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Ryan Ahmed
In this hands-on project, we will train Logistic Regression and XG-Boost models to predict whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually. This data was obtained from U.S. Census database and consists of features like occupation, age, native country, capital gain, education, and work class.
Read more
In this hands-on project, we will train Logistic Regression and XG-Boost models to predict whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually. This data was obtained from U.S. Census database and consists of features like occupation, age, native country, capital gain, education, and work class.
Read more
In this hands-on project, we will train Logistic Regression and XG-Boost models to predict whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually. This data was obtained from U.S. Census database and consists of features like occupation, age, native country, capital gain, education, and work class.
By the end of this project, you will be able to:
- Understand the theory and intuition behind Logistic Regression and XG-Boost models
- Import key Python libraries, dataset, and perform Exploratory Data Analysis like removing missing values, replacing characters, etc.
- Perform data visualization using Seaborn.
- Prepare the data to increase the predictive power of Machine Learning models by One-Hot Encoding, Label Encoding, and Train/Test Split
- Build and train Logistic Regression and XG-Boost models to classify the Income Bracket of U.S. Household.
- Assess the performance of trained model and ensure its generalization using various KPIs such as accuracy, precision and recall.
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/3cie1x/logistic
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Syllabus
Logistic Regression 101: US Household Income Bracket Classification
In this hands-on project, we will train Logistic regression and XG-Boost models to predict whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually. This data was obtained from U.S. Census database and consists of features like occupation, age, native country, capital gain, education, and work class.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides hands-on learning, enabling practical application of Logistic Regression and XG-Boost models
Covers key technical skills highly relevant to data analysis and machine learning roles
Emphasizes essential data preprocessing techniques, empowering learners with foundational knowledge
Suitable for beginners seeking an introduction to Logistic Regression and XG-Boost
Offers practical insights into real-world data through the use of U.S. Census data
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Save Logistic Regression 101: US Household Income Classification to your list so you can find it easily later:
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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 Logistic Regression 101: US Household Income Classification with these
activities:
Seek guidance from experienced professionals in Logistic Regression
Show steps
Connecting with experienced mentors will provide access to valuable insights, advice, and potential collaboration opportunities in Logistic Regression.
Browse courses on
Logistic Regression
Show steps
-
Attend industry events and conferences
-
Reach out to professionals on LinkedIn
-
Request mentorship through professional organizations
Review 'An Introduction to Statistical Learning'
Show steps
Reviewing the foundational concepts in statistical learning will strengthen understanding of the algorithms covered in the course.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
-
Read Chapters 1-3
-
Take notes on key concepts
-
Complete the exercises at the end of each chapter
Follow Coursera tutorials on Logistic Regression
Show steps
Completing guided tutorials will provide step-by-step practice in implementing Logistic Regression models.
Browse courses on
Logistic Regression
Show steps
-
Complete the 'Logistic Regression with Python' tutorial
-
Explore the 'Logistic Regression with R' tutorial
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete practice problems on Kaggle
Show steps
Solving practice problems on Kaggle will provide hands-on experience in applying Logistic Regression models.
Browse courses on
Logistic Regression
Show steps
-
Sign up for a Kaggle account
-
Join the 'Logistic Regression' competition
-
Submit solutions to the competition
Mentor junior students on Logistic Regression projects
Show steps
Mentoring others will deepen understanding of Logistic Regression concepts and enhance communication skills.
Browse courses on
Logistic Regression
Show steps
-
Reach out to junior students interested in Logistic Regression
-
Schedule regular mentorship sessions
-
Provide guidance on project ideas and implementation
Create a blog post on the applications of Logistic Regression
Show steps
Writing a blog post will reinforce understanding of Logistic Regression and its practical applications in various industries.
Browse courses on
Logistic Regression
Show steps
-
Research different use cases of Logistic Regression
-
Outline the blog post structure
-
Write the blog post
-
Share the blog post on social media
Participate in a Logistic Regression hackathon
Show steps
Participating in a hackathon will foster collaboration, enhance problem-solving skills, and encourage innovation in applying Logistic Regression.
Browse courses on
Logistic Regression
Show steps
-
Find a hackathon focused on Logistic Regression
-
Form a team or work independently
-
Develop a solution to the hackathon challenge
-
Present the solution to a panel of judges
Develop a Logistic Regression model for a real-world problem
Show steps
Undertaking a project will provide practical experience in applying Logistic Regression to solve real-world problems.
