Medical Data Analytics Using R

Medical Data Analytics Using R 1.) R for Recency => months since last donation, 2.) F for Frequency => total number of donation, 3.) M for Monetary => total amount of blood donated in c.c., 4.) T for Time => months since first donation and 5.) Binary variable => 1 -> donated blood, 0-> didnt donate blood. The main idea behind this dataset is the concept of relationship management CRM. Based on three metrics: Recency, Frequency and Monetary (RFM) which are 3 out of the 5 attributes of the dataset, we would be able to predict whether a customer is likely to donate blood again based to a marketing campaign. For example, customers who have donated or visited more currently (Recency), more frequently (Frequency) or made higher monetary values (Monetary) are more likely to respond to a marketing effort. Customers with less RFM score are less likely to react. It is also known in customer behavior, that the time of the first positive interaction (donation, purchase) is not significant. However, the Recency of the last donation is very important. In the traditional RFM implementation each customer is ranked based on his RFM value parameters against all the other customers and that develops a score for every customer. Customers with bigger scores are more likely to react in a positive way for example (visit again or donate). The model constructs the formula which could predict the following problem. Keep in repository only customers that are more likely to continue donating in the future and remove those who are less likely to donate, given a certain period of time. The previous statement also determines the problem which will be trained and tested in this project. Firstly, I created a .csv file and generated 748 unique random numbers in Excel in the domain [1,748] in the first column, which corresponds to the customers or users ID. Then I transferred the whole data from the .txt file (transfusion.data) to the .csv file in excel by using the delimited (,) option. Then I randomly split it in a train file and a test file. The train file contains the 530 instances and the test file has the 218 instances. Afterwards, I read both the training dataset and the test dataset. From the previous results, we can see that we have no missing or invalid values. Data ranges and units seem reasonable. Figure 1 above depicts boxplots of all the attributes and for both train and test datasets. By examining the figure, we notice that both datasets have similar distributions and there are some outliers (Monetary > 2,500) that are visible. The volume of blood variable has a high correlation with frequency. Because the volume of blood that is donated each time is fixed, the Monetary value is proportional to the Frequency (number of donations) each person gave. For example, if the amount of blood drawn in each person was 250 ml/bag (Taiwan Blood Services Foundation 2007) March then Monetary = 250*Frequency. This is also why in the predictive model we will not consider the Monetary attribute in the implementation. So, it is reasonable to expect that customers with higher frequency will have a lot higher Monetary value. This can be verified also visually by examining the Monetary outliers for the train set. We retrieve back 83 instances. In order, to understand better the statistical dispersion of the whole dataset (748 instances) we will look at the standard deviation (SD) between the Recency and the variable whether customer has donated blood (Binary variable) and the SD between the Frequency and the Binary variable.The distribution of scores around the mean is small, which means the data is concentrated. This can also be noticed from the plots. From this correlation matrix, we can verify what was stated above, that the frequency and the monetary values are proportional inputs, which can be noticed from their high correlation. Another observation is that the various Recency numbers are not factors of 3. This goes to opposition with what the description said about the data being collected every 3 months. Additionally, there is always a maximum number of times you can donate blood per certain period (e.g. 1 time per month), but the data shows that. 36 customers donated blood more than once and 6 customers had donated 3 or more times in the same month. The features that will be used to calculate the prediction of whether a customer is likely to donate again are 2, the Recency and the Frequency (RF). The Monetary feature will be dropped. The number of categories for R and F attributes will be 3. The highest RF score will be 33 equivalent to 6 when added together and the lowest will be 11 equivalent to 2 when added together. The threshold for the added score to determine whether a customer is more likely to donate blood again or not, will be set to 4 which is the median value. The users will be assigned to categories by sorting on RF attributes as well as their scores. The file with the donators will be sorted on Recency first (in ascending order) because we want to see which customers have donated blood more recently. Then it will be sorted on frequency (in descending order this time because we want to see which customers have donated more times) in each Recency category. Apart from sorting, we will need to apply some business rules