Apache Spark Machine Learning Project (House Sale Price Prediction)
Apache Spark Machine Learning Project (House Sale Price Prediction) for beginner using Databricks Notebook (Unofficial)
Updated on Sep, 2024
Language - English
Development ,Database and Design Development,
Lectures -15
Resources -2
Duration -1 hours
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Course Description
Apache 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 the 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
About Databricks:
Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.
Goals
- 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
Prerequisites
- Apache Spark basic and Scala fundamental knowledge is required and SQL Basics
- Following browsers on Windows, Linux or macOS desktop:
- Google Chrome (Latest version), Firefox (Latest version), Safari (Latest version), Microsoft Edge* (Latest version)
- Internet Explorer 11* on Windows 7, 8, or 10 (with latest Windows updates applied)
- *You might see performance degradation for some features on Microsoft Edge and Internet Explorer.
- The following browsers are not supported:
- Mobile browsers.
- Beta, “preview,” or otherwise pre-release versions of desktop browsers.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
-
Introduction
03:45
03:45
-
Download Resources
Project Begins
12 Lectures
Instructor Details
Bigdata Engineer
I am Solution Architect with 12+ year’s of experience in Banking, Telecommunication and Financial Services industry across a diverse range of roles in Credit Card, Payments, Data Warehouse and Data Center programmes
My role as Bigdata and Cloud Architect to work as part of Bigdata team to provide Software Solution.
Responsibilities includes,
- Support all Hadoop related issues
- Benchmark existing systems, Analyse existing system challenges/bottlenecks and Propose right solutions to eliminate them based on various Big Data technologies
- Analyse and Define pros and cons of various technologies and platforms
- Define use cases, solutions and recommendations
- Define Big Data strategy
- Perform detailed analysis of business problems and technical environments
- Define pragmatic Big Data solution based on customer requirements analysis
- Define pragmatic Big Data Cluster recommendations
- Educate customers on various Big Data technologies to help them understand pros and cons of Big Data
- Data Governance
- Build Tools to improve developer productivity and implement standard practices
I am sure the knowledge in these courses can give you extra power to win in life.
All the best!!
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