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University Admission Prediction Using Multiple Linear Regression

Ryan Ahmed

In this hands-on guided project, we will train regression models to find the probability of a student getting accepted into a particular university based on their profile. This project could be practically used to get the university acceptance rate for individual students using web application.

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In this hands-on guided project, we will train regression models to find the probability of a student getting accepted into a particular university based on their profile. This project could be practically used to get the university acceptance rate for individual students using web application.

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.

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What's inside

Syllabus

University Admission Prediction Using Multiple Linear Regression
In this hands-on project, we will train regression models to find the probability of a student getting accepted into a particular university based on their profile. This project could be practically used to get the university acceptance rate for individual students using web application. In this hands-on project we will go through the following tasks: (1) Understand the Problem Statement, (2) Import libraries and datasets, (3) Perform Exploratory Data Analysis, (4) Perform Data Visualization, (5) Create Training and Testing Datasets, (6) Train and Evaluate a Linear Regression Model, (7) Train and Evaluate an Artificial Neural Network Model, (8) Train and Evaluate a Random Forest Regressor and Decision Tree Model, (9) Understand the various regression KPIs, (10) Calculate and Print Regression model KPIs.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines data visualization, which is standard in industry
Taught by Ahmed, who are recognized for their work in data visualization
Involves hands-on labs and interactive materials
Builds a strong foundation for beginners
Develops professional skills in data visualization
Course explicitly requires that this course be taken in serial with others as part of a series

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Reviews summary

Highly-rated course for regression modeling

Learners say this university admissions prediction course using multiple linear regression is very insightful and well received. It is a great option for beginners and students who need to learn more about the topic. They also appreciate the instructor's explanations and guidance, saying they make the material easy to understand.
Content is clear and easy to follow
"Very Insightful Project"
"make you understand the code completely"
"la explicación justa para entender que esta haciendo"
Course is accessible for those new to the topic
"Excellent for beginners."
"It is useful for absolute beginners."
"A nice project which gives a good idea on all the topics"
Instructor is knowledgeable and helpful
"brilliant course"
"excellent instructor"
"the teacher is far better than other Coursera guided projects"
Coding text is too small in the online system
"The coding text is too small in the online coding system."

Activities

Coming soon We're preparing activities for University Admission Prediction Using Multiple Linear Regression. These are activities you can do either before, during, or after a course.

Career center

Learners who complete University Admission Prediction Using Multiple Linear Regression will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts collect, analyze, and interpret data. They use their findings to help businesses understand their customers, improve their products and services, and make better decisions. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Data Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Data Analyst.
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. These models can help businesses automate tasks, improve decision-making, and gain a competitive advantage. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Machine Learning Engineer. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Machine Learning Engineer.
Data Scientist
Data Scientists build models that can make predictions about the future. These models can help businesses make better decisions and improve their bottom line. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Data Scientist. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Data Scientist.
Statistician
Statisticians collect, analyze, and interpret data. They use their findings to help businesses and organizations make better decisions. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Statistician. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Statistician.
Financial Analyst
Financial Analysts use data to make investment recommendations. They analyze financial data to identify undervalued stocks and bonds. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Financial Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Financial Analyst.
Risk Analyst
Risk Analysts use data to identify and assess risks. They develop strategies to mitigate risks and protect organizations from financial losses. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Risk Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Risk Analyst.
Marketing Analyst
Marketing Analysts use data to understand customer behavior and develop marketing campaigns. They track the results of marketing campaigns and make recommendations for improvements. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Marketing Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Marketing Analyst.
Product Manager
Product Managers are responsible for the development and launch of new products and services. They work with engineers, designers, and marketers to ensure that products meet the needs of customers. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Product Manager. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Product Manager.
Actuary
Actuaries use data to assess risk and develop insurance policies. They analyze financial data to determine the likelihood of events such as death, disability, and property damage. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Actuary. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as an Actuary.
Business Analyst
Business Analysts use data and analysis to help businesses improve their performance. They identify problems, develop solutions, and track results. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Business Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Business Analyst.
Systems Analyst
Systems Analysts design, develop, and maintain computer systems. They work with users to understand their needs and develop systems that meet those needs. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Systems Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Systems Analyst.
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency of business operations. They develop mathematical models to optimize processes and reduce costs. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Operations Research Analyst. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as an Operations Research Analyst.
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with users to understand their needs and develop software solutions that meet those needs. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Software Engineer. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Software Engineer.
Database Administrator
Database Administrators design, develop, and maintain databases. They work with users to understand their needs and develop databases that meet those needs. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Database Administrator. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Database Administrator.
Data Visualization Specialist
Data Visualization Specialists create visual representations of data. They use charts, graphs, and other visual aids to help people understand data and make better decisions. The University Admission Prediction Using Multiple Linear Regression course can help you develop the skills you need to become a successful Data Visualization Specialist. You will learn how to collect, clean, and analyze data, and how to build regression models. This knowledge will be invaluable in your career as a Data Visualization Specialist.

Reading list

We've selected 14 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 University Admission Prediction Using Multiple Linear Regression.
Provides a comprehensive introduction to statistical learning methods, including regression, classification, and clustering. It valuable resource for students and practitioners who want to learn about the latest advances in statistical learning.
Provides a comprehensive overview of causal inference, a branch of statistics that deals with the problem of inferring causal relationships from observational data. It valuable resource for students and practitioners who want to learn about the latest advances in causal inference.
Provides a comprehensive overview of statistics, a branch of mathematics that deals with the collection, analysis, interpretation, and presentation of data. It valuable resource for students and practitioners who want to learn about the latest advances in statistics.
Provides a comprehensive overview of statistical learning, a branch of statistics that deals with the problem of learning from data. It valuable resource for students and practitioners who want to learn about the latest advances in statistical learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It valuable resource for students and practitioners who want to learn about the latest advances in machine learning.
Provides a Bayesian approach to statistical modeling, which powerful and flexible approach to modeling data. It valuable resource for students and practitioners who want to learn about the latest advances in Bayesian modeling.
Provides a comprehensive overview of time series analysis and forecasting, a branch of statistics that deals with the problem of analyzing and forecasting time series data. It valuable resource for students and practitioners who want to learn about the latest advances in time series analysis and forecasting.
Provides a comprehensive overview of deep learning, a subfield of machine learning that has been responsible for significant advances in fields such as computer vision, natural language processing, and speech recognition. It valuable resource for students and practitioners who want to learn about the latest advances in deep learning.
Provides a comprehensive overview of machine learning using Python, with a focus on the practical aspects of building and deploying machine learning models. It valuable resource for students and practitioners who want to learn about the latest advances in machine learning in Python.
Provides a comprehensive overview of econometrics, a branch of economics that uses statistical methods to analyze economic data. It valuable resource for students and practitioners who want to learn about the latest advances in econometrics.
Provides a comprehensive overview of forecasting, a branch of statistics that deals with the problem of predicting future events. It valuable resource for students and practitioners who want to learn about the latest advances in forecasting.
Provides a comprehensive overview of probability, a branch of mathematics that deals with the study of uncertainty. It valuable resource for students and practitioners who want to learn about the latest advances in probability.
Provides a practical introduction to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for students and practitioners who want to learn how to build and deploy machine learning models in Python.

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