Heart Attack and Diabetes Prediction Project in Apache Spark
What you'll learn
- In this course you will implement Spark Machine Learning Project 2 Mini Projects 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
- Data Preprocessing: Handle messy, large-scale healthcare datasets and prepare them for analysis.
- Machine Learning Pipelines: Build scalable ML workflows using Apache Spark’s MLlib for real-time predictions.
- Heart Attack Risk Prediction: Develop a model to predict heart attack risks based on patient data, enabling early interventions.
- Diabetes Diagnosis: Create a classification model to predict diabetes, supporting improved healthcare outcomes.
- Performance Optimization: Learn to tune and optimize Spark jobs for efficiency and scalability in production.
Requirements
- Apache Spark basic and Scala fundamental knowledge is required and SQL Basics along with Machine Learning 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.
Description
Apache Spark Project - Heart Attack and Diabetes Prediction Project in Apache Spark Machine Learning Project (2 mini-projects) for beginners using Databricks Notebook (Unofficial) (Community edition Server)
Are you ready to combine the power of big data analytics with Machine Learning to solve real-world healthcare challenges? This project-based course takes you step-by-step through building Heart Attack and Diabetes Prediction Models using the cutting-edge capabilities of Apache Spark. By working on two high-impact projects, you'll not only deepen your technical skills but also create solutions that demonstrate your ability to turn data into life-saving insights.
Through hands-on experience with Apache Spark and MLlib, this course equips you with the expertise to handle large healthcare datasets, implement Machine Learning pipelines, and deliver actionable predictions. Whether you’re looking to advance your career in data science, big data, or healthcare analytics, this course positions you as a sought-after professional.
What You’ll Learn:
Data Preprocessing: Handle messy, large-scale healthcare datasets and prepare them for analysis.
Machine Learning Pipelines: Build scalable ML workflows using Apache Spark’s MLlib for real-time predictions.
Heart Attack Risk Prediction: Develop a model to predict heart attack risks based on patient data, enabling early interventions.
Diabetes Diagnosis: Create a classification model to predict diabetes, supporting improved healthcare outcomes.
Performance Optimization: Learn to tune and optimize Spark jobs for efficiency and scalability in production.
Real-World Benefits:
Industry-Relevant Skills: Gain expertise in solving real-world healthcare challenges using big data and AI.
Portfolio-Worthy Projects: Build two impactful projects to showcase your skills to potential employers.
Career-Ready Knowledge: Position yourself as a big data and ML professional with a focus on healthcare applications.
Who Should Enroll:
Data Scientists & Machine Learning Engineers eager to build real-world projects that make a difference.
Big Data Professionals looking to integrate Machine Learning into their Spark workflows.
Healthcare Analysts & IT Experts interested in leveraging big data for predictive analytics in healthcare.
In this Data science Machine Learning project, we will create
1) Heart Disease Prediction
2) Diabetes Prediction
using a few algorithms of the predictive models.
Explore Apache Spark and Machine Learning on the Databricks platform.
Launching Spark Cluster
Process that data using a Machine Learning model (Spark ML Library)
Hands-on learning
Real time Use Case
Create a Data Pipeline
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 using Apache Spark
1) Heart Disease Prediction using Decision Tree Classification Model
2) Diabetes Prediction using Logistic Regression Model and One-vs-Rest classifier (a.k.a. One-vs-All) Model
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.
Take the leap into the future of healthcare analytics. Enroll now to master Apache Spark and MLlib while building projects that save lives and drive your career forward!
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 build real-world projects that make a difference.
- Big Data Professionals looking to integrate Machine Learning into their Spark workflows.
- Healthcare Analysts & IT Experts interested in leveraging big data for predictive analytics in healthcare.
Instructor
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!!