# Python Learn and Predict Examples

In the following example, an SVM script is used to predict the purchase of bikes based on a customer's income and number of children.

import pandas from sklearn import svm __pyramidOutput=0 def pyramid_learn(df): X = df.iloc[:,0:2] y= df.iloc[:,2] clf = svm.SVC(gamma=0.001, C=1.0) clf.fit(X, y) return clf def pyramid_eval(model, df): X = df.iloc[:,0:2] y = df.iloc[:,2] output = model.predict(X) correctCount=0 for idx,item in enumerate(output): if item == y.iloc[idx]: correctCount+=1 return str(correctCount / len(y)) def pyramid_predict(model, df): X = df.iloc[:,0:2] output = model.predict(X) return pandas.DataFrame({'Prediction':output})

#### Learn

In the learn function, X = the first 2 columns given as the input, and Y = the last column given as the output.

def pyramid_learn(df): X = df.iloc[:,0:2] y= df.iloc[:,2]

clf is the ML model that will be returned by the learn function:

clf = svm.SVC(gamma=0.001, C=1.0) clf.fit(X, y) return clf

#### Eval

The eval function takes the model returned by the learn function (model) and runs it against a testing set (df):

def pyramid_eval(model, df): X = df.iloc[:,0:2] y = df.iloc[:,2]

The output is a set of predictions:

output = model.predict(X)

The predictions are then compared with the actual data, and this comparison returns the model score return str(correctCount / len(y)):

correctCount=0 for idx,item in enumerate(output): if item == y.iloc[idx]: correctCount+=1 return str(correctCount / len(y))

#### Predict

The predict function applies the ML model to the entire data set and returns the set of predictions:

def pyramid_predict(model, df): X = df.iloc[:,0:2] output = model.predict(X)