Spark Machine Learning Project (House Sale Price Prediction)

Spark Machine Learning Project (House Sale Price Prediction) for beginner using Databricks Notebook (Unofficial)

Description

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!

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