Average Requirement for each crop with average condition

Now we will get the predicted crop according to average climate conditions by training our data.

Average Requirement For Each Crop With Average Conditions

@interact
def compare(conditions = ['N', 'P', 'K', 'temperature', 'ph', 'humidity', 'rainfall']):
    print("Average Value for", conditions,"is {0:.2f}".format(data[conditions].mean()))
    print("----------------------------------------------")
    print("Rice : {0:.2f}".format(data[(data['label'] == 'rice')][conditions].mean()))
    print("orange : {0:.2f}".format(data[(data['label'] == 'orange')][conditions].mean()))
    print("blackgram : {0:.2f}".format(data[(data['label'] == 'blackgram')][conditions].mean()))
    print("grapes : {0:.2f}".format(data[(data['label'] == 'grapes')][conditions].mean()))
    print("mango : {0:.2f}".format(data[(data['label'] == 'mango')][conditions].mean()))
    print("coffee : {0:.2f}".format(data[(data['label'] == 'coffee')][conditions].mean()))
    print("muskmelon : {0:.2f}".format(data[(data['label'] == 'muskmelon')][conditions].mean()))
    print("jute : {0:.2f}".format(data[(data['label'] == 'jute')][conditions].mean()))
    print("papaya : {0:.2f}".format(data[(data['label'] == 'papaya')][conditions].mean()))
    print("maize : {0:.2f}".format(data[(data['label'] == 'maize')][conditions].mean()))
    print("mungbean : {0:.2f}".format(data[(data['label'] == 'mungbean')][conditions].mean()))
    print("cotton : {0:.2f}".format(data[(data['label'] == 'cotton')][conditions].mean()))
    print("banana : {0:.2f}".format(data[(data['label'] == 'banana')][conditions].mean()))
    print("chickpea : {0:.2f}".format(data[(data['label'] == 'chickpea')][conditions].mean()))
    print("pigeonpeas : {0:.2f}".format(data[(data['label'] == 'pigeonpeas')][conditions].mean()))
    print("watermelon : {0:.2f}".format(data[(data['label'] == 'watermelon')][conditions].mean()))
    print("lentil : {0:.2f}".format(data[(data['label'] == 'lentil')][conditions].mean()))
    print("kidneybeans : {0:.2f}".format(data[(data['label'] == 'kidneybeans')][conditions].mean()))
    print("mothbeans : {0:.2f}".format(data[(data['label'] == 'mothbeans')][conditions].mean()))
    print("apple : {0:.2f}".format(data[(data['label'] == 'apple')][conditions].mean()))
    print("pomegranate : {0:.2f}".format(data[(data['label'] == 'pomegranate')][conditions].mean()))
    print("coconut : {0:.2f}".format(data[(data['label'] == 'coconut')][conditions].mean()))

Output:

interactive(children=(Dropdown(description='conditions', options=('N', 'P', 'K', 'temperature', 'ph', 'humidit…