How to handle alphanumeric values in machine learning - machine-learning

Question

I am trying to the find the best algorithm for my claims data. The claims data include some diagnosis code which are alphanumeric like 'EA43454' . when i run the below code to evaluate the models
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
scoring = 'accuracy'
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=None)
cv_results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
i get the error
ValueError: could not convert string to float: 'U0003'
How to handle these alphanumeric values?

Answer 1

You need to convert your strings to an indicator variable (dummy variables). Each value of the string variable has to be associated with a number so that the models can train on that data.
Scikit-learn has several preprocessors to help you with this such as OneHotEncoder. You can also use pandas.get_dummies, but using sklearn's own classes is more composable - for example, you can use them as part of a pipeline.
import pandas as pd
import numpy as np
from sklearn.preprocessing import OneHotEncoder
rng = np.random.default_rng()
animals = pd.DataFrame({"animal": rng.choice(["cat", "dog"], size=10),
"age": rng.integers(1, 20, size=10)})
animals_ohe = OneHotEncoder().fit_transform(animals.drop(columns=["age"]))