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:

pip install pyspark

Usage

Clone this repository to your local machine:

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 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.