Browse courses on
Logistic Regression
Show steps
-
Identify a problem that can be solved using Logistic Regression
-
Gather and prepare data
-
Train and evaluate the Logistic Regression model
-
Deploy the model and monitor its performance
Seek guidance from experienced professionals in Logistic Regression
Show steps
Connecting with experienced mentors will provide access to valuable insights, advice, and potential collaboration opportunities in Logistic Regression.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Attend industry events and conferences
- Reach out to professionals on LinkedIn
- Request mentorship through professional organizations
Review 'An Introduction to Statistical Learning'
Show steps
Reviewing the foundational concepts in statistical learning will strengthen understanding of the algorithms covered in the course.
View
An Introduction to Statistical Learning: with...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-3
- Take notes on key concepts
- Complete the exercises at the end of each chapter
Follow Coursera tutorials on Logistic Regression
Show steps
Completing guided tutorials will provide step-by-step practice in implementing Logistic Regression models.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Complete the 'Logistic Regression with Python' tutorial
- Explore the 'Logistic Regression with R' tutorial
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete practice problems on Kaggle
Show steps
Solving practice problems on Kaggle will provide hands-on experience in applying Logistic Regression models.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Sign up for a Kaggle account
- Join the 'Logistic Regression' competition
- Submit solutions to the competition
Mentor junior students on Logistic Regression projects
Show steps
Mentoring others will deepen understanding of Logistic Regression concepts and enhance communication skills.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Reach out to junior students interested in Logistic Regression
- Schedule regular mentorship sessions
- Provide guidance on project ideas and implementation
Create a blog post on the applications of Logistic Regression
Show steps
Writing a blog post will reinforce understanding of Logistic Regression and its practical applications in various industries.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Research different use cases of Logistic Regression
- Outline the blog post structure
- Write the blog post
- Share the blog post on social media
Participate in a Logistic Regression hackathon
Show steps
Participating in a hackathon will foster collaboration, enhance problem-solving skills, and encourage innovation in applying Logistic Regression.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Find a hackathon focused on Logistic Regression
- Form a team or work independently
- Develop a solution to the hackathon challenge
- Present the solution to a panel of judges
Develop a Logistic Regression model for a real-world problem
Show steps
Undertaking a project will provide practical experience in applying Logistic Regression to solve real-world problems.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Identify a problem that can be solved using Logistic Regression
- Gather and prepare data
- Train and evaluate the Logistic Regression model
- Deploy the model and monitor its performance
Career center
Learners who complete Logistic Regression 101: US Household Income Classification will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. This course provides a solid foundation in logistic regression, a statistical model that is widely used in data science. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret data science models that can predict outcomes, such as whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course provides a solid foundation in logistic regression and XG-Boost, two machine learning algorithms that are widely used in industry. By understanding the theory and intuition behind these algorithms, you will be better equipped to build and deploy machine learning models that can solve real-world problems, such as predicting whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of statistics, programming, and optimization to solve business problems. This course provides a solid foundation in logistic regression, a statistical model that is widely used in operations research. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret operations research models that can help solve business problems, such as predicting whether a particular product will be successful.
Statistician
Statistician
Statisticians use their knowledge of statistics, programming, and data analysis to solve real-world problems. This course provides a solid foundation in logistic regression, a statistical model that is widely used in statistics. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret statistical models that can help solve problems, such as predicting whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually.
Software Engineer
Software Engineer
Software Engineers use their knowledge of programming, data structures, and algorithms to design and build software applications. This course provides a solid foundation in logistic regression, a statistical model that is widely used in software engineering. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret software engineering models that can help businesses solve problems and make decisions, such as predicting whether a particular software application will be successful.
Data Analyst
Data Analyst
Data Analysts use their knowledge of statistics, programming, and data visualization to analyze data and extract insights. This course provides a solid foundation in logistic regression, a statistical model that is widely used in data analysis. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret data analysis models that can predict outcomes, such as whether a particular person earns less than 50,000 US Dollars or more than 50,000 US Dollars annually.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of statistics, programming, and finance to analyze financial data and make investment decisions. This course provides a solid foundation in logistic regression, a statistical model that is widely used in financial analysis. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret financial analysis models that can help make investment decisions, such as predicting whether a particular stock will go up or down in price.
Risk Analyst
Risk Analyst
Risk Analysts use their knowledge of statistics, programming, and finance to analyze risk and make decisions. This course provides a solid foundation in logistic regression, a statistical model that is widely used in risk analysis. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret risk analysis models that can help make decisions, such as whether a particular loan applicant is a good credit risk.
