decision tree

WeatherConditions.py

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+import random
+
+def checkConditions():
+    weather = random.choice(['Sunny', 'Cloudy', 'Rainy', 'Hail'])
+    day_time = random.choice(['Day', 'Night'])
+    temperature = random.choice(['Freezing', 'Cold', 'Mild', 'Hot'])
+    wind = random.choice(['Windless', 'Strong Wind', 'Gale'])
+    humidy = random.choice(['Low', 'High'])
+
+    return weather, day_time, temperature, wind, humidy

decisiontree.py

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+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\\data3.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 = {'Sunny' : 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)
+
+#print(dtree.predict([[0, 1, 0, 0, 0, 1]]))