Des _tree

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import matplotlib.pyplot as plt

import seaborn as sns

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelEncoder

from sklearn.metrics import classification_report, confusion_matrix, precision_recall_curve, auc, roc_curve

from sklearn.tree import DecisionTreeClassifier, export_graphviz

import graphviz

data = pd.read_csv("/home/umesh/Desktop/icfoss/bcdata.csv")

data.drop(['Unnamed: 32'], axis=1, inplace=True)

data.drop(['id'], axis=1, inplace=True)

data.diagnosis.value_counts()

data.diagnosis = [1 if each == "M" else 0 for each in data.diagnosis]

y = data.diagnosis.values

x = data.drop(['diagnosis'], axis=1)

x = (x - np.min(x) ) / ( np.max(x) - np.min(x) ).values

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.3 , random_state = 1)

from sklearn.tree import DecisionTreeClassifier, export_graphviz

import graphviz

clf = DecisionTreeClassifier()

clf = clf.fit(x_train, y_train)

dot_data = export_graphviz(clf, out_file=None,

feature_names=x.columns,

filled=True, rounded=True,

special_characters=True)

graph = graphviz.Source(dot_data)

graph.view()