We may earn an affiliate commission when you visit our partners.
Priya Jha
In this 1 hour long project-based course, you will learn to build a logistic regression model using Pyspark MLLIB to classify patients as either diabetic or non-diabetic. We will use the popular Pima Indian Diabetes data set. Our goal is to use a simple logistic regression classifier from the pyspark Machine learning library for diabetes classification. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark.You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the educational purpose.
Read more
In this 1 hour long project-based course, you will learn to build a logistic regression model using Pyspark MLLIB to classify patients as either diabetic or non-diabetic. We will use the popular Pima Indian Diabetes data set. Our goal is to use a simple logistic regression classifier from the pyspark Machine learning library for diabetes classification. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark.You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the educational purpose.
Read more
In this 1 hour long project-based course, you will learn to build a logistic regression model using Pyspark MLLIB to classify patients as either diabetic or non-diabetic. We will use the popular Pima Indian Diabetes data set. Our goal is to use a simple logistic regression classifier from the pyspark Machine learning library for diabetes classification. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark.You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the educational purpose.
By the end of this project, you will be able to build the logistic regression classifier using Pyspark MLlib to classify between the diabetic and nondiabetic patients.You will also be able to setup and work with Pyspark on Google colab environment. Additionally, you will also be able to clean and prepare data for analysis.
You should be familiar with the Python Programming language and you should have a theoretical understanding of the Logistic Regression algorithm. You will need a free Gmail account to complete this project.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/i84b0j/diabetes
What's inside
Syllabus
Project Overview
In this 1 hour long project-based course, you will learn to build a logistic regression model using Pyspark MLLIB to classify patients as either diabetic or non-diabetic. We will use the popular Pima Indian Diabetes data set. Our goal is to use a simple logistic regression classifier from the pyspark Machine learning library for diabetes classification. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for learning purposes.By the end of this project, you will be able to build the logistic regression classifier using Pyspark MLlib to classify between the diabetic and nondiabetic patients. You will also be able to set up and work with Pyspark on the Google colab environment. Additionally, you will also be able to clean and prepare data for analysis.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for students interested in learning about logistic regression models and data classification for medical applications
Utilizes real-world data and a practical approach to demonstrate the application of ML techniques in medical diagnosis
Teaches students how to prepare and clean data, which is a crucial skill in real-world data analysis
Carries out the project using Google Colab, which provides a user-friendly environment for data analysis and model building
Requires familiarity with Python programming and a theoretical understanding of logistic regression, which may limit accessibility for beginners
The course assumes that students have access to a free Gmail account, which may not always be feasible
Save this course
Save Diabetes Prediction With Pyspark MLLIB to your list so you can find it easily later:
Save
Save
Reviews summary
Diabetes prediction & pyspark mllib
Learners say PySpark MLLib is mostly well received with engaging assignments but learners left wanting more.
Learners found assignments engaging
"Thank You for making course so simple to learn how to develop prediction model"
Learners expected more evaluation of models
"left much would have liked to see more model evaluation and comparison"
Learners wish for more depth
"More deep dive into Spark functionalities would have been great"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Diabetes Prediction With Pyspark MLLIB with these
activities:
Review a book on Machine Learning algorithms
Show steps
Provides a deeper understanding of the theoretical foundations of Logistic Regression and related algorithms.
View
Machine Learning in Action
on Amazon
Show steps
-
Read the book and make notes on key concepts.
-
Summarize the main ideas of the book in your own words.
Review basic statistical concepts
Show steps
Strengthens the foundation of statistical concepts necessary for understanding Logistic Regression.
Show steps
-
Re-read notes or textbook chapters on basic statistics and probability.
-
Solve practice problems to reinforce understanding of concepts such as mean, standard deviation, and hypothesis testing.
Review introductory Python programming concepts
Show steps
Refreshes the foundational concepts of Python programming for better retention and understanding of Logistic Regression models.
Show steps
-
Re-read notes or textbook chapters on basic Python syntax, data structures, and control flow.
-
Solve practice problems or coding exercises to reinforce understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on Logistic Regression using Pyspark
Show steps
Provides guided practice and application of Logistic Regression concepts within the context of Pyspark.
Show steps
-
Identify and select tutorials that cover Logistic Regression implementation using Pyspark.
-
Follow the tutorials step-by-step, implementing the code and understanding the underlying concepts.
