Top 10 Regression Machine Learning Projects
Regression can be defined as a method or an algorithm in Machine Learning that models a target value based on independent predictors. It is essentially a statistical tool used in finding out the relationship between a dependent variable and independent variable.
Linear Regression Algorithm
Linear Regression is the first machine learning algorithm based on ‘Supervised Learning’. Linear regression performs the task to predict a dependent variable value (y) based on a given independent variable (x).
When there is a single input variable (x), the method is referred to as ‘Simple Linear Regression’. When there are multiple input variables, the method is referred to as ‘Multiple Linear Regression’.
Simple regression equation: Y = A + B * X
X → Input variable (training data)
B → Coefficient of X
A → Intercept (Constant)
Y → Predicted Value (Calculated from A, B and X)
Logistic Regression Algorithm
Logistic regression is a statistical technique used to predict probability of binary response based on one or more independent variables. It means that, given a certain factor, logistic regression is used to predict an outcome which has two values such as 0 or 1, pass or fail, yes or no etc.
Here we are going to see some regression machine learning projects.
So, let’s get started
1) Red Wine Quality
For this project, you can use Kaggle’s Red Wine Quality dataset to build various classification models to predict whether a particular red wine is “good quality” or not. Each wine in this dataset is given a “quality” score between 0 and 10. For the purpose of this project, you converted the output to a binary output where each wine is either “good quality” (a score of 7 or higher) or not (a score below 7). The quality of a wine is determined by 11 input variables.
- Fixed acidity
- Volatile acidity
- Citric sugar
- Residual sugar
- Free sulphur dioxide
- Total sulphur dioxide
The objectives of this project are as follows
- To experiment with different classification methods to see which yields the highest accuracy
- To determine which features are the most indicative of a good quality wine
2) Boston House Prices Prediction
In this project, we will develop and evaluate the performance and the predictive power of a model trained and tested on data collected from houses in Boston’s suburbs.
Once you get a good fit, you can use this model to predict the monetary value of a house located at the Boston’s area.
A model like this would be very valuable for a real estate agent who could make use of the information provided in a daily basis.
This project requires Python and the following python libraries installed.
3) Health Insurance Cost Prediction
Health Insurance companies have a tough task at determining premiums for their customers. While the health care law in the United States does have some rules for the companies to follow to determine premiums, it’s really up to the companies on what factor/s they want to hold more weightage to.
So, what are the most important factors? And how much statistical importance do they hold?
Using Multiple Linear Regression. You can try to determine the most (statistically) significant factors (independent variables) that influence the premiums charged (dependent variable) by an insurance company. You can download the dataset from Kaggle.
4) Iris Dataset
This is probably the most versatile, easy and resourceful dataset in pattern recognition literature. Nothing could be simpler than the Iris dataset to learn classification techniques. If you are totally new to data science, this is your start line. The data has only 150 rows & 4 columns
We have to predict the class of the flower based on available attributes. For that you have to build a Logistic Regression model. So that you can predict the class of a flower.
5) BigMart Sales Dataset
Retail is another industry which extensively uses analytics to optimize business processes. Tasks like product placement, inventory management, customized offers, product bundling, etc. are being smartly handled using data science techniques. As the name suggests, this data comprises of transaction records of a sales store.
We have to predict the sales of a store. This is a Linear Regression problem.
6) Height and Weight Dataset
This is a fairly straight forward problem and is ideal for people starting off with data science. It is a regression problem.
We have to predict the height and weight of a person. You can download the dataset for this problem from Kaggle.
7) Black Friday Dataset
This dataset comprises of sales transactions captured at a retail store. It’s a classic dataset to explore and expand your feature engineering skills and day to day understanding from multiple shopping experiences. This is a regression problem.
We have to predict the purchase amount.
8) Loan Prediction Dataset
Among all industries, the insurance domain has one of the largest uses of analytics & data science methods. This dataset provides you a taste of working on data sets from insurance companies – what challenges are faced there, what strategies are used, which variables influence the outcome, etc. This is a classification problem.
We have to predict if a loan will get approval or not. You have to build a Logistic Regression model to know the if a loan will get approval or not.
9) Million Song Dataset
Did you know data science can be used in the entertainment industry also? Do it yourself now. This data set puts forward a regression task.
The objective of the problem is to predict the release year of the song. You can download the dataset from Kaggle.
10) Predict Employee Salary
This is another most easy and simple problem in data science. This problem requires regression technique (i.e. Linear Regression method).
The objective of the problem is to predict the salary of employees. You can download the dataset from Kaggle.
So, here are some Regression machine learning projects you can work on with.