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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)
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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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
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
#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:#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
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