that have occurred after multiple tests: For Recency (Business rule 1): If the Recency in months is less than 15 months, then these customers will be assigned to category 3. If the Recency in months is equal or greater than 15 months and less than 26 months, then these customers will be assigned to category 2. Otherwise, if the Recency in months is equal or greater than 26 months, then these customers will be assigned to category 1 And for Frequency (Business rule 2): If the Frequency is equal or greater than 25 times, then these customers will be assigned to category 3. If the Frequency is less than 25 times or greater than 15 months, then these customers will be assigned to category 2. If the Frequency is equal or less than 15 times, then these customers will be assigned to category 1 RESULTS The output of the program are two smaller files that have resulted from the train file and the other one from the test file, that have excluded several customers that should not be considered future targets and kept those that are likely to respond. Some statistics about the precision, recall and the balanced F-score of the train and test file have been calculated and printed. Furthermore, we compute the absolute difference between the results retrieved from the train and test file to get the offset error between these statistics. By doing this and verifying that the error numbers are negligible, we validate the consistency of the model implemented. Moreover, we depict two confusion matrices one for the test and one for the training by calculating the true positives, false negatives, false positives and true negatives. In our case, true positives correspond to the customers (who donated on March 2007) and were classified as future possible donators. False negatives correspond to the customers (who donated on March 2007) but were not classified as future possible targets for marketing campaigns. False positives correlate to customers (who did not donate on March 2007) and were incorrectly classified as possible future targets. Lastly, true negatives which are customers (who did not donate on March 2007) and were correctly classified as not plausible future donators and therefore removed from the data file. By classification we mean the application of the threshold (4) to separate those customers who are more likely and less likely to donate again in a certain future period. Lastly, we calculate 2 more single value metrics for both train and test files the Kappa Statistic (general statistic used for classification systems) and Matthews Correlation Coefficient or cost/reward measure. Both are normalized statistics for classification systems, its values never exceed 1, so the same statistic can be used even as the number of observations grows. The error for both measures are MCC error: 0.002577   and Kappa error:   0.002808, which is very small (negligible), similarly with all the previous measures. REFERENCES UCI Machine Learning Repository (2008) UCI machine learning repository: Blood transfusion service center data set. Available at: http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center (Accessed: 30 January 2017). Fundation, T.B.S. (2015) Operation department. Available at: http://www.blood.org.tw/Internet/english/docDetail.aspx?uid=7741pid=7681docid=37144 (Accessed: 31 January 2017). The Appendix with the code starts below. However the whole code has been uploaded on my Git Hub profile and this is the link where it can be accessed. https://github.com/it21208/RassignmentDataAnalysis/blob/master/RassignmentDataAnalysis.R library(ggplot2) library(car)   # read training and testing datasets traindata à ¯Ã†â€™Ã… ¸Ãƒâ€šÃ‚   read.csv(C:/Users/Alexandros/Dropbox/MSc/2nd Semester/Data analysis/Assignment/transfusion.csv) testdata à ¯Ã†â€™Ã… ¸Ãƒâ€šÃ‚   read.csv(C:/Users/Alexandros/Dropbox/MSc/2nd Semester/Data analysis/Assignment/test.csv) # assigning the datasets to dataframes dftrain à ¯Ã†â€™Ã… ¸ data.frame(traindata) dftest à ¯Ã†â€™Ã… ¸ data.frame(testdata) sapply(dftrain, typeof) # give better names to columns names(dftrain)[1] à ¯Ã†â€™Ã… ¸ ID names(dftrain)[2] à ¯Ã†â€™Ã… ¸ recency names(dftrain)[3]à ¯Ã†â€™Ã… ¸frequency names(dftrain)[4]à ¯Ã†â€™Ã… ¸cc names(dftrain)[5]à ¯Ã†â€™Ã… ¸time names(dftrain)[6]à ¯Ã†â€™Ã… ¸donated # names(dftest)[1]à ¯Ã†â€™Ã… ¸ID names(dftest)[2]à ¯Ã†â€™Ã… ¸recency names(dftest)[3]à ¯Ã†â€™Ã… ¸frequency names(dftest)[4]à ¯Ã†â€™Ã… ¸cc names(dftest)[5]à ¯Ã†â€™Ã… ¸time names(dftest)[6]à ¯Ã†â€™Ã… ¸donated # drop time column from both files dftrain$time à ¯Ã†â€™Ã… ¸ NULL dftest$time à ¯Ã†â€™Ã… ¸ NULL #   sort (train) dataframe on Recency in ascending order sorted_dftrain à ¯Ã†â€™Ã… ¸ dftrain[ order( dftrain[,2] ), ] #   add column in (train) dataframe -   hold score (rank) of Recency for each customer sorted_dftrain[ , Rrank] à ¯Ã†â€™Ã… ¸ 0 #   convert train file from dataframe format to matrix matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain, as.numeric)) #   sort (test) dataframe on Recency in ascending order sorted_dftest à ¯Ã†â€™Ã… ¸ dftest[ order( dftest[,2] ), ] #   add column in (test) dataframe -hold score (rank) of Recency for each customer sorted_dftest[ , Rrank] à ¯Ã†â€™Ã… ¸ 0 #   convert train file from dataframe format to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest, as.numeric)) # categorize