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Easy Learning with Spark Machine Learning Project (House Sale Price Prediction)
Development > Data Science
4.5 h
£14.99 £12.99
4.3 ★★★★★
18589 students

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Language: English

Master Real Estate Price Prediction with Apache Spark

What you will learn:

  • Build a complete machine learning project to predict house sale prices.
  • Master Apache Spark (Scala & PySpark) and Spark MLlib.
  • Utilize the Databricks platform for large-scale data processing.
  • Perform comprehensive data exploration and preprocessing.
  • Build and evaluate a robust linear regression model.
  • Master feature engineering techniques using StringIndexer and VectorAssembler.
  • Visualize data effectively using Matplotlib and Seaborn.
  • Create a data pipeline and deploy your project.
  • Understand model evaluation metrics such as RMSE.
  • Gain practical, hands-on experience in big data and machine learning.

Description

Unlock the power of big data and machine learning with our comprehensive course on real estate price prediction using Apache Spark. This project-based course is designed for beginners, guiding you through the entire process from data exploration to model deployment. You'll learn to leverage Apache Spark (Scala & PySpark) and Databricks to build a robust machine learning pipeline, gaining practical experience in one of the most in-demand fields.

This course uses a real-world housing dataset to teach you essential skills such as data loading and exploration with Spark SQL, preprocessing techniques for categorical and numerical features (including StringIndexer and VectorAssembler), building and evaluating a Linear Regression model using Spark MLlib, data splitting for training and testing, and data visualization with Matplotlib and Seaborn within the Databricks environment. We'll also cover key concepts like calculating model performance using Root Mean Square Error (RMSE) and best practices for working within the Databricks platform.

Whether you're a student, aspiring data scientist, data engineer, or simply curious about predictive analytics, this course provides hands-on experience that will significantly boost your career prospects. You'll gain practical expertise in utilizing Spark for large-scale data processing, and learn to create a polished, deployable machine learning project that you can proudly showcase to potential employers. We cover setting up your environment, handling data pipelines, and visualizing your results, equipping you with a comprehensive toolkit for future machine learning projects.

Dive in and master the art of predictive modeling today!

Curriculum

Course Introduction & Setup

This section sets the stage for the project. You'll be introduced to the course objectives, the tools you'll be using (Apache Spark, Spark MLlib, Databricks), the project overview, and the structure of the housing dataset. Hands-on lectures cover the essential steps of setting up your Java environment, installing Docker (on Windows and Ubuntu), and configuring the Apache Zeppelin interpreter to connect with your Spark environment. The section also introduces you to the fundamentals of Apache Zeppelin itself, covering its key features, interface, and how to use various charting options.

Data Exploration and Preprocessing

Here, you'll dive into the real-world dataset, learning how to load and explore the data using Spark SQL. The focus shifts to data preprocessing, covering techniques for handling categorical and numerical features. You'll master the use of Spark MLlib's StringIndexer and VectorAssembler to prepare your data for model training. This section prepares you for the core machine learning tasks ahead.

Building & Evaluating the Machine Learning Model

This section is the core of the project. You'll learn to build a linear regression model using Spark MLlib, split your data into training and testing sets, train your model on the training data, and then test its performance on unseen data. Lectures detail the process of evaluating your model using the Root Mean Square Error (RMSE) metric, a crucial step in assessing the accuracy and reliability of your predictions. You’ll also learn effective data visualization techniques to represent your findings.

Databricks Project Implementation

This section focuses on implementing the house price prediction project within the Databricks environment. You will learn to launch a Spark cluster, create a data pipeline, process data using the Spark ML Library, and visualize data using Databricks notebooks. Detailed lectures cover the creation of a Databricks account, cluster provisioning, and best practices for working within the Databricks environment. This culminates in a complete, deployable project you can share with potential employers.