[100% Off] Spark Machine Learning Project (House Sale Price Prediction)

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

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.

Requirements

  • Basic knowledge of programming (Scala or Python familiarity is helpful but not mandatory).
  • A computer with Windows, Linux, or MacOS.
  • Willingness to install software (Java, Apache Zeppelin, Docker, or Databricks free account).
  • Basic understanding of machine learning concepts (regression, training, testing).
  • No prior knowledge of Spark MLlib is required — everything will be taught from scratch.

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!

Author(s): Bigdata Engineer
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