As someone deeply involved in the field, I can attest to the popularity of linear regression in machine learning. It’s a supervised learning algorithm which finds applications in many sectors. If you’re eager about this topic and want to test your skills, trying out a few linear regression projects can be a good idea. In this article, I’ll be discussing about the same.
Here, I’ve listed 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. Let’s get into the details now!
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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.
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.
It studies the relationship between quantitative variables. Students must know the fundamentals of statistics, irrespective of their career plans. Linear regression projects help students to widen their thinking and analytical abilities. These ideas for linear regression projects in python help students learn various aspects of linear regression that help them in their careers.
Types of Linear Regression:
Linear regression is commonly divided into two types i.e., Simple linear regression and multiple linear regression. Let’s discuss these types in detail.
1. Simple Linear Regression:
It shows the relationship between a single independent variable and an equivalent output or dependent variable. This relationship can be expressed as y = b0 +b1x+e.
Here, ‘y’ is the dependent variable or output. The b0 and b1 constants denoting the intercept and coefficient. ‘e’ is the error term. This equation can be plotted on a graph for further analysis. After understanding the overview and types of linear regression, you can understand -which part of the discussion or concept on linear correlation challenged you the most?
2. Multiple Linear Regression:
It determines the relationship between two or more independent variables (or inputs) and the equivalent dependent variable (or output). The independent variables can be either categorical or continuous. The linear regression analysis is quite helpful when working on linear regression projects in python. For example, it helps in forecasting future values and trends. It can also predict the effects of changes.
Simple Linear Regression – Model Assumptions
The following are some of the presumptions upon which the Linear Regression Model rests:-
The correlation between the feature variables and the response should be linear. A scatter plot of response and feature variables can be used to assess the linearity of the assumed connection.
All variables must be multivariate normal if you want to use the linear regression model. Any linear combination of the variables in a vector with a multivariate normal distribution follows the same distributional assumptions as the original vector.
No or little multicollinearity
It is considered that multicollinearity is negligible at best. When the features (or independent variables) are extremely correlated, we say that there is multicollinearity.
It is also expected that the data exhibit negligible or no auto-correlation. When the residual errors are not statistically independent of one another, autocorrelation arises.
In homoscedasticity, the error term (or model noise) is the same for all possible values of the independent variables. That all points on the regression line have the same residuals. The use of a scatter plot allows for verification.
Practical applications of linear regression:
1. Medical research:
Medical researchers frequently use linear regression to know the relationship between patients’ blood pressure and drug dosage. They can oversee different dosages of a certain drug to patients and supervise their blood pressure response. They can use a simple linear regression model that uses blood pressure as the response variable and dosage as the predictor variable. The equation of the regression model would be:
blood pressure = b0 + b1(dosage)
The coefficient b0 represents the anticipated blood pressure when the dosage is zero.
The coefficient b1 represents the average change in blood pressure when the dosage is raised by one unit.
Commonly, businesses use linear regression to know the relationship between their revenue and advertising expenditure. They can use a simple linear regression model that considers revenue as the response variable and advertising expenditure as the predictor variable. The corresponding linear regression projects in python use this equation of the regression model:
revenue = b0 + b1(ad expenditure)
The coefficient b0 represents the overall estimated revenue when ad expenditure is zero.
The coefficient represents the average change in the total revenue when the ad expenditure is raised by one unit.
- The negative value of b1 indicates that more ad expenditure is resultant due to less revenue.
- The positive value of b1indicates that ad expenditure increases with increased revenue.
- If b1is around zero, it means that the ad expenditure doesn’t significantly influence the revenue.
Agricultural scientists widely use linear regression to determine the impact of water and fertilizer on crops. For example, they can use various amounts of water and fertilizer on various fields and observe how the crops are affected. They can use a multiple linear regression model that considers crops as the response variable, and water and fertilizer as the predictor variables. The regression model equation would be:
crop yield = b0 + b1(quantity of fertilizer) + b2(quantity of water)
The coefficient b0 represents the expected crop yield with no water or fertilizer.
The coefficient b1 represents the average change in crop yield when the quantity of fertilizer is increased by one unit. It is assumed that the water’s quantity stays the same.
The coefficient b2 represents the average change in crop yield due to an increase in water quantity by one unit. It is assumed that the fertilizer’s quantity stays the same.
Agricultural scientists can modify the amount of water and fertilized based on the values of b1 and b2. The purpose is to maximize crop production.
It is one of those linear regression projects with datasets that can benefit a huge number of people.
Data scientists can be beneficial to professional sports teams. They use linear regression to know how various training regimens influence players’ performance. For example, data scientists can analyze how different amounts of workout sessions and yoga sessions can influence player scores. They can use a multiple linear regression model. This model considers the total points scored (player’s score) as the response variable, and the workout sessions and yoga sessions as the predictor variables.
The regression model equation would be:
Total points scored = b0 + b1(yoga sessions) + b2(workout sessions)
The coefficient b0 represents the estimated points scored by a player who doesn’t participate in workout sessions and yoga sessions.
The coefficient b1 shows the average change in the score when the yoga sessions’ frequency is increased by one unit. It is assumed that the workout sessions’ frequency stays the same.
