Aprende Machine Learning Con Scikitlearn Keras Y Tensorflow -

x_train = x_train.astype("float32") / 255.0 x_test = x_test.astype("float32") / 255.0

Elena lived in a chaotic, charmingly old building in the heart of Madrid. The elevator, a relic from the 1970s, had a personality disorder. Some days it opened its doors with a cheerful ding. Other days, it would skip the fourth floor entirely, plummet straight from the fifth to the third, and then get stuck, humming a low, mournful tune.

One Tuesday, after being trapped for twenty minutes with a neighbor’s complaining parrot, Elena snapped. “I’m an engineer,” she muttered. “I build bridges. I can outsmart a grumpy elevator.” aprende machine learning con scikitlearn keras y tensorflow

She downloaded the maintenance logs from the past five years: dates, times, outside temperature, number of passengers, weird sounds logged by the superintendent (creak, groan, ding-malfunction, silence). It was a mess of spreadsheets, handwritten notes, and sensor data.

She needed help. That night, she opened her laptop and typed a sentence she didn’t fully understand: “aprende machine learning con scikitlearn keras y tensorflow.” x_train = x_train

The first result was a tutorial titled: “From Zero to Hero.”

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
print(classification_report(y_test, model.predict(X_test)))
 Other days, it would skip the fourth floor