The project contains the classical machine learning problem from a real dataset iris. The iris dataset contains the measurements of different flowers as features(petel size, color, width, length, texture, etc) and the target variable is the name of the flower. Based on the data and the target variable we can say that it is a clasification problem and after EDA (exploratory data analysis), we can probably say Decision tree will give optimum results.
The below diagram which is generated by the model based on the training data gives an overview of how the model has been constructed and why will the model behave the way it is behaving.
The visualization of the Decision Tree looks like this when we see it using graphviz :
