Use machine learning models

avalanche-dog equipment store scenario:	We want to train the model just once, then load that model onto the server that runs our online store.
	Although the model is trained on a dataset we downloaded from the internet, we actually want to use it to estimate the boot size of our customers' dogs who are not in this dataset!
Steps	Create a basic model

	Save it to disk
	Load it from disk
	Use it to make predictions about a dog who was not in the training dataset

Load Data set

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import pandas
!pip install statsmodels
!wget <https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/graphing.py>
!wget <https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/doggy-boot-harness.csv>

# Load a file containing dog's boot and harness sizes
data = pandas.read_csv('doggy-boot-harness.csv')

# Print the first few rows
data.head()

Create and train model

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import statsmodels.formula.api as smf

# Fit a simple model that finds a linear relationship
# between boot size and harness size, which we can use later
# to predict a dog's boot size, given their harness size
model = smf.ols(formula = "boot_size ~ harness_size", data = data).fit()

print("Model trained!")

Save Model
save model to disk

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import joblib

model_filename = './avalanche_dog_boot_model.pkl'
joblib.dump(model, model_filename)

print("Model saved!")

Load Model

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model_loaded = joblib.load(model_filename)

print("We have loaded a model with the following parameters:")
print(model_loaded.params)

Function
To load model from disk
Predict harness size from dogs boot height

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def load_model_and_predict(harness_size):
    '''
    This function loads a pretrained model. It uses the model
    with the customer's dog's harness size to predict the size of
    boots that will fit that dog.

    harness_size: The dog harness size, in cm 
    '''

    # Load the model from file and print basic information about it
    loaded_model = joblib.load(model_filename)

    print("We've loaded a model with the following parameters:")
    print(loaded_model.params)

    # Prepare data for the model
    inputs = {"harness_size":[harness_size]} 

    # Use the model to make a prediction
    predicted_boot_size = loaded_model.predict(inputs)[0]

    return predicted_boot_size

# Practice using our model
predicted_boot_size = load_model_and_predict(45)

print("Predicted dog boot size:", predicted_boot_size)

Function
Calculates whether the customer has chosen a pair of doggy boots that are a sensible size.
This works by estimating the dog's actual boot size from their harness size.
Function returns a message for the customer that should be shown before they complete their payment
selected_harness_size: The size of the harness the customer wants to buy
selected_boot_size: The size of the doggy boots the customer wants to buy

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# The boots you have selected might 
# be TOO BIG for a dog as small as yours. 
# We recommend a doggy boots size of 38.

def check_size_of_boots(selected_harness_size, selected_boot_size):
    # Estimate the customer's dog's boot size
    estimated_boot_size = load_model_and_predict(selected_harness_size)

    # Round to the nearest whole number because we don't sell partial sizes
    estimated_boot_size = int(round(estimated_boot_size))

    # Check if the boot size selected is appropriate
    if selected_boot_size == estimated_boot_size:
        # The selected boots are probably OK
        return f"Great choice! We think these boots will fit your avalanche dog well."

    if selected_boot_size < estimated_boot_size:
        # Selected boots might be too small 
        return "The boots you have selected might be TOO SMALL for a dog as "\\
               f"big as yours. We recommend a doggy boots size of {estimated_boot_size}."

    if selected_boot_size > estimated_boot_size:
        # Selected boots might be too big 
        return "The boots you have selected might be TOO BIG for a dog as "\\
               f"small as yours. We recommend a doggy boots size of {estimated_boot_size}."
    

# Practice using our new warning system
check_size_of_boots(selected_harness_size=55, selected_boot_size=39)