The coefficient b2 shows the average change in score scored when workout sessions’ frequency is increased by one unit. It is assumed that the yoga sessions’ frequency stays the same.
The data scientists can use these measured values of b1 and b2 in their linear regression projects with datasets. They can recommend to a player how to participate in yoga and workout sessions to maximize their score.
The answer to this question – which part of the discussion or concept on linear correlation challenged you the most? can be the preparation of data. So, the following section explains how to prepare data for your linear regression model.
How to prepare data for linear regression?
You can implement the following steps when working on your linear regression projects with datasets.
1) Discard outliers:
The regression model assumes a linear relationship between variables. Hence, it is significant to discard outliers that can impact the results.
2) Discard collinearity:
Collinearity denotes the correlation between independent variables. It can create data overfitting that can provide inconsistent results.
3) Normalize the data:
Linear regressions make more precise predictions if the data adopts a normal distribution curve.
4) Standardize the data:
It is accomplished by subtracting a measure of location (for example, mean) and dividing its standard deviation. This step is quite important when two data sets feature different ranges.
5) Input extra data:
You can provide space for additional imputations if some data points have missing values. This step is not mandatory if you are dealing with big data sets.
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.
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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.
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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.
Idea 10# Linear Regression Project Idea for Stock Price Prediction
This linear regression project on stock price prediction using linear regression is concerned with developing a reliable device to help investors make sound decisions. Predictive model creation is an objective of the project based on analysing historical stock data, including appropriate economic indicators, and applying market trends.
The goal is to look for trends and connections in the data that can make stock price prediction possible.It involves the selection and rigorous refinement of such features as trading volumes, market indices, and other vital factors that impact stock performance. Via regression analysis, we’ll see the impact of those variables on stock prices, creating a model that can respond to continuously changing market conditions. The tool’s output will predict and offer analytics into the factors influencing price fluctuations.
Investors can gain from this predictive tool because it assists them in predicting possible market shifts, making better decisions about their investments, and navigating the intricacies of the stock market more confidently. Specifically, the project aims to bring more strategic and data-oriented trading of stocks, which implies better stock trading decisions for investors.
Idea 11# Linear Regression Project Idea for Movie Rating
Next, we have a movie rating as a linear regression project in Python. This project uses multivariate regression to predict movie ratings, showing the diverse elements influencing audience preferences. A model will be developed that considers a range of parameters (genre, cast, director, release date, and promotional strategies) that relate to the movie ratings.
In the process, vast amounts of movie-centred data are gathered and organized, essential patterns are extracted, and multivariate regression analysis is done to give quantitative measures of the variables that affect audience ratings. The emerging model seeks not only to exceed simple correlations but also to bring in a complex understanding of the often-intertwined factors accounting for people’s views.
Film producers and studios can benefit from our project in creative and marketing judgment calls of movie production. The tool seeks to serve as an invaluable resource for forecasting audience responses, advising filmmakers on creating content that resonates with their intended audience, and maximizing promotional efforts. The project of the multivariate regression of movie rating is intended to endow the film industry with actionable knowledge, which, in turn, guarantees engaging, appealing movies.
Idea 12# Linear Regression Project to Build a Song Popularity Predictor
Our project aims to build up a Song Popularity Predictor using statistics to estimate how popular a song might be. By diving into several musical elements like the genre, artist popularity, tempo, and lyrical content, the aim is to develop an all-encompassing model that captures the factors affecting a song’s popularity.
The process gathers diverse songs and examines the relations between various features utilizing regression analysis. With such an investigation, we aim to detect the degree of influence of each variable on a song’s popularity, building a tool that goes deeper than shallow measurements.
Artists, producers, and musical platforms can learn from this project on the changing terrain of musical tastes. The Song Popularity Predictor intends to aid decision-making procedures, guiding the development and publicity of music that matches consumer preferences as they change.
This project aims to give music industry stakeholders a tool to successfully create and promote their music by providing a multidimensional analysis of the elements contributing to a song’s success. You have had an excellent overview of how you can select your ideas by going through the linear regression project examples discussed above.
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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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Are linear and logistic regression the two most common kinds of regression?
Logistic regression goes one step further by fitting the line values to the sigmoid curve, while linear regression aims to determine the best-fitting line. Linear regression uses the mean squared error as its loss function, while logistic regression uses maximum likelihood estimation.
How many distinct kinds of regression analyses are there?
Linear and logistic regression are the two most used types of regression analysis. The nature of the data will ultimately dictate the sort of regression analysis model we choose.
Does just linear data work in logistic regression?
Both linear and non-linear forms of a logistic model exist. A linear model is one in which the predictor function is linear. A non-linear model is one that employs a prediction function that does not follow a straight line. A link function connects the prediction function to the anticipated value ().
Which regression model is best?
In the realm of regression analysis, linear regression—also known as ordinary least squares (OLS) and linear least squares—is the actual workhorse. Learn how a shift of one unit in each independent variable contributes to a shift of one unit in the dependent variable with the help of linear regression.
What are the applications of linear regression?
Business projections and choices might benefit from linear regression, a statistical technique that establishes the link between variables. It may be used in economics, corporate strategy, marketing, medical, and more.
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.
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