Spark Machine Learning Project (House Sale Price Prediction) for beginner using Databricks Notebook (Unofficial)
Are you looking to build real-world machine learning projects using Apache Spark?
What you’ll learn
- Understand the end-to-end workflow of a Spark ML project..
- Set up the environment by installing Java, Apache Zeppelin, Docker, and Spark..
- Work with Zeppelin notebooks for running Spark jobs and visualizations..
- Understand the house sales dataset and prepare it for machine learning..
- Perform data preprocessing and feature engineering using Spark MLlib..
- Use StringIndexer for handling categorical features..
- Apply VectorAssembler to transform multiple features into a single vector column..
- Split data into training and testing sets for machine learning tasks..
- Train a regression model in Spark MLlib for predicting house sale prices..
- Test and evaluate the regression model with metrics like RMSE..
- Visualize outputs and interpret model results for business insights..
- Run Spark jobs both in Apache Zeppelin and in Databricks (cloud environment)..
- Gain practical experience with Spark DataFrames, SQL queries, caching, and job tracking..
- Build confidence to apply Spark MLlib in real-world business projects..
Course Content
- Introduction to the Course –> 6 lectures • 19min.
- Setting Up the Environment –> 13 lectures • 33min.
- Download Resources –> 2 lectures • 2min.
- Zeppelin Basics –> 8 lectures • 1hr.
- Zeppelin with Apache Spark –> 5 lectures • 44min.
- Machine Learning Project –> 12 lectures • 53min.
- Introduction –> 1 lecture • 4min.
- Download Resources –> 1 lecture • 1min.
- Project Begins –> 14 lectures • 1hr 21min.
Requirements
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!