Data Engineer
Data Engineer
Data Engineers use their knowledge of statistics, programming, and data management to design and build data pipelines. This course provides a solid foundation in logistic regression, a statistical model that is widely used in data engineering. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret data engineering models that can help businesses solve problems and make decisions, such as predicting whether a particular product will be successful.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of statistics, programming, and finance to analyze financial data and make investment decisions. This course provides a solid foundation in logistic regression, a statistical model that is widely used in quantitative finance. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret quantitative finance models that can help make investment decisions, such as predicting whether a particular stock will go up or down in price.
Actuary
Actuary
Actuaries use their knowledge of statistics, programming, and finance to assess and manage risk. This course provides a solid foundation in logistic regression, a statistical model that is widely used in actuarial science. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret actuarial science models that can help businesses solve problems and make decisions, such as predicting whether a particular insurance policy will be successful.
Computer Scientist
Computer Scientist
Computer Scientists use their knowledge of programming, data structures, and algorithms to design and build computer systems. This course provides a solid foundation in logistic regression, a statistical model that is widely used in computer science. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret computer science models that can help businesses solve problems and make decisions, such as predicting whether a particular software application will be successful.
Management Consultant
Management Consultant
Management Consultants use their knowledge of statistics, programming, and business to help businesses solve problems and make decisions. This course provides a solid foundation in logistic regression, a statistical model that is widely used in management consulting. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret management consulting models that can help businesses solve problems and make decisions, such as predicting whether a particular product will be successful.
Market Research Analyst
Market Research Analyst
Market Research Analysts use their knowledge of statistics, programming, and data analysis to conduct market research and make marketing decisions. This course provides a solid foundation in logistic regression, a statistical model that is widely used in market research. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret market research models that can help make marketing decisions, such as predicting whether a particular product will be successful.
Business Analyst
Business Analyst
Business Analysts use their knowledge of statistics, programming, and data analysis to analyze business data and make business decisions. This course provides a solid foundation in logistic regression, a statistical model that is widely used in business analysis. By understanding the theory and intuition behind logistic regression, you will be better equipped to build and interpret business analysis models that can help make business decisions, such as predicting whether a particular product will be successful.
For more career information including salaries, visit:
OpenCourser.com/course/3cie1x/logistic
Reading list
We've selected ten 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
Logistic Regression 101: US Household Income Classification.
Provides a comprehensive overview of logistic regression, including its theoretical foundations and practical applications. It valuable resource for students and practitioners who want to understand and use logistic regression.
Classic textbook on statistical learning. It provides a comprehensive overview of the field, including logistic regression. It valuable resource for students and practitioners who want to gain a deep understanding of the theory and practice of statistical learning.
Provides a practical guide to data science for business. It includes a chapter on logistic regression. It valuable resource for students and practitioners who want to learn how to use data science to solve business problems.
Provides a practical guide to machine learning using Python. It includes a chapter on logistic regression. It valuable resource for students and practitioners who want to learn how to apply machine learning to real-world problems.
Provides a comprehensive guide to machine learning using Python. It includes a chapter on logistic regression. It valuable resource for students and practitioners who want to learn how to apply machine learning to real-world problems.
Provides a comprehensive guide to machine learning using R. It includes a chapter on logistic regression. It valuable resource for students and practitioners who want to learn how to apply machine learning to real-world problems.
Provides a comprehensive guide to machine learning using C++. It includes a chapter on logistic regression. It valuable resource for students and practitioners who want to learn how to apply machine learning to real-world problems.
Save
Provides a comprehensive guide to machine learning using .NET. It includes a chapter on logistic regression. It valuable resource for students and practitioners who want to learn how to apply machine learning to real-world problems.
Provides a broad overview of statistical learning methods, including logistic regression. It useful reference for students and practitioners who want to gain a deeper understanding of the theory and practice of statistical learning.
Provides a gentle introduction to machine learning. It includes a chapter on logistic regression. It valuable resource for students and practitioners who want to learn the basics of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3cie1x/logistic
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Effort
2 Hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Provides hands-on learning, enabling practical application of Logistic Regression and XG-Boost models
Covers key technical skills highly relevant to data analysis and machine learning roles
Emphasizes essential data preprocessing techniques, empowering learners with foundational knowledge
Suitable for beginners seeking an introduction to Logistic Regression and XG-Boost
Offers practical insights into real-world data through the use of U.S. Census data
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