Machine Learning algorithm for predicting number of cases in pandemic

I’m giving my first steps with AI and Machine Learning so I have the following issue. I’m trying to predict an outcome from COVID-19 number of day vs confirmed cases using scikit-learn library. I mean, my input is the number of days since the pandemic started in my country and my output is the number of confirmed cases in that corresponding date. However both using GradientBoosting and RandomForest I get the same output values for the test values…I post below the code in Python as it is very short…

import numpy as np

from sklearn import ensemble

import pandas
datos = pandas.read_csv('covid.csv',";")

entrada = np.array(datos['ORDEN']).reshape(-1,1)

salida = datos["CASOS"]    

regr = ensemble.GradientBoostingRegressor(random_state=0,n_estimators=500).fit(entrada,salida)

test = np.array([i for i in range(63,70)]).reshape(-1,1)

print(regr.predict(test))

regr = ensemble.RandomForestRegressor(random_state=0,n_estimators=500).fit(entrada,salida)

print(regr.predict(test))

My output is this:

[1782.99976513 1782.99976513 1782.99976513 1782.99976513 1782.99976513
 1782.99976513 1782.99976513]
[1773.99 1773.99 1773.99 1773.99 1773.99 1773.99 1773.99]

What am I doing wrong?? Thanks in advance.