-
Troubleshoot any errors or issues encountered during the tutorial.
Participate in a study group or discussion forum on Logistic Regression
Show steps
Fosters collaboration, knowledge sharing, and deeper understanding of Logistic Regression concepts.
Show steps
-
Join a study group or online discussion forum dedicated to Logistic Regression.
-
Participate in discussions, ask questions, and share insights.
-
Collaborate on projects or assignments.
Practice building Logistic Regression models with Pyspark
Show steps
Provides hands-on experience and reinforces skills in building Logistic Regression models using Pyspark.
Show steps
-
Find or create a dataset suitable for binary classification.
-
Load the dataset into a Pyspark DataFrame.
-
Preprocess the data, handling missing values and feature scaling.
-
Build and train a Logistic Regression model using Pyspark MLlib.
-
Evaluate the model's performance using metrics such as accuracy and F1 score.
Create a data visualization to represent the results of a Logistic Regression model
Show steps
Enhances understanding of model outcomes and allows for easier interpretation of results.
Show steps
-
Choose an appropriate data visualization technique.
-
Use a tool or library to create the visualization.
-
Interpret the visualization and draw insights.
Create a presentation or blog post on a Logistic Regression project
Show steps
Encourages students to synthesize their knowledge and communicate their understanding of Logistic Regression.
Show steps
-
Choose a topic related to Logistic Regression and Pyspark.
-
Research and gather relevant information.
-
Create a presentation or blog post that clearly explains the topic.
-
Share the presentation or blog post with others.
Review a book on Machine Learning algorithms
Show steps
Provides a deeper understanding of the theoretical foundations of Logistic Regression and related algorithms.
View
Machine Learning in Action
on Amazon
Show steps
Show steps
Show steps
- Read the book and make notes on key concepts.
- Summarize the main ideas of the book in your own words.
Review basic statistical concepts
Show steps
Strengthens the foundation of statistical concepts necessary for understanding Logistic Regression.
Show steps
Show steps
Show steps
- Re-read notes or textbook chapters on basic statistics and probability.
- Solve practice problems to reinforce understanding of concepts such as mean, standard deviation, and hypothesis testing.
Review introductory Python programming concepts
Show steps
Refreshes the foundational concepts of Python programming for better retention and understanding of Logistic Regression models.
Show steps
Show steps
Show steps
- Re-read notes or textbook chapters on basic Python syntax, data structures, and control flow.
- Solve practice problems or coding exercises to reinforce understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow tutorials on Logistic Regression using Pyspark
Show steps
Provides guided practice and application of Logistic Regression concepts within the context of Pyspark.
Show steps
Show steps
Show steps
- Identify and select tutorials that cover Logistic Regression implementation using Pyspark.
- Follow the tutorials step-by-step, implementing the code and understanding the underlying concepts.
- Troubleshoot any errors or issues encountered during the tutorial.
Participate in a study group or discussion forum on Logistic Regression
Show steps
Fosters collaboration, knowledge sharing, and deeper understanding of Logistic Regression concepts.
Show steps
Show steps
Show steps
- Join a study group or online discussion forum dedicated to Logistic Regression.
- Participate in discussions, ask questions, and share insights.
- Collaborate on projects or assignments.
Practice building Logistic Regression models with Pyspark
Show steps
Provides hands-on experience and reinforces skills in building Logistic Regression models using Pyspark.
Show steps
Show steps
Show steps
- Find or create a dataset suitable for binary classification.
- Load the dataset into a Pyspark DataFrame.
- Preprocess the data, handling missing values and feature scaling.
- Build and train a Logistic Regression model using Pyspark MLlib.
- Evaluate the model's performance using metrics such as accuracy and F1 score.
Create a data visualization to represent the results of a Logistic Regression model
Show steps
Enhances understanding of model outcomes and allows for easier interpretation of results.
Show steps
Show steps
Show steps
- Choose an appropriate data visualization technique.
- Use a tool or library to create the visualization.
- Interpret the visualization and draw insights.
Create a presentation or blog post on a Logistic Regression project
Show steps
Encourages students to synthesize their knowledge and communicate their understanding of Logistic Regression.
Show steps
Show steps
Show steps
- Choose a topic related to Logistic Regression and Pyspark.
- Research and gather relevant information.
- Create a presentation or blog post that clearly explains the topic.
- Share the presentation or blog post with others.
