ML modeling steps

Machine Learning for Marketing in Python

Karolis Urbonas

Head of Analytics & Science, Amazon

Supervised learning steps

Split data to training and testing
Initialize the model
Fit the model on the training data
Predict values on the testing data
Measure model performance on testing data

Supervised learning with code

First, let's load the libraries.

from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

Supervised learning steps with code

# 1. Split data to training and testing
train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size=0.25)

# 2. Initialize the model
mytree = tree.DecisionTreeClassifier()

# 3. Fit the model on the training data
treemodel = mytree.fit(train_X, train_Y)

# 4. Predict values on the testing data
pred_Y = treemodel.predict(test_X)

# 5. Measure model performance on testing data
accuracy_score(test_Y, pred_Y)

Unsupervised learning steps

Initialize the model
Fit the model
Assign cluster values
Explore results

Unsupervised learning with code

First, let's load the libraries.

from sklearn.cluster import KMeans
import pandas as pd

Unsupervised learning with code

1. Initialize the model
kmeans = KMeans(n_clusters=3)

# Fit the model 
kmeans.fit(data)

# 3. Asign cluster values
data.assign(Cluster=kmeans.labels_)

# 4. Explore results
data.groupby('Cluster').mean()

Let's go build some models!

Machine Learning for Marketing in Python