ML-modellering: stappen

Machine Learning voor marketing in Python

Karolis Urbonas

Head of Analytics & Science, Amazon

Supervised learning: stappen

Splits data in train en test
Initialiseer het model
Fit het model op de trainingsdata
Voorspel op de testdata
Meet prestaties op de testdata

Supervised learning met code

Eerst laden we de libraries.

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

Supervised learning: stappen met 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: stappen

Initialiseer het model
Fit het model
Ken clusternummers toe
Verken de resultaten

Unsupervised learning met code

Eerst laden we de libraries.

from sklearn.cluster import KMeans
import pandas as pd

Unsupervised learning met 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()

Laten we modellen bouwen!

Machine Learning voor marketing in Python