CODEWITHSUNDEEP
jupyter-notebookappendregressionprediction
user2543
user2543

Reputation: 121

How to add predicted values in a dataframe?

I extended the predictions to five values from this link. Now, I want to add the new five predicted values (New_Interest_Rate and New_Unemployment_Rate) so I can plot them together in a new figure together with the original timeseries.

import pandas as pd
from sklearn import linear_model
import statsmodels.api as sm

Stock_Market = {'Year': [2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016],
                'Month': [12, 11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,3,2,1],
                'Interest_Rate': [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75],
                'Unemployment_Rate': [5.3,5.3,5.3,5.3,5.4,5.6,5.5,5.5,5.5,5.6,5.7,5.9,6,5.9,5.8,6.1,6.2,6.1,6.1,6.1,5.9,6.2,6.2,6.1],
                'Stock_Index_Price': [1464,1394,1357,1293,1256,1254,1234,1195,1159,1167,1130,1075,1047,965,943,958,971,949,884,866,876,822,704,719]        
                }

df = pd.DataFrame(Stock_Market,columns=['Year','Month','Interest_Rate','Unemployment_Rate','Stock_Index_Price'])

X = df[['Interest_Rate','Unemployment_Rate']] # here we have 2 variables for multiple regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example.Alternatively, you may add additional variables within the brackets
Y = df['Stock_Index_Price']
 
# with sklearn
regr = linear_model.LinearRegression()
regr.fit(X, Y)

print('Intercept: \n', regr.intercept_)
print('Coefficients: \n', regr.coef_)

# prediction with sklearn
New_Interest_Rate = [2.75, 3, 4, 1, 2]
New_Unemployment_Rate = [5.3, 4, 3, 2, 1]
for i in range(len(New_Interest_Rate)):
    print (str(i+1) + ' - Predicted Stock Index Price: \n', 
           regr.predict([[New_Interest_Rate[i] ,New_Unemployment_Rate[i]]]))

# with statsmodels
X = sm.add_constant(X) # adding a constant

model = sm.OLS(Y, X).fit()
predictions = model.predict(X) 
 
print_model = model.summary()
print(print_model)

I cannot figure out how to append that because when I try, an error comes out.

Interest_Rate=Interest_Rate.append(New_Interest_Rate)

TypeError: cannot concatenate object of type "<class 'float'>"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid

My goal is to plot the extended predicted values. I use jupyter notebook. The original code comes from thislink. Thank you!

Upvotes: 1

Views: 1234

Answers (1)

Max Behling
Max Behling

Reputation: 311

Running the code you provided seems to work on my computer, but with some warning messages. The versions I'm using are python 3.9.7, pandas 1.3.3-1, sklearn-pandas 2.2.0-1, and statsmodels 0.13.0 . I just saved it to a file and ran it in a terminal with "python copypastedcode.py". I got this output:

Intercept:
 1798.4039776258544
Coefficients:
 [ 345.54008701 -250.14657137]
/usr/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(
1 - Predicted Stock Index Price:
 [1422.86238865]
/usr/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(
2 - Predicted Stock Index Price:
 [1834.43795318]
/usr/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(
3 - Predicted Stock Index Price:
 [2430.12461156]
/usr/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(
4 - Predicted Stock Index Price:
 [1643.6509219]
/usr/lib/python3.9/site-packages/sklearn/base.py:441: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names
  warnings.warn(
5 - Predicted Stock Index Price:
 [2239.33758028]
                            OLS Regression Results
==============================================================================
Dep. Variable:      Stock_Index_Price   R-squared:                       0.898
Model:                            OLS   Adj. R-squared:                  0.888
Method:                 Least Squares   F-statistic:                     92.07
Date:                Wed, 20 Oct 2021   Prob (F-statistic):           4.04e-11
Time:                        09:07:19   Log-Likelihood:                -134.61
No. Observations:                  24   AIC:                             275.2
Df Residuals:                      21   BIC:                             278.8
Df Model:                           2
Covariance Type:            nonrobust
=====================================================================================
                        coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------------
const              1798.4040    899.248      2.000      0.059     -71.685    3668.493
Interest_Rate       345.5401    111.367      3.103      0.005     113.940     577.140
Unemployment_Rate  -250.1466    117.950     -2.121      0.046    -495.437      -4.856
==============================================================================
Omnibus:                        2.691   Durbin-Watson:                   0.530
Prob(Omnibus):                  0.260   Jarque-Bera (JB):                1.551
Skew:                          -0.612   Prob(JB):                        0.461
Kurtosis:                       3.226   Cond. No.                         394.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

the "X does not have valid feature names..." warnings can be fixed by changing

regr.fit(X,Y)

to

regr.fit(X.values, Y.values)

If you want to use New_Interest_rate and New_Unemployment_Rate to create the regression, then you would need Y to have 5 more corresponding stock prices. I don't think that's what you want to do if you're trying to predict stock prices from interest and unemployment rates. Here's how you would do that though:

New_Interest_Rate = [2.75, 3, 4, 1, 2]
New_Unemployment_Rate = [5.3, 4, 3, 2, 1]
New_Stock_Prices = [1,2,3,4,5]
X_new = pd.DataFrame(data={'Interest_Rate': New_Interest_Rate,'Unemployment_Rate': New_Unemployment_Rate})
Y_new = pd.DataFrame(data={'Stock_Index_Price': New_Stock_Prices})
regr = linear_model.LinearRegression()
X = X.append(X_df)
Y = Y.append(Y_df)
regr.fit(X.values, Y.values)

And if you want to make plots, you can make a small function to get stock predictions from input arrays with something like this:

def predict_stock_price(future_interest_rate, future_unemployment_rate):
    return [regr.predict([[i ,j]])[0,0] for i,j in zip(future_interest_rate,future_unemployment_rate)]

prices = predict_stock_price(New_Interest_Rate,New_Unemployment_Rate)
print("list of predicted stock prices:",prices)

predicted_stock_market = {'Month': range(13,13+len(prices)), #just to have a time axis to plot with
                         'Interest_Rate': New_Interest_Rate,
                         'Unemployment_Rate': New_Unemployment_Rate,
                         'Stock_Index_Price': prices}
predicted_df = pd.DataFrame(predicted_stock_market)
predicted_df.plot( x="Month",y="Stock_Index_Price",kind='scatter')
plt.show()

Upvotes: 3

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