regression problem: According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally, responsible for approximately 11% of total deaths. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, various diseases, and smoking status. Each row in the data provides relavant information about the patient it is modelled using linear regression. classification problem: This dataset includes top football leagues scorers their goals ,Country, Club, matches played ,substitution, min ,Goals. here different columns are classified using logistic regression.
The dataset used for regression is the Stroke Prediction Dataset(https://www.kaggle.com/fedesoriano/stroke-prediction-dataset/download) from kaggle. he dataset used for classification is the Top Football Leagues Scorers Dataset(https://www.kaggle.com/mohamedhanyyy/top-football-leagues-scorers/download) from kaggle
Linear regression: Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc.
Linear regression algorithm shows a linear relationship between a dependent (y) and one or more independent (y) variables, hence called as linear regression. Since linear regression shows the linear relationship, which means it finds how the value of the dependent variable is changing according to the value of the independent variable.
The linear regression model provides a sloped straight line representing the relationship between the variables.
logistic regression: Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. The nature of target or dependent variable is dichotomous, which means there would be only two possible classes.
In simple words, the dependent variable is binary in nature having data coded as either 1 (stands for success/yes) or 0 (stands for failure/no).
Mathematically, a logistic regression model predicts P(Y=1) as a function of X. It is one of the simplest ML algorithms that can be used for various classification problems such as spam detection, Diabetes prediction, cancer detection etc.
The stroke prediction dataset can also be used in making mulitple regression model for predicting stroke based on many parameters.