Career center
Learners who complete Diabetes Prediction With Pyspark MLLIB will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist uses scientific methods and statistical techniques to extract knowledge and actionable insights from data, enabling organizations to make informed decisions. This course in Diabetes Prediction using Pyspark MLLIB may serve as a springboard for aspiring Data Scientists, providing foundational knowledge in data analysis and machine learning. By understanding how to classify patients as diabetic or non-diabetic using logistic regression classifiers, learners develop a strong base in developing predictive models to solve real-world problems.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data, using statistical methods and techniques to draw conclusions from data. This course in Diabetes Prediction using Pyspark MLLIB aligns with a Statistician's responsibilities, providing hands-on experience in data cleaning, preparation, and analysis. By learning to work with Pyspark and apply logistic regression algorithms, learners gain valuable skills in statistical modeling and data-driven decision-making, enhancing their career prospects in this field.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models to solve complex problems. This course in Diabetes Prediction using Pyspark MLLIB aligns with a Machine Learning Engineer's role, providing practical experience in building and evaluating logistic regression models using Pyspark MLLIB. Learners develop expertise in feature engineering, model selection, and model evaluation, preparing them to contribute to the development of data-driven solutions in various industries.
Data Analyst
Data Analyst
A Data Analyst analyzes data to extract meaningful insights and inform decision-making. This course in Diabetes Prediction using Pyspark MLLIB caters to aspiring Data Analysts by providing foundational knowledge in data analysis and machine learning. Through hands-on experience in data cleaning, feature engineering, and model evaluation, learners develop essential skills for identifying trends, patterns, and insights from complex datasets, empowering them to drive informed decision-making within organizations.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course in Diabetes Prediction using Pyspark MLLIB can be beneficial for Software Engineers interested in specializing in data-driven software development. By gaining proficiency in Pyspark and logistic regression algorithms, learners enhance their ability to build and integrate machine learning models into software applications, making them valuable assets in the software industry.
Operational Research Analyst
Operational Research Analyst
An Operational Research Analyst uses mathematical and analytical techniques to solve complex business problems. This course in Diabetes Prediction using Pyspark MLLIB provides a strong foundation for aspiring Operational Research Analysts, offering practical experience in data analysis and modeling. By learning to apply logistic regression to real-world datasets, learners develop valuable skills in problem-solving, data interpretation, and decision-making, preparing them for success in this field.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and identifies areas for improvement. This course in Diabetes Prediction using Pyspark MLLIB may be useful for Business Analysts seeking to enhance their data analysis and modeling skills. By gaining proficiency in Pyspark and logistic regression, learners develop the ability to extract insights from complex datasets, enabling them to make data-driven recommendations and drive business decisions effectively.
Financial Analyst
Financial Analyst
A Financial Analyst evaluates financial performance and makes investment recommendations. This course in Diabetes Prediction using Pyspark MLLIB provides a basic understanding of data analysis and machine learning techniques, which can be beneficial for aspiring Financial Analysts. By learning to apply logistic regression to financial datasets, learners develop skills in data-driven decision-making, risk assessment, and investment analysis, enhancing their career prospects in the financial industry.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. This course in Diabetes Prediction using Pyspark MLLIB may be useful for Project Managers seeking to enhance their analytical and data-driven decision-making skills. By understanding the basics of data analysis and machine learning, learners gain the ability to interpret data, identify trends, and make informed decisions, enabling them to effectively manage projects and achieve successful outcomes.
Product Manager
Product Manager
A Product Manager defines the vision, roadmap, and features of a product. This course in Diabetes Prediction using Pyspark MLLIB can provide valuable insights for Product Managers seeking to leverage data and analytics in product development. By understanding the basics of data analysis and machine learning, learners gain the ability to understand customer needs, analyze market trends, and make data-driven decisions, enabling them to build and launch successful products.
Marketing Analyst
Marketing Analyst
A Marketing Analyst analyzes marketing campaigns and provides insights to improve marketing efforts. This course in Diabetes Prediction using Pyspark MLLIB may be useful for aspiring Marketing Analysts seeking to enhance their data analysis and modeling skills. By gaining proficiency in Pyspark and logistic regression, learners develop the ability to extract insights from marketing data, measure campaign effectiveness, and make data-driven recommendations, enabling them to contribute to successful marketing strategies.
