Spark Machine Learning Project (House Sale Price Prediction)

About Spark Machine Learning Project (House Sale Price Prediction)

Are you looking to build

real-world machine learning projects

using

Apache Spark

? Do you want to learn how to work with

big data

, build

end-to-end ML pipelines

, and apply your skills to a

practical use case

? If yes, this course is for you! In this

hands-on project-based course

, we will use

Apache Spark MLlib

to build a

House Sale Price Prediction model

from scratch. You’ll go beyond theory and actually implement a complete machine learning workflow—covering

data ingestion, preprocessing, feature engineering, model training, evaluation, and visualization

—all inside

Apache Zeppelin notebooks

and

Databricks

. Whether you are a

data engineering beginner

, a

machine learning enthusiast

, or a

professional preparing for real-world Spark projects

, this course will give you the confidence and skills to apply Spark MLlib to solve real business problems. What makes this course unique?

Project-based learning

: Instead of just slides, you’ll learn by building an

end-to-end project

on house price prediction.

Step-by-step environment setup

: We’ll guide you through

installing Java, Apache Zeppelin, Docker, and Spark

on both Ubuntu and Windows.

Hands-on with Zeppelin

: Learn how to

write, run, and visualize Spark code

inside Zeppelin notebooks.

Spark MLlib in action

: From

RDDs and DataFrames

to

pipelines and regression models

, you’ll gain practical experience in Spark’s machine learning library.

Performance insights

: Learn how to

track jobs and optimize performance

when working with large datasets.

Flexible workflow

: Work locally with Zeppelin or on the cloud with

Databricks free account

. What you’ll work on in the project

Load and explore a

real-world house sales dataset

Use

StringIndexer

to handle categorical variables

Apply

VectorAssembler

to prepare training data

Train a

regression model

in Spark MLlib

Test and evaluate the model with

RMSE (Root Mean Squared Error)

Visualize and interpret model results for

business insights

By the end of the course, you will have built a

complete Spark ML project

and gained skills you can confidently apply in

data science, data engineering, or machine learning roles

. If you want to master

Spark MLlib

through a real-world project and add an impressive machine learning use case to your portfolio, this course is the perfect place to start!