import pandas from sklearn import tree import pydotplus from sklearn.tree import DecisionTreeClassifier import matplotlib.pyplot as plt import matplotlib.image as pltimg df = pandas.read_csv("treedata\\data.csv") #Map text values to number values d = {'toPlow' : 0, 'toWater' : 1, 'toSeed' : 2, 'toFertilize' : 3, 'toCut' : 4} df['Field'] = df['Field'].map(d) d = {'Night' : 0, 'Day' : 1} df['Day Time'] = df['Day Time'].map(d) d = {'Clear Sky' : 0, 'Cloudy' : 1, 'Rainy' : 2, 'Hail': 3} df['Weather'] = df['Weather'].map(d) d = {'Freezing' : 0, 'Cold' : 1, 'Mild': 2, 'Hot': 3} df['Temperature'] = df['Temperature'].map(d) d = {'Windless' : 0, 'Strong Wind' : 1, 'Gale': 2} df['Wind'] = df['Wind'].map(d) d={'Low': 0, 'High': 1} df['Humidy'] = df['Humidy'].map(d) d = {'Wait' : 0, 'Make Action' : 1} df['Decision'] = df['Decision'].map(d) #Separate the feature columns from targert columns features = ['Field', 'Day Time', 'Weather', 'Temperature', 'Wind', 'Humidy'] X = df[features] y = df['Decision'] dtree = DecisionTreeClassifier() dtree = dtree.fit(X, y) data = tree.export_graphviz(dtree, out_file=None, feature_names=features) graph = pydotplus.graph_from_dot_data(data) graph.write_png('mydecisiontree.png') img = pltimg.imread('mydecisiontree.png') imgplot = plt.imshow(img) plt.show() #print(dtree.predict([[0, 1, 0, 0, 0, 1]]))