Predict Sales Revenue with scikit-learn from Coursera

In this 2-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper.

By the end of this course, you will be able to:

- Explain the core ideas of linear regression to technical and non-technical audiences

- Build a simple linear regression model in Python with scikit-learn

- Employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas

- Evaluate a simple linear regression model using appropriate metrics

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Jupyter and Python 3.7 with all the necessary libraries pre-installed.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.

- 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.

What's inside

Syllabus

Project: Predict Sales Revenue with Simple Linear Regression

In this project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper. By the end of this project, you will be able explain the core ideas of linear regression to technical and non-technical audiences, build a simple linear regression model in Python with scikit-learn, employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas, and evaluate a simple linear regression model using appropriate metrics.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops a foundation in linear regression, which is a cornerstone in statistics

Builds a linear regression model in Python, which is a highly sought-after skill in various domains

Introduces scikit-learn, a renowned library for machine learning

Applies Exploratory Data Analysis to enhance understanding of the dataset

Emphasizes evaluation techniques for performance assessment

Leverages the Advertising dataset, a classic example to demonstrate regression analysis

Reviews summary

Easy-to-understand course for regression beginners

Learners say beginners and those new to regression can take away a lot from this course. Students especially appreciate the real-world project and hands-on experience that simulate tasks you may encounter in professional data science work.

Valuable real-world, hands-on experience in simple linear regression, perfect for learners who like to apply skills.

"After I did this guided project, I was able to build simple regression models by applying the skills I learnt."

"A very good course for anyone who wants a hands-on experience before starting a real-world project."

"This course give [sic] enough base understanding on how to work with simple linear regression."

Suitable for beginners to learn the basics of simple linear regression.

"This course is exactly what you need to get your hands dirty with machine learning for the first time!"

"This project is great for new learner[s]."

"Good for beginners who want to understand simple linear regression."

The course may be too simple and basic for those with some experience in simple regression or machine learning.

"It's not a Project. Its just a small course about liner regression."

"It was just too easy for me! It could have been some more harder and larger."

"If YOU are very beginner in ML. You can take this course. Otherwise check for another"

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 Predict Sales Revenue with scikit-learn with these activities:

Read 'An Introduction to Statistical Learning' by James et al.

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Deepen your understanding of linear regression and related statistical concepts by delving into this comprehensive textbook, which provides a thorough theoretical and practical foundation.

View An Introduction to Statistical Learning: with... on Amazon

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Read Chapter 3: Linear Models for Regression
Focus on sections covering simple linear regression and its assumptions
Work through the practice exercises and examples provided in the chapter

Explore Linear Regression with an Interactive Tutorial

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Supplement your course learning by engaging in guided tutorials that provide interactive demonstrations of linear regression concepts, making the topic more accessible and engaging.

Browse courses on Linear Regression

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Find an interactive tutorial on linear regression
Follow the tutorial, experiment with different parameters, and observe the impact on the model
Reflect on the concepts and techniques presented in the tutorial

Develop a Visual Representation of the Simple Linear Regression Model

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Enhance your understanding by creating visual representations of the simple linear regression model, such as scatter plots, line charts, or interactive dashboards.

Browse courses on Visualization

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Gather the necessary data and variables
Choose an appropriate visualization tool (e.g., Seaborn, Plotly)
Create scatter plots to depict the relationship between the variables
Plot the fitted regression line to visualize the model's predictions

Four other activities

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Show all seven activities

Practice Simple Linear Regression with Scikit-learn

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Refine your understanding of the core concepts and techniques of simple linear regression through repetitive exercises using the powerful Scikit-learn library.

Browse courses on Simple Linear Regression

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Import the necessary libraries (NumPy, Pandas, Scikit-learn)
Load and preprocess the Advertising dataset
Split the dataset into training and testing sets
Build a simple linear regression model using Scikit-learn's LinearRegression class
Fit the model to the training data

Participate in a Data Analysis Competition

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Test and refine your skills in a real-world setting by participating in a data analysis competition, which requires you to apply linear regression and other techniques to solve practical problems.

Browse courses on Data Analysis

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Find a suitable data analysis competition
Prepare and preprocess the data
Apply linear regression and other techniques to build models
Evaluate model performance and draw insights
Submit your results and reflect on the experience

Predict Sales Revenue for a New Product Launch

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Solidify your understanding by applying simple linear regression to a realistic business scenario, predicting sales revenue for a new product launch and gaining valuable experience in decision-making.

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Gather data on relevant predictors (e.g., advertising spending, market conditions)
Build a simple linear regression model to predict sales revenue
Validate the model's performance using metrics such as R-squared and MAE
Make predictions for different advertising budgets
Communicate your findings and recommendations to decision-makers

Contribute to Scikit-learn's Linear Regression Module

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Take your learning to the next level by contributing to the development of Scikit-learn's linear regression module, enhancing your understanding of its implementation and gaining hands-on experience in open-source software development.

