text: # Logistic Regression Predictions
  # ==============================================================================
  
  import pandas as pd
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  from sklearn import metrics
  from sklearn import linear_model
  
  # Data extraction
  # ==============================================================================
  
  datos = pd.read_csv("../data/raw/banquero.csv")
  dataframe = pd.DataFrame(datos)
  print(datos)
  
  X = (dataframe[["abierta","moroso","trabajo"]])
  y = (dataframe["resultado"])
  
  # Training
  # ==============================================================================
  
  X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25,random_state=0)
  model= LogisticRegression()
  model.fit(X_train,y_train)
  datanew = {'abierta': [0],
                    'moroso': [1],
                    'trabajo': [1]
                    }
  
  clientesnew = pd.DataFrame(datanew,columns= ['abierta', 'moroso','trabajo'])
  prediccion=model.predict(clientesnew)
  print (clientesnew)
  print (prediccion)
  
  