyspark-mllib, pyspark-python, python
- Language: Jupyter Notebook
- Homepage:
- Size: 12.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PySpark Analysis & ML Projects
Welcome to the **PySpark Analysis & ML Projects** repository! This repository showcases the power and flexibility of **PySpark** for large-scale data processing and machine learning tasks. It contains a collection of projects and exercises designed to demonstrate PySpark's capabilities for data analysis, modeling, and machine learning workflows.
## Overview
Apache Spark, with its distributed computing model, is widely used for processing massive datasets in a fast and efficient manner. PySpark is the Python API for Apache Spark, and in this repository, you'll explore how to leverage PySpark for data analysis and machine learning tasks that scale beyond traditional libraries.
The aim of this repository is to help you understand and apply PySpark in real-world scenarios, and it includes:
- Data Preprocessing with PySpark
- Exploratory Data Analysis (EDA) on large datasets
- Machine Learning Models using PySpark's MLlib
- Examples of scalable algorithms and solutions
## Features
- **Data Analysis**: Learn how to process large datasets using Spark DataFrames and RDDs.
- **MLlib**: Explore machine learning models like regression, classification, clustering, and more.
- **Scalability**: Demonstrate how PySpark scales to handle massive datasets that don't fit in memory.
- **Real-World Datasets**: Work with diverse datasets and understand the application of PySpark in various data domains.
## Installation
To use the code in this repository, you need to have **PySpark** installed. You can install it via pip:
```bash
pip install pyspark
```
## Usage
Clone this repository to your local machine:
```bash
git clone https://github.com/yourusername/pyspark-ml-lab.git
cd pyspark-ml-lab
```
Run any of the provided scripts in your local Spark environment. Each project or exercise typically includes its own set of instructions and setup.
## Example Projects
Here are a few examples of what you will find in this repository:
### 1. Data Preprocessing with PySpark
- Cleaning and transforming large datasets
- Handling missing values, filtering, and aggregating data
### 2. Machine Learning with PySpark
- Classification models (Logistic Regression, Random Forest)
- Clustering with KMeans
- Regression tasks (Linear Regression)
### 3. Advanced PySpark Features
- Window functions for advanced data aggregation
- Performance tuning for large-scale datasets
## Contributing
Contributions are welcome! If you have any suggestions, bug fixes, or improvements, feel free to open an issue or submit a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Thanks to the creators of **Apache Spark** for providing the powerful engine behind PySpark.
- All datasets used are publicly available for educational purposes.