consola.py

# Librerías estándar de análisis de datos
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import r2_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import confusion_matrix
import pickle

import warnings
warnings.filterwarnings("ignore")

from input import teclado

df = teclado()


df["hypertension"] = df["hypertension"].astype(bool)
df["heart_disease"] = df["heart_disease"].astype(bool)
df["stroke"] = df["stroke"].astype(bool)
df["stroke"].value_counts()


df.isnull().sum(axis = 0)


categoricas = ["gender", "ever_married", "work_type", "Residence_type", "smoking_status", "hypertension", "heart_disease", "stroke"]
numericas = ["age", "avg_glucose_level", "bmi"]


## se elimina variable objetivo, por que es la que queremos predecir
X = df.drop("stroke", axis=1)
y = df["stroke"]


categoricas = ["gender", "ever_married", "work_type", "Residence_type", "smoking_status", "hypertension", "heart_disease"]

    
carga_transformer = pickle.load(open('transformer_entrenado.pkl', 'rb'))    
carga_modelo = pickle.load(open('modelo_entrenado.pkl', 'rb'))

transformer = carga_transformer
model = carga_modelo
df = transformer.transform(df)

print(f"Matriz: \n {df}")

predict=model.predict(df)
print(f"Usted puede sufrir un Ictus: {predict}")