linear_regression_example_pyspark

PySpark Linear Regression

This repository contains code examples and tutorials for implementing linear regression using PySpark.

Table of Contents

• Introduction
• Setup
• Usage

Introduction

Linear regression is a fundamental statistical technique used for modeling the relationship between a dependent variable and one or more independent variables. PySpark, the Python API for Apache Spark, provides powerful capabilities for distributed computing and machine learning, making it suitable for implementing linear regression on large-scale datasets.

This repository provides hands-on examples and tutorials to help you learn and understand how to implement linear regression using PySpark. Whether you're new to PySpark or looking to deepen your understanding of linear regression, you'll find practical examples and code snippets to guide you through the process.

Setup

To run the code examples in this repository, you'll need to have Python and Apache Spark installed on your system. You can install PySpark using pip:

bash pip install pyspark Additionally, make sure you have a compatible version of Java installed on your system, as Apache Spark requires Java to run.

Usage You can find the code examples and tutorials in the examples directory. Each example demonstrates a different aspect of implementing linear regression using PySpark, such as data preprocessing, feature engineering, model training, and evaluation.

To run an example, navigate to its directory and execute the Python script using spark-submit. For example:

bash Copy code spark-submit linear_regression_example_pyspark.py Follow the instructions within each example to understand the code and experiment with different parameters and settings.