Linear regression is a popular topic in machine learning. It’s a supervised learning algorithm and finds applications in many sectors. If you’re learning about this topic and want to test your skills, then you should try out a few linear regression projects. In this article, we’re discussing the same.
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We have linear regression project ideas for different skill levels and domains so that you can choose one according to your expertise and interests. Moreover, you can modify the challenge level of any project we’ve mentioned here by increasing (or decreasing) the data values you add in your data set.
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What is a Linear Regression?
Linear Regression is a supervised learning algorithm in machine learning. It models a prediction value according to independent variables and helps in finding the relationship between those variables and the forecast. Regression models depend on the relationship between the independent and dependent variables as well as the number of variables they use.
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Linear regression predicts the dependent value (y) according to the independent variable (x). The output here is the dependent value, and the input is the independent value. The hypothesis function for linear regression is the following:
Y = 1+2x
The linear regression model finds the best line, which predicts the value of y according to the provided value of x. To get the best line, it finds the most suitable values for 1 and 2. 1 is the intercept, and 2 is the coefficient of x. When we find the best values for 1 and 2, we find the best line for your linear regression as well.
Now that we’ve discussed the basic concepts of linear regression, we can move onto our linear regression project ideas.
Our Top Linear Regression Project Ideas
Idea #1: Budget a Long Drive
Suppose you want to go on a long drive (from Delhi to Lonawala). Before going on a trip this long, it’s best to prepare a budget and figure out how much you need to spend on a particular section. You can use a linear regression model here to determine the cost of gas you’ll have to get.
In this linear regression, the total amount of money you’d have to pay would be the dependent variable, which means it would be the output of our model. The distance between the destinations would be the independent variable. To keep the model simple, we can assume that the price of fuel would remain constant during the trip.
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You can choose any two destinations for this project. It’s a great project idea for beginners because it allows you to experiment and understand the concept clearly. Plus, you can use the model whenever you plan a long drive too!
Idea #2: Compare Unemployment Rates with Gains in Stock Market
If you’re an economics enthusiast, or if you want to use your knowledge of Machine Learning in this field, then this is one of the best linear regression project ideas for you. We all know how unemployment is a significant problem for our country. In this project, we’d find the relation between the unemployment rates and the gains happening in the stock market.
You can use official data from the government to get the unemployment rates and use it to find out if there’s a relationship between it and the gains in the stock market.
Idea #3: Compare Salaries of Batsmen with The Average Runs They Score per Game
Cricket is easily the most popular game in India. You can use your knowledge of machine learning in this simple yet exciting project where you’ll plot the relationship between the salaries of batsmen and the average runs they score in every game. Our cricketers are among some of the highest-earning athletes in the world. Working on this project would help you find out how much their batting averages are responsible for their earnings.
If you’re a beginner, you can start with one team and check the salaries of its batsmen. On the other hand, if you want to take it a step further, you can consider multiple teams (Australia, England, South Africa, etc.) and check the salaries of their batsmen too.
Idea #4: Compare the Dates in a Month with the Monthly Salary
This project explores the application of machine learning in human resources and management. It is among the beginner-level linear regression projects, so if you haven’t worked on such a project before, then you can start with this one. Here, you’ll take the dates present in a month and compare it with the monthly salary.
After you’ve established the relationship between the two variables, you can explore if the current wage is optimal or not. You can choose any career and find its average salary to select as the independent variable. You can make this project more challenging by discussing many other jobs apart from the original one.
Idea #5: Compare Average Global Temperatures and Levels of Pollution
Pollution and its impact on the environment is a prominent topic of discussion. The recent pandemic has also shown us how we can still save our environment. You can use your machine learning skills in this field too. This project would help you in understanding how machine learning can solve the various problems present in this domain as well.
Here, you’d take the average global temperatures in several years and compare them with the level of pollution that happened in that duration. Creating a linear regression model on this topic is easy and wouldn’t take a lot of effort. However, it’ll surely help you in trying out your machine learning skills.
Idea #6: Compare Local Temperature with the Amount of Rain
This is another exciting project idea for lovers of nature and the environment. In this project, you have to find the relationship between the local temperature and the amount of rain taking place there. After completing this project, you’d see how you can use linear regression and other machine learning techniques in Geography and related subjects.
You should keep the temperature in Celsius and the amount of rain in mm (millimetres). For starters, you can consider a few prominent cities of the country (such as New Delhi, Mumbai, Pune, Jaipur) and add more as you complete the project.
Idea #7: Compare Average age of Humans with The Amount of Their Sleep
Sleep has always fascinated our scientists. And if you’re fascinated by this topic too, then you should work on this one. In this project, you have to compare the average lifespan of people with the amount of sleep they get.
If you want to enter the field of biotechnology or neuroscience with expertise in machine learning, then this is an excellent choice for you. It’d help you explore the applications of linear regression in these sectors. There are many research papers on this topic, so you won’t have trouble finding relevant data sources.
Idea #8: Compare the Percentage of Sediments in River with its Discharge
This is another exciting project idea for enthusiasts of the environment and geography. Here, you have to compare the percentage of sediments present in water with the level of its discharge. You can start with one river and make it more challenging by adding more streams. Similarly, you can start with a small stream (or a section of a giant river), if you haven’t worked on linear regression projects before.
A river’s discharge is the volume following through its channel. It is the total volume of water flowing through a certain point, and the unit for measuring a river’s discharge in cubic meters per second. Sediments are the solid materials present in a stream that move and get deposited to a new location through the river.
Idea #9: Compare Budgets of National Film Awards-nominated Movies with the number Movies Winning These Awards
You apply linear regression in the entertainment sector too. In this project, you have to compare the budgets of the movies nominated for the National Film Awards with the number of films that won these awards. You would find out if the budget of a film affects its probability of winning an award or not. You can start with data for the last five years (2014-19). And if you want to take it a level further, then you can add data from more years and make the project more challenging.
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We’ve reached the end of our project list. We hope you found these linear regression project ideas helpful. If you have any questions regarding linear regression or these project ideas, feel free to ask us.
On the other hand, if you want to learn more about linear regression, then we recommend heading to our blog, where you’d find many valuable resources, guides, and articles on this topic. For starters, here’s our guide on linear regression in machine learning.
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What are the important steps to follow in linear regression?
Something more than fitting a linear line through a cluster of data points is involved in linear regression analysis. It has three stages: (1) examining the data for correlation and directionality, (2) predicting the model, i.e. fitting the line, and (3) assessing the model's validity and utility. To begin, use a scatter plot to assess the data and verify for directionality and correlation. Fitting the regression line is the second stage in regression analysis. The unexplained residual is minimized using mathematical least square estimation. The test of significance is the final stage in the linear regression analysis.
Why does linear regression need normal distribution?
Some users mistakenly believe that linear regression's normal distribution assumption applies to their data. They could make a histogram of their response variable to see if it departs from a normal distribution. Others believe the explanatory variable must have a regularly distributed distribution. Neither is necessary. The normality assumption applies to the residual distributions. The data is normally distributed, as well as the regression line is matched to the data so that the residual mean is zero.
What are the advantages and disadvantages of linear regression?
The most significant benefit of linear regression analysis is their linearity: It simplifies the estimating process and, more crucially, these linear equations have an easy-to-understand modular interpretation (i.e. the weights). Linear regression simply considers the Dependent Variable's mean. The link between the average of the dependent variable and the independent variables is studied using linear regression. Outliers can affect Linear Regression.