# Need Help With The Following Assignment Have Attached The Required Data And Mate 4

Need help with the following assignment have attached the required data and materials of the professor’s lecture. It needs to be performed in RStudio. All of the detailed instructions and necessary documents are attached. All of the 10 questions answers need to be accurate.

Instructions and Assignment:

Advanced Analytics in R

Attached Files:

IT836 Advanced R Assignment.pdf (98.987 KB) nbtrain.csv (306.612 KB)

In this assignment you will train a Naïve Bayes classifier on categorical data and predict individuals’ incomes.  Import the nbtrain.csv file.  Use the first 9010 records as training data and the remaining 1000 records as testing data.

In this assignment you will train a Naïve Bayes classifier on categorical data and predict individuals’ incomes. Import the nbtrain.csv file. Use the first 9010 records as training data and the remaining 1000 records as testing data. 1. Read the nbtrain.csv file into the R environment. 2. Construct the Naïve Bayes classifier from the training data, according to the formula “income ~ age + sex + educ”. To do this, use the “naiveBayes” function from the “e1071” package. Provide the model’s a priori and conditional probabilities. 3. Score the model with the testing data and create the model’s confusion matrix. Also, calculate the overall, 10-50K, 50-80K, and GT 80K misclassification rates. Explain the variation in the model’s predictive power across income classes. 4. Use the first 9010 records as training data and the remaining 1000 records as testing data. 5. What is propose of separating the data into a training set and testing set? 6. Construct the classifier according to the formula “sex ~ age + educ + income”, and calculate the overall, female, and male misclassification rates. Explain the misclassification rates? 7. Divide the training data into two partitions, according to sex, and randomly select 3500 records from each partition. Reconstruct the model from part (a) from these 7000 records. Provide the model’s a priori and conditional probabilities. 8. How well does the model classify the testing data? Explain why. 9. Repeat step (b) 4 several times. What effect does the random selection of records have on the model’s performance? 10. What conclusions can one draw from this exercise?

#section5.5.1TheGroceriesDataset#section5.5.1TheGroceriesDataset

data(Groceries)Groceriessummary(Groceries)class(Groceries)

# display the first 20 grocery labelsGroceries@itemInfo[1:20,]

# display the 10th to 20th transactionsapply(Groceries@data[,10:20], 2,       function(r) paste(Groceries@itemInfo[r,”labels”], collapse=”, “))

#section5.5.2FrequentItemsetGe≠ration#section5.5.2FrequentItemsetGe≠ration

# frequent 1-itemsetsitemsets <- apriori(Groceries, parameter=list(minlen=1, maxlen=1, support=0.02, target=”frequent itemsets”))summary(itemsets)inspect(head(sort(itemsets, by = “support”), 10))

# frequent 2-itemsetsitemsets <- apriori(Groceries, parameter=list(minlen=2, maxlen=2, support=0.02, target=”frequent itemsets”))summary(itemsets)inspect(head(sort(itemsets, by =”support”),10))

# frequent 3-itemsetsitemsets <- apriori(Groceries, parameter=list(minlen=3, maxlen=3, support=0.02, target=”frequent itemsets”))inspect(sort(itemsets, by =”support”))

# frequent 4-itemsetsitemsets <- apriori(Groceries, parameter=list(minlen=4, maxlen=4, support=0.02, target=”frequent itemsets”))inspect(sort(itemsets, by =”support”))

# run Apriori without setting the maxlen parameteritemsets <- apriori(Groceries, parameter=list(minlen=1, support=0.02,                                              target=”frequent itemsets”))

#section5.5.3Re–Ge≠rationandVisualization#section5.5.3Re̲Ge≠rationandVisualization

rules <- apriori(Groceries, parameter=list(support=0.001,                                           confidence=0.6, target = “rules”))summary(rules)

plot(rules)plot(rules@quality)

# displays rules with top lift scoresinspect(head(sort(rules, by=”lift”), 10))

confidentRules <- rules[quality(rules)\$confidence > 0.9]confidentRules

plot(confidentRules, method=”matrix”, measure=c(“lift”, “confidence”),     control=list(reorder=TRUE))

# select the 5 rules with the highest lifthighLiftRules <- head(sort(rules, by=”lift”), 5)

plot(highLiftRules, method=”graph”, control=list(type=”items”))

This code covers the code presented in # Section 8.2 ARIMA Model###

section 8.2.5 Building and Evaluating an ARIMA Model###

install.packages(“forecast”)       # install, if necessarylibrary(forecast)

# read in gasoline production time series# monthly gas production expressed in millions of barrelsgas_prod_input <- as.data.frame( read.csv(“c:/data/gas_prod.csv”) )

# create a time series objectgas_prod <- ts(gas_prod_input[,2])

#examine the time seriesplot(gas_prod, xlab = “Time (months)”,     ylab = “Gasoline production (millions of barrels)”)

# check for conditions of a stationary time seriesplot(diff(gas_prod))abline(a=0, b=0)

# examine ACF and PACF of differenced seriesacf(diff(gas_prod), xaxp = c(0, 48, 4), lag.max=48, main=””)pacf(diff(gas_prod), xaxp = c(0, 48, 4), lag.max=48, main=””)

# fit a (0,1,0)x(1,0,0)12 ARIMA modelarima_1 <- arima (gas_prod,                  order=c(0,1,0),                  seasonal = list(order=c(1,0,0),period=12))arima_1

# it may be necessary to calculate AICc and BIC # http://stats.stackexchange.com/questions/76761/extract-bic-and-aicc-from-arima-objectAIC(arima_1,k = log(length(gas_prod)))   #BIC

# examine ACF and PACF of the (0,1,0)x(1,0,0)12 residualsacf(arima_1\$residuals, xaxp = c(0, 48, 4), lag.max=48, main=””)pacf(arima_1\$residuals, xaxp = c(0, 48, 4), lag.max=48, main=””)

# fit a (0,1,1)x(1,0,0)12 ARIMA modelarima_2 <- arima (gas_prod,                  order=c(0,1,1),                  seasonal = list(order=c(1,0,0),period=12))arima_2

# it may be necessary to calculate AICc and BIC # http://stats.stackexchange.com/questions/76761/extract-bic-and-aicc-from-arima-objectAIC(arima_2,k = log(length(gas_prod)))   #BIC

# examine ACF and PACF of the (0,1,1)x(1,0,0)12 residualsacf(arima_2\$residuals, xaxp = c(0, 48, 4), lag.max=48, main=””)pacf(arima_2\$residuals, xaxp = c(0, 48,4), lag.max=48, main=””)

# Normality and Constant Variance

plot(arima_2\$residuals, ylab = “Residuals”)abline(a=0, b=0)

hist(arima_2\$residuals, xlab=”Residuals”, xlim=c(-20,20))

qqnorm(arima_2\$residuals, main=””)qqline(arima_2\$residuals)

# Forecasting

#predict the next 12 monthsarima_2.predict <- predict(arima_2,n.ahead=12)matrix(c(arima_2.predict\$pred-1.96*arima_2.predict\$se,         arima_2.predict\$pred,         arima_2.predict\$pred+1.96*arima_2.predict\$se), 12,3,       dimnames=list( c(241:252) ,c(“LB”,”Pred”,”UB”)) )

plot(gas_prod, xlim=c(145,252),     xlab = “Time (months)”,     ylab = “Gasoline production (millions of barrels)”,     ylim=c(360,440))lines(arima_2.predict\$pred)lines(arima_2.predict\$pred+1.96*arima_2.predict\$se, col=4, lty=2)lines(arima_2.predict\$pred-1.96*arima_2.predict\$se, col=4, lty=2)

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