Health Data Analyst
Health Data Analyst
A Health Data Analyst collects, analyzes, and interprets health data to improve healthcare outcomes. This course in Diabetes Prediction using Pyspark MLLIB aligns with the responsibilities of a Health Data Analyst, providing hands-on experience in data analysis and machine learning. By learning to apply logistic regression to healthcare datasets, learners develop valuable skills in identifying risk factors, predicting disease progression, and evaluating treatment effectiveness, enabling them to contribute to advancements in healthcare and improve patient outcomes.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to solve problems in biology and medicine. This course in Diabetes Prediction using Pyspark MLLIB can provide a strong foundation for aspiring Biostatisticians, offering practical experience in data analysis and modeling in a healthcare context. By learning to apply logistic regression to biological and medical datasets, learners develop valuable skills in analyzing clinical trials, evaluating treatment effectiveness, and identifying risk factors, preparing them for a successful career in biostatistics.
For more career information including salaries, visit:
OpenCourser.com/course/i84b0j/diabetes
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Diabetes Prediction With Pyspark MLLIB.
Comprehensive guide to machine learning with Python. It covers all the major machine learning algorithms, including supervised learning, unsupervised learning, and deep learning. This book will help you build end-to-end machine learning projects.
Is an excellent resource to learn the fundamentals of machine learning. It starts with basic concepts and progresses to more advanced techniques. This book will help you understand how to use Python's popular machine learning libraries, such as NumPy, Scikit-Learn, and TensorFlow.
Save
Comprehensive guide to deep learning. It covers all the major deep learning algorithms including convolutional neural networks, recurrent neural networks, and generative adversarial networks. This book will help you understand how to design and build deep learning models.
Comprehensive guide to machine learning using the R programming language. It covers all the major machine learning algorithms, including supervised learning, unsupervised learning, and deep learning. This book will help you build end-to-end machine learning projects with R.
Comprehensive guide to machine learning with the JavaScript programming language. It covers all the major machine learning algorithms, including supervised learning, unsupervised learning, and deep learning. This book will help you build end-to-end machine learning projects with JavaScript.
Comprehensive guide to machine learning with the Rust programming language. It covers all the major machine learning algorithms, including supervised learning, unsupervised learning, and deep learning. This book will help you build end-to-end machine learning projects with Rust.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i84b0j/diabetes
Share
Similar courses
Here are nine courses similar to
Diabetes Prediction With Pyspark MLLIB.
Breast Cancer Prediction Using Machine Learning
OpenCourser.com/course/sok1kb/breast
Breast Cancer Prediction Using Machine Learning
Most relevant
Graduate Admission Prediction with Pyspark ML
OpenCourser.com/course/uxdpws/graduate
Graduate Admission Prediction with Pyspark ML
Most relevant
Employee Attrition Prediction Using Machine Learning
OpenCourser.com/course/7dbvqx/employee
Employee Attrition Prediction Using Machine Learning
Most relevant
Predictive Analytics Using Apache Spark MLlib on Databricks
OpenCourser.com/course/ugnndd/predictive
Predictive Analytics Using Apache Spark MLlib on...
Most relevant
Classification Analysis
OpenCourser.com/course/y6nuor/classification
Classification Analysis
Most relevant
Machine Learning for Telecom Customers Churn Prediction
OpenCourser.com/course/u4lk3l/machine
Machine Learning for Telecom Customers Churn Prediction
Most relevant
Logistic Regression&application as Classification Algorithm
OpenCourser.com/course/b6a4tj/logistic
Logistic Regression&application as Classification...
Most relevant
Logistic Regression in R for Public Health
OpenCourser.com/course/zn4u9b/logistic
Logistic Regression in R for Public Health
Understanding and Applying Logistic Regression
OpenCourser.com/course/j0n4zv/understanding
Understanding and Applying Logistic Regression
Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Priya Jha
Language
English
Good to know
Suitable for students interested in learning about logistic regression models and data classification for medical applications
Utilizes real-world data and a practical approach to demonstrate the application of ML techniques in medical diagnosis
Teaches students how to prepare and clean data, which is a crucial skill in real-world data analysis
Carries out the project using Google Colab, which provides a user-friendly environment for data analysis and model building
Requires familiarity with Python programming and a theoretical understanding of logistic regression, which may limit accessibility for beginners
The course assumes that students have access to a free Gmail account, which may not always be feasible
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser