Multiple Regression Python Code + Results

import pandas as pd
from sklearn import linear_model
import statsmodels.api as sm
df = pd.read_csv(r'C:\Users\Isaiah Lee\Desktop\covid19-in-usa\us_covid19_dailya.csv')
X = df[['Week','death']]
Y = df['Approval']
# with sklearn
regr = linear_model.LinearRegression()
regr.fit(X, Y)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)
print('Intercept: \n', regr.intercept_)
Intercept:
45.18728627249923
print('Coefficients: \n', regr.coef_)
# prediction with sklearn
New_Week = 9
New_Death = 90000
print('Predicted Approval Rating: \n', regr.predict([[New_Week, New_Death]]))
#with statsmodels
X = sm.add_constant(X)
model = sm.OLS(Y, X).fit()
predictions = model.predict(X)
print_model = model.summary()
print(print_model)

//results
  OLS Regression Results
  
  //Coefficients:
// [-1.75624063e+00  9.96593971e-05]
  //Predicted Approval Rating:
// [38.35046633]
==============================================================================
Dep. Variable:               Approval   R-squared:                       0.089
Model:                            OLS   Adj. R-squared:                  0.053
Method:                 Least Squares   F-statistic:                     2.449
Date:                Thu, 14 May 2020   Prob (F-statistic):             0.0966
Time:                        21:51:29   Log-Likelihood:                -155.71
No. Observations:                  53   AIC:                             317.4
Df Residuals:                      50   BIC:                             323.3
Df Model:                           2
Covariance Type:            nonrobust
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         45.1873      2.763     16.352      0.000      39.637      50.738
Week          -1.7562      1.661     -1.058      0.295      -5.092       1.579
death       9.966e-05      0.000      0.696      0.490      -0.000       0.000
==============================================================================
Omnibus:                        1.703   Durbin-Watson:                   1.917
Prob(Omnibus):                  0.427   Jarque-Bera (JB):                1.285
Skew:                          -0.381   Prob(JB):                        0.526
Kurtosis:                       3.017   Cond. No.                     2.03e+05
==============================================================================