Academic Writing

I have a assignment its in Machine learning. I have some questions with me , it should be coded in python or java . Questions: Perform the following steps to classify the churn data with the Naïve Bayes classifier: 1. Load the e1071 library and employ the naiveBayes function to build the classifier: > library(e1071) > classifier=naiveBayes(trainset[, !names(trainset) %in% c("churn")], trainset$churn) 2. Type classifier to examine the function call, a-priori probability, and conditional probability: > classifier Naive Bayes Classifier for Discrete Predictors Call: [login to view URL](x = trainset[, !names(trainset) %in% c("churn")], y = trainset$churn) A-priori probabilities: trainset$churn yes no 0.1477322 0.8522678 Conditional probabilities: international_plan trainset$churn no yes yes 0.70467836 0.29532164 no 0.93512418 0.06487582 3. Next, you can generate a classification table for the testing dataset: > [login to view URL] = table(predict(classifier, testset[, !names(testset) %in% c("churn")]), testset$churn) > [login to view URL] yes no yes 68 45 no 73 832 4. Lastly, you can generate a confusion matrix from the classification table: > confusionMatrix([login to view URL]) Confusion Matrix and Statistics yes no yes 68 45 no 73 832 Accuracy : 0.8841 95% CI : (0.8628, 0.9031) No Information Rate : 0.8615 P-Value [Acc > NIR] : 0.01880 Kappa : 0.4701 Mcnemar's Test P-Value : 0.01294 Sensitivity : 0.4823 Specificity : 0.9487 Pos Pred Value : 0.6018 Neg Pred Value : 0.9193 Prevalence : 0.1385 Detection Rate : 0.0668 Detection Prevalence : 0.1110 Balanced Accuracy : 0.7155 'Positive' Class : yes