Task — We have given sample Iris dataset of flowers with 3 category to train our Algorithm/classifier and the Purpose is if we feed any new data to this classifier, it would be able to predict the right class accordingly.
Iris_data contain total 6 features in which 4 features (SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalwidthCm) are independent features and 1 feature(Species) is dependent or target variable. And Id column is like serial number for each data points. All Independent features has not-null float values and target variable has class labels(Iris-setosa, Iris-versicolor, Iris-virginica).
As we saw that each classes (Species) has equal number of data points, So our Iris data said to be Balanced dataset. No Class is fully dominating in our dataset.
For Visualizing the dataset we used Matplotlib or seaborn as a python library. Their are many plots like scatter, hist, bar, count etc. to visualized the data for better understanding…
Here I just try to find some new feature with the help of existing features. Taking difference of each feature with each other to get some more information and visualized it by using plots.
Building Classification Decision Tree Model and Visualizing Decision Tree using graphviz library.