The problem statement is from the given iris Dataset,we have to create the Decision Tree Classifier and Visualize it graphically.
Starting from importing the libraries followed by Data Exploration and Data Visualization. The very next step is to do a little of feature engineering. The target column is of Object type(categorical values), so we converted them to integer type using apply function on the column "Species".
After feature engineering , we started building our Decision-tree Classifier on our Train-dataset, keeping all parameters as dafualt, we fit our model.
After fitting, We did model testing on the unseen data followed by comparion predicted values with the actual values.
The last step: we did some evaluation which results in extreme good accuracy followed by visualization of a decision tree. Our model was trained efficiently and was able to predict the right values when feed with further unseen Data.
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