Name: Spark Machine Learning Project (House Sale Price Prediction)
Availability: InStock

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.

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.

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