I have done mini projects some ML algorithms like Linear Regrassion,Logistic Regression,Random Forest Classifier,Decision Tree Classifier.
These are done during my AI intern at Gateway Solutions
- Linear regression is an algorithm used to predict, or visualize, a relationship between two different features/variables. In linear regression tasks, there are two kinds of variables being examined: the dependent variable and the independent variable. The independent variable is the variable that stands by itself, not impacted by the other variable. As the independent variable is adjusted, the levels of the dependent variable will fluctuate. The dependent variable is the variable that is being studied, and it is what the regression model solves for/attempts to predict. In linear regression tasks, every observation/instance is comprised of both the dependent variable value and the independent variable value.
- Logistic regression is a classification algorithm. It is used to predict a binary outcome based on a set of independent variables.
- A binary outcome is one where there are only two possible scenarios—either the event happens (1) or it does not happen (0). Independent variables are those variables or factors which may influence the outcome (or dependent variable).
- Random forest is an ensemble machine learning model. An ensemble machine learning model is a model which is a collection of several smaller models. The Random Forest model of machine learning is nothing but a collection of several decision trees. These trees come together to a combined decision to give the output.
- Decision Tree Classifier is a machine learning model. which creates a bunch of models and get the highest ratio as the output.