Linear Regression projects for beginners will help every fresher to polish their skills. To help you get started, we’ve listed the best projects on linear regression.

The use of linear regression is widely used in ML, AI, and Data Science. Without linear regression, one cannot learn or work on Machine Learning, Data Science, and AI. All these fields need linear regression to solve complex problems of IT.

Anyone who has recently completed the course on linear regression or wants to work in the field of ML, AI, and Data Science can take these projects on linear regression. This will provide more hands-on knowledge that will help you in the real world.

**Why is it Important? **

These linear regression projects for beginners are designed to improve the hands-on experience to be equipped with job-ready skills.

To master your linear regression skills, we highly recommend that you enroll in multiple guided projects. Here you can find the list of all linear regression projects in the Coursera platform. The cost of each guided project is $10.

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## Best Projects on Linear Regression for Beginners

Linear regression is a statistical method to find the relationship between two variables. It can be used in many fields of study, such as economics, medicine, and engineering. The goal of linear regression is to use the least amount of data possible to predict an outcome with high accuracy.

The linear regression equation is formed by performing a series of simple calculations that are used to calculate the best estimate for a data point.

The equation can be written as: “y = mx + b” where “y” is the corresponding dependent variable, “m” is the slope, and “b” is the y-intercept.

### 1. Linear Regression with Python

This is a beginner’s linear regression project in Python. You will use Numpy and Python to learn how you can implement Linear Regression. Since linear regression is a basic concept in Deep Learning and Machine Learning, one should thoroughly understand the concept. This project is perfect for teaching beginners about linear regression.

The students who take this project on linear regression should understand gradient descent and linear regression because you will practically create a linear model and implement gradient descent. Then using gradient descent, you will train this linear model.

### 2. Linear Regression with NumPy and Python

Another great linear regression project in Python. Here the students will use the gradient descent algorithm from scratch. Then using Python and Numpy students perform univariate linear regression.

Both Data Science and Machine Learning practitioners will benefit from these projects on Linear Regression because it uses popular Machine Learning libraries such as statsmodel and scikit-learn. Finally, you will build plots and data visualizations using matplotlib.

### 3. Predicting Salaries with Simple Linear Regression in R

This linear regression project for beginners is to solve regression problems in the R language. To achieve this, students will learn by creating a simple linear regression algorithm. Using this algorithm, you will create, train, test, and visualize using the R language.

In this project on linear regression, there is no need to download any software to your PC. All you need is a web browser and follow the tutorial. The length of the linear regression project is 2 hours. So far, more than 4k students have enrolled in the projects on linear regression.

### 4. Multiple Linear Regression with scikit-learn

This is one of the essential projects on linear regression to learn multiple linear regression. Using Python programming, you will create and test multiple linear regression models. Then, using sciket-learn and pandas, users can calculate the regression and manage data.

With seaborn, students will carry out Exploratory Data Analysis (EDA) and data visualization. Also, using scikit-learn, you learn to build multivariate and univariate linear regression. By the end of this linear regression project, users have learned many practical skill sets that will be useful in the real world.

### 5. Predict Sales Revenue with scikit-learn

Learn to build a simple linear regression model using Python in this guided project for beginners. Then you will proceed to apply scikit-learn and statsmodels to regression problems. To do this, you will use the Advertising data set to predict sales.

This 2 hours long project on linear regression can be taken by anyone, even by a non-technical student. This hands-on project will build a solid foundation deep into your mind as you practice by doing. More than 2k students have taken this linear regression project in Python.

### 6. Regression Analysis with Yellowbrick

Here you will use linear regression and build a machine learning model. It will make use of a diagnostic platform known as Yellowbrick. Using this Yellowbrick you will learn to check the performance of regression models.

The goal of the project is to steer your machine learning workflow using the visualization method. The topics covered in this project are EDA, regression modeling, model evaluation, cross-validation, and hyperparameter tuning.

### 7. Regression with Automatic Differentiation in TensorFlow

In this beginner’s project on linear regression, you’ll learn to solve a linear regression problem. This is achieved by using automatic differentiation in TensorFlow. The project begins with a split-screen video where you should follow the instructor’s guide to complete a project.

You’ll learn some popular topics on generating data for linear regression, watching Tensors, and persistent tape. If you’ve decent knowledge of Python, Linear Regression, and Gradient Descent, this project will be helpful.

### 8. Predict Housing Prices in R on Boston Housing Data

This is an hour-long guided project on linear regression in Coursera. Here you will use the R programming language to perform a linear regression project. You’ll begin by creating Testing and Training Sets via R.

Then to a given data set, you’ll apply Random Forest, GBM, and Linear Models. You will end the project by choosing the most accurate models. Accurate models are chosen based on the results using RMSE and a Confusion Matrix.

### 9. Mining Quality Prediction Using Machine & Deep Learning

This linear regression project will train you to build Artificial Neural Network models to perform regression tasks. This project will help if you want to take Deep Learning certification in the future. This project is 90 minutes long.

Here you will be trained to understand the difference between various regression models KPIs such as MAE, RMSE, MSE, R2, and adjusted R2. Then evaluate the performance of the regression model.

### 10. University Admission Prediction Using Multiple Linear Regression

In this Coursera-guided project on linear regression, you will learn to train regression models. Using this regression model, one can make the probability of a student getting approved into University.

Students are provided with step-by-step instructions to follow along with the project. This will provide deep insight into the student’s mind to learn the concepts by doing. Over 3k students have taken this linear regression project.

## Summary: Is Linear Regression for Beginners worth it?

These projects on linear regression are helpful for beginners to get more hands-on practice. The more hands-on labs you do, the more familiar you will be with the concepts to work on real-world problems.

We’ve similar Coursera-guided projects on Machine Learning and Data Science projects for freshers.

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