• In this course you will implement Spark Machine Learning Project House Sale Price Prediction in Apache Spark using Databricks Notebook(Community edition server)
• Launching Apache Spark Cluster
• Process that data using a Machine Learning model (Spark ML Library)
• Hands-on learning
• Create a Data Pipeline
• Real-time Use Case
• Publish the Project on Web to Impress your recruiter
• Graphical  Representation of Data using Databricks notebook.
• Transform structured data using SparkSQL and DataFrames
• Data Exploration & Preprocessing: Clean, transform, and analyze large-scale real estate data to uncover key trends and patterns.
• Feature Engineering: Identify the most influential factors driving house prices, such as location, size, and market trends.
• Machine Learning Pipelines: Build predictive models using Spark’s MLlib to estimate house sale prices with precision.
• Model Evaluation & Optimization: Assess model performance and fine-tune parameters to enhance accuracy and reliability.
• Scalable Data Processing: Leverage Spark’s distributed computing to handle and analyze massive datasets efficiently.

Are you a beginner looking to break into the world of Machine Learning and Big Data? This hands-on project-based course is your perfect starting point!

In “House Sale Price Prediction for Beginners using Apache Spark and Apache Zeppelin,” you will learn how to build a complete machine learning pipeline to predict housing prices using real-world data. Leveraging the power of Apache Spark (Scala & PySpark) and the visualization capabilities of Apache Zeppelin, you will explore, prepare, model, and evaluate a regression model step-by-step.

Whether you’re a data enthusiast, student, or aspiring data engineer/scientist, this course gives you practical experience in one of the most in-demand fields today—Big Data and Machine Learning.

What You Will Learn:

• Understand the structure of a real-world housing dataset

• Load and explore data using Spark SQL

• Preprocess categorical and numerical features

• Use StringIndexer and VectorAssembler in feature engineering

• Build and evaluate a Linear Regression model using Spark MLlib

• Split data for training and testing

• Visualize predictions with Matplotlib and Seaborn inside Zeppelin

• Calculate model performance using Root Mean Square Error (RMSE)

Tools and Technologies Used:

• Apache Spark (Scala + PySpark)

• Apache Zeppelin

• Spark MLlib (Machine Learning Library)

• Matplotlib & Seaborn for visualization

Who This Course is For:

• Beginners in data science or big data

• Students working on academic ML projects

• Aspiring data engineers and analysts

• Anyone curious about predictive analytics using real-world datasets

By the end of the course, you’ll have built a complete machine learning project from scratch, equipped with the foundational knowledge to move on to more advanced topics in Spark and data science.

Spark Machine Learning Project (House Sale Price Prediction) for beginners using Databricks Notebook (Unofficial) (Community edition Server)

In this Data science Machine Learning project, we will predict the sales prices in the Housing data set using LinearRegression one of the predictive models.

• Explore Apache Spark and Machine Learning on the Databricks platform.

• Launching Spark Cluster

• Create a Data Pipeline

• Process that data using a Machine Learning model (Spark ML Library)

• Hands-on learning

• Real time Use Case

• Publish the Project on Web to Impress your recruiter

• Graphical Representation of Data using Databricks notebook.

• Transform structured data using SparkSQL and DataFrames

Predict sales prices a Real time Use Case on Apache Spark

Who this course is for:

• Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist
• Data Scientists & Machine Learning Engineers eager to gain real-world experience with Spark and predictive modeling.
• Real Estate Professionals & Analysts wanting to leverage data-driven strategies for pricing and market analysis.
• Big Data & IT Professionals looking to expand their skillset in Spark and machine learning for real-world applications.