Browse courses on scikit-learn

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Identify an area for improvement or a new feature to develop
Fork the Scikit-learn repository and create a new branch
Implement your changes and write unit tests
Submit a pull request and actively participate in its review
Learn from the feedback and iterate on your contribution

Career center

Learners who complete Predict Sales Revenue with scikit-learn will develop knowledge and skills that may be useful to these careers:

Data Analyst

As a Data Analyst, you will use your skills in data analysis and visualization to help businesses understand their data and make better decisions. This course will teach you how to build simple linear regression models to predict sales revenue, and you will also learn how to use Python, pandas, and seaborn to explore and visualize data. These skills are in high demand in the job market, and this course will give you the foundation you need to succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying machine learning models. This course will give you the foundational knowledge you need to build and evaluate simple linear regression models, which are essential for many machine learning applications. You will also learn how to use Python, pandas, and seaborn, which are popular tools for data science and machine learning.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

As a Business Analyst, you will use your skills in data analysis and problem-solving to help businesses improve their operations. This course will teach you how to build simple linear regression models to predict sales revenue, and you will also learn how to use Python, pandas, and seaborn to explore and visualize data. These skills are essential for Business Analysts, and this course will give you the foundation you need to succeed in this role.

See salaries and explore the career path for Business Analyst

Data Scientist

As a Data Scientist, you will use your expertise in machine learning, statistics, and programming to solve complex business problems. By building predictive models like the simple linear regression model you will learn in this course, you can help businesses make better decisions, improve efficiency, and increase profits. This course is an excellent foundation for a career as a Data Scientist, and it will give you the skills you need to succeed in this in-demand field.

See salaries and explore the career path for Data Scientist

Operations Research Analyst

As an Operations Research Analyst, you will use your skills in mathematics and modeling to solve complex business problems. This course will teach you how to use Python and scikit-learn to build simple linear regression models, helping you to develop the skills you need to succeed in this role. You will also learn how to evaluate your models using appropriate metrics, which is essential for ensuring that your models are accurate and reliable.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

As a Quantitative Analyst, you will use your skills in mathematics, statistics, and programming to analyze financial data and develop trading strategies. This course will teach you how to build simple linear regression models to predict stock prices, and you will also learn how to use Python, pandas, and seaborn to explore and visualize financial data. These skills are essential for Quantitative Analysts, and this course will give you the foundation you need to succeed in this role.

See salaries and explore the career path for Quantitative Analyst

Statistician

As a Statistician, you will use your skills in data analysis and modeling to solve problems in a variety of fields. This course will teach you how to build simple linear regression models to predict sales revenue, and you will also learn how to use Python, pandas, and seaborn to explore and visualize data. These skills are essential for Statisticians, and this course will give you the foundation you need to succeed in this role.

See salaries and explore the career path for Statistician

Economist

As an Economist, you will use your skills in data analysis and modeling to study economic trends and develop policies. This course will teach you how to build simple linear regression models to predict economic growth, and you will also learn how to use Python, pandas, and seaborn to explore and visualize economic data. These skills are essential for Economists, and this course will give you the foundation you need to succeed in this role.

See salaries and explore the career path for Economist

Market Researcher

As a Market Researcher, you will use your skills in data analysis and modeling to study consumer behavior and develop marketing strategies. This course will teach you how to build simple linear regression models to predict sales revenue, and you will also learn how to use Python, pandas, and seaborn to explore and visualize market research data. These skills are essential for Market Researchers, and this course will give you the foundation you need to succeed in this role.

See salaries and explore the career path for Market Researcher

Financial Analyst

As a Financial Analyst, you will use your skills in data analysis and modeling to analyze financial data and make investment recommendations. This course will teach you how to build simple linear regression models to predict stock prices, and you will also learn how to use Python, pandas, and seaborn to explore and visualize financial data. These skills are essential for Financial Analysts, and this course will give you the foundation you need to succeed in this role.

See salaries and explore the career path for Financial Analyst

Actuary

As an Actuary, you will use your skills in mathematics, statistics, and modeling to assess risk and develop insurance policies. This course will teach you how to build simple linear regression models to predict insurance claims, and you will also learn how to use Python, pandas, and seaborn to explore and visualize insurance data. These skills are essential for Actuaries, and this course will give you the foundation you need to succeed in this role.

See salaries and explore the career path for Actuary

Data Engineer

As a Data Engineer, you will use your skills in data management and engineering to build and maintain data pipelines. This course may teach you some of the skills you need to succeed in this role, such as how to use Python and pandas to manage and explore data. However, this course does not cover data engineering in depth, so you may need to take additional courses or training to fully prepare for this role.

See salaries and explore the career path for Data Engineer

Software Engineer

As a Software Engineer, you will use your skills in programming and software development to build and maintain software applications. This course may teach you some of the skills you need to succeed in this role, such as how to use Python and scikit-learn to build and evaluate machine learning models. However, this course does not cover software engineering in depth, so you may need to take additional courses or training to fully prepare for this role.

See salaries and explore the career path for Software Engineer

Web Developer

As a Web Developer, you will use your skills in web design and development to build and maintain websites. This course may teach you some of the skills you need to succeed in this role, such as how to use Python and pandas to analyze data. However, this course does not cover web development in depth, so you may need to take additional courses or training to fully prepare for this role.

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Database Administrator

As a Database Administrator, you will use your skills in database management and administration to build and maintain databases. This course may teach you some of the skills you need to succeed in this role, such as how to use Python and pandas to manage and explore data. However, this course does not cover database administration in depth, so you may need to take additional courses or training to fully prepare for this role.

See salaries and explore the career path for Database Administrator