Build Apache Spark Machine Learning Project for eCommerce
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Build Apache Spark Machine Learning Project for eCommerce
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 647 MB
Genre: eLearning Video | Duration: 17 lectures (1 hour, 49 mins) | Language: English
Build Apache Spark Machine Learning Project for eCommerce Company that sells clothes online (Revenue Prediction Model)
What you'll learn
In this Spark Project course you will implement Predicting eCommerce Customer Revenue Prediction Project in Apache Spark (ML) using Databricks Notebook (Community edition server)
You will be able to learn and Explore Apache Spark and Machine Learning on the Databricks platform.
You will be able to 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
Requirements
Apache Spark basic and Scala fundamental knowledge is required and SQL Basics along with Machine Learning
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.
Description
Build Apache Spark Machine Learning Project for an eCommerce Company that sells clothes online (Revenue Prediction Model)
We will learn the most important aspect of Spark Machine learning (Spark MLlib):
Apache Spark fundamentals and implementing spark machine learning
Importing and Working with Datasets
Process data using a Machine Learning model using spark MLlib
Build and train a Linear regression model
Test and analyze the model
Learn the latest Big Data Technology - Spark! And learn to use it with one of the most popular programming languages, Scala!
One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!
Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 3.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!
Project Details:
This project is about an eCommerce company that sells clothes online.
This project is about customers who buy clothes online.
The store offers in-store style and clothing advice sessions.
Customers come into the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want.
We need to predict the future spending of Customer(ie Revenue for Company ) so business strategies can be made to convert "Customer" to "Loyalty Customer"
In this Data Science Machine Learning project, we will create an eCommerce Customer Revenue Prediction Project using Apache Spark Machine Learning Models using Linear Regression, one of the predictive models.
Explore Apache Spark and Machine Learning on the Databricks platform.
Launching Spark Cluster
Create a Data Pipeline
A 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
eCommerce Customer Revenue Prediction Project 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.
Who this course is for:
Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist
Download link:
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