A Data Scientist, you are supposed to build a multivariate linear regression (MLR) model that might be used to predict the Stock Price Index (dependent variable) based on two independent variables namely Interest Rate and Unemployment Rate as they are shown in the mlr_dat.csv dataset. Find the intercept b and the coefficient Wi analytically by consulting the aforementioned problem to the textbook AIMA 3rd Edition pp. 718-721. Compare and show side by side to see how close your MLR model prediction with the actual one (ground truth). Learning Outcomes: LO 5: Apply various techniques to an agent when acting under certainty LO 6: Apply various AI algorithms to solve the problems
A Data Scientist, you are supposed to build a multivariate linear regression (MLR) model that might be used to predict the Stock Price Index (dependent variable) based on two independent variables namely Interest Rate and Unemployment Rate as they are shown in the mlr_dat.csv dataset. Find the intercept b and the coefficient Wi analytically by consulting the aforementioned problem to the textbook AIMA 3rd Edition pp. 718-721. Compare and show side by side to see how close your MLR model prediction with the actual one (ground truth). Learning Outcomes: LO 5: Apply various techniques to an agent when acting under certainty LO 6: Apply various AI algorithms to solve the problems
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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![#Fit the model using x_train and y_train
mlr
LinearRegression ()
mlr.fit (x_train, y_train)
%3D
#Print the Intercept and Coefficients of the
Model created
print ("Intercept:",mlr.intercept_)
print ("Coefficients:",mlr.coef_)
#Predict Model
y_pred = mlr.predict (x_test)
Y_pred
#Compare Prediction and Actual values
mlr_diff = pd. DataFrame ({'Actual value':
y_test, 'Predicted value': y_pred})
mlr_diff.head ()
Code Image:
inport pandas as pd
Stock Narket {
Interest Rate": [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.7
"Unemployment Rate: [5.3,5.1,5.3,5.1,5.4,5.6,5.5,5.5,5.5,5.6,5.7,s.9,6,5.9,5.8,6.1,a.2,6.1,6.1,6.1,5.9,6.2,6.2,
"Stack Index Price': (1464,1264,1257, 1290, 1256, 125sa, 1224, 1195,1150,167,11, 2805, 1017,065,911,05a,91,0,aas,i
dff pd.Datafrae(Stock Market, colurs'Interst Rate,ploynt Rate, Stock Index Price ])
dff.head()
IereRa UneieprenR snde_Prie
275
53
144
250
53
250
20
63
4.
250
54
128](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F67c7b2bb-ed45-4c8d-8524-f83b8daeeeb4%2Ff3374fb7-5142-41f4-88a3-d7feceefd4a1%2F6l6ddv_processed.jpeg&w=3840&q=75)
Transcribed Image Text:#Fit the model using x_train and y_train
mlr
LinearRegression ()
mlr.fit (x_train, y_train)
%3D
#Print the Intercept and Coefficients of the
Model created
print ("Intercept:",mlr.intercept_)
print ("Coefficients:",mlr.coef_)
#Predict Model
y_pred = mlr.predict (x_test)
Y_pred
#Compare Prediction and Actual values
mlr_diff = pd. DataFrame ({'Actual value':
y_test, 'Predicted value': y_pred})
mlr_diff.head ()
Code Image:
inport pandas as pd
Stock Narket {
Interest Rate": [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.7
"Unemployment Rate: [5.3,5.1,5.3,5.1,5.4,5.6,5.5,5.5,5.5,5.6,5.7,s.9,6,5.9,5.8,6.1,a.2,6.1,6.1,6.1,5.9,6.2,6.2,
"Stack Index Price': (1464,1264,1257, 1290, 1256, 125sa, 1224, 1195,1150,167,11, 2805, 1017,065,911,05a,91,0,aas,i
dff pd.Datafrae(Stock Market, colurs'Interst Rate,ploynt Rate, Stock Index Price ])
dff.head()
IereRa UneieprenR snde_Prie
275
53
144
250
53
250
20
63
4.
250
54
128
Transcribed Image Text:A Data Scientist, you are supposed to build a multivariate linear
regression (MLR) model that might be used to predict the Stock Price
Index (dependent variable) based on two independent variables namely
Interest Rate and Unemployment Rate as they are shown in the
mlr_dat.csv dataset. Find the intercept b and the coefficient Wi
analytically by consulting the aforementioned problem to the textbook
AIMA 3rd Edition pp. 718-721. Compare and show side by side to see
how close your MLR model prediction with the actual one (ground
truth).
Learning Outcomes:
LO 5: Apply various techniques to an agent when acting under
certainty
LO 6: Apply various AI algorithms to solve the problems
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