Free Udemy Course __ Spark Machine Learning Project (House Sale Price Prediction)

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

4.5 (16,850 students students enrolled) English
back-end Nosql
Spark Machine Learning Project (House Sale Price Prediction)

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.

Who This Course is 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.

Your Instructor

Bigdata Engineer

Bigdata Engineer

3.8 Instructor Rating

1,557 Reviews

134,469 Students

22 Courses

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