Build a House Price Prediction Model with Spark ML

Are you ready to dive into the world of machine learning with Apache Spark? In this hands-on project-based course, you will learn how to create a House Sale Price Prediction model from the ground up using Spark MLlib. This course is tailored for those eager to gain practical experience in building real-world machine learning projects. With a focus on hands-on learning, you will implement an end-to-end machine learning workflow that includes data ingestion, preprocessing, feature engineering, model training, evaluation, and visualization—all within the immersive environment of Apache Zeppelin notebooks and Databricks.

No prior experience with Spark MLlib is necessary, as this course is designed to guide you through every step, from setting up your environment to executing your machine learning tasks. You’ll explore a real-world house sales dataset and apply techniques such as StringIndexer and VectorAssembler to prepare your data for training. As you progress, you’ll train and evaluate your regression model, optimizing its performance and visualizing your results to extract business insights.

By the end of the course, not only will you have completed a compelling project, but you will also have acquired a robust skill set that you can confidently apply in data science, engineering, or machine learning roles. Whether you are a beginner or an aspiring professional, this project will elevate your portfolio and demonstrate your competency in handling complex data tasks using Apache Spark.

What you will 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:

• Sections: 9
• Lectures: 62
• Duration: 4h 55m

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.

Who is it for?

• Data Engineers & Big Data Developers who want to add machine learning with Spark MLlib to their toolkit.
• Data Scientists & ML Engineers who want to run scalable machine learning projects on Spark.
• Students & Beginners who want to learn Spark MLlib through a hands-on, project-based approach.
• Software Developers & Analysts looking to apply Spark for predictive analytics.
• Anyone preparing for interviews in data engineering or Spark-related roles who wants real project experience.
• Professionals who want to enhance their portfolio with a practical machine learning project on house price prediction.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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