matrix_train and add scores for Recency apply business rule for(i in 1:nrow(matrix_train)) { if (matrix_train [i,2]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train [i,6] à ¯Ã†â€™Ã… ¸ 3 } else if ((matrix_train [i,2] = 15)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train [i,6] à ¯Ã†â€™Ã… ¸ 2 } else {   matrix_train [i,6] à ¯Ã†â€™Ã… ¸ 1   }   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   } # categorize matrix_test and add scores for Recency apply business rule for(i in 1:nrow(matrix_test)) { if (matrix_test [i,2]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test [i,6] à ¯Ã†â€™Ã… ¸ 3 } else if ((matrix_test [i,2] = 15)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test [i,6] à ¯Ã†â€™Ã… ¸ 2 } else {   matrix_test [i,6] à ¯Ã†â€™Ã… ¸ 1 }   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   } # convert matrix_train back to dataframe sorted_dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # sort dataframe 1rst by Recency Rank (desc.) then by Frequency (desc.) sorted_dftrain_2à ¯Ã†â€™Ã… ¸ sorted_dftrain[order(-sorted_dftrain[,6], -sorted_dftrain[,3] ), ] # add column in train dataframe- hold Frequency score (rank) for each customer sorted_dftrain_2[ , Frank] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain_2, as.numeric)) # convert matrix_test back to dataframe sorted_dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # sort dataframe 1rst by Recency Rank (desc.) then by Frequency (desc.) sorted_dftest2 à ¯Ã†â€™Ã… ¸ sorted_dftest[ order( -sorted_dftest[,6], -sorted_dftest[,3] ), ] # add column in test dataframe- hold Frequency score (rank) for each customer sorted_dftest2[ , Frank] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest2, as.numeric)) #categorize matrix_train, add scores for Frequency for(i in 1:nrow(matrix_train)){    if (matrix_train[i,3] >= 25) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train[i,7] à ¯Ã†â€™Ã… ¸ 3    } else if ((matrix_train[i,3] > 15) (matrix_train[i,3]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train[i,7] à ¯Ã†â€™Ã… ¸ 2    } else {   matrix_train[i,7] à ¯Ã†â€™Ã… ¸ 1   }   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   } #categorize matrix_test, add scores for Frequency for(i in 1:nrow(matrix_test)){    if (matrix_test[i,3] >= 25) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test[i,7] à ¯Ã†â€™Ã… ¸ 3    } else if ((matrix_test[i,3] > 15) (matrix_test[i,3]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test[i,7] à ¯Ã†â€™Ã… ¸ 2    } else {  Ãƒâ€šÃ‚   matrix_test[i,7] à ¯Ã†â€™Ã… ¸ 1   } } #   convert matrix test back to dataframe sorted_dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # sort (train) dataframe 1rst on Recency rank (desc.) 2nd Frequency rank (desc.) sorted_dftrain_2 à ¯Ã†â€™Ã… ¸ sorted_dftrain[ order( -sorted_dftrain[,6], -sorted_dftrain[,7] ), ] # add another column for the Sum of Recency rank and Frequency rank sorted_dftrain_2[ , SumRankRAndF] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain_2, as.numeric)) #   convert matrix test back to dataframe sorted_dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # sort (train) dataframe 1rst on Recency rank (desc.) 2nd Frequency rank (desc.) sorted_dftest2 à ¯Ã†â€™Ã… ¸ sorted_dftest[ order( -sorted_dftest[,6],   -sorted_dftest[,7] ), ] # add another column for the Sum of Recency rank and Frequency rank sorted_dftest2[ , SumRankRAndF] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest2, as.numeric)) # sum Recency rank and Frequency rank for train file for(i in 1:nrow(matrix_train)) { matrix_train[i,8] à ¯Ã†â€™Ã… ¸ matrix_train[i,6] + matrix_train[i,7] } # sum Recency rank and Frequency rank for test file for(i in 1:nrow(matrix_test)) { matrix_test[i,8] à ¯Ã†â€™Ã… ¸ matrix_test[i,6] + matrix_test[i,7] } # convert matrix_train back to dataframe sorted_dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # sort train dataframe according to total rank in descending order sorted_dftrain_2 à ¯Ã†â€™Ã… ¸ sorted_dftrain[ order( -sorted_dftrain[,8] ), ] # convert sorted train dataframe matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain_2, as.numeric)) # convert matrix_test back to dataframe sorted_dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # sort test dataframe according to total rank in descending order sorted_dftest2 à ¯Ã†â€™Ã… ¸ sorted_dftest[ order( -sorted_dftest[,8] ), ] # convert sorted test dataframe to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest2, as.numeric)) # apply business rule check count customers whose score >= 4 and that Have Donated, train file # check count for all customers that have donated in the train dataset count_train_predicted_donations à ¯Ã†â€™Ã… ¸ 0 counter_train à ¯Ã†â€™Ã… ¸ 0 number_donation_instances_whole_train à ¯Ã†â€™Ã… ¸ 0 false_positives_train_counter à ¯Ã†â€™Ã… ¸ 0 for(i in 1:nrow(matrix_train)) {    if ((matrix_train[i,8] >= 4) (matrix_train[i,5] == 1)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   count_train_predicted_donations = count_train_predicted_donations + 1   } if ((matrix_train[i,8] >= 4) (matrix_train[i,5] == 0)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   false_positives_train_counter = false_positives_train_counter + 1}    if (matrix_train[i,8] >= 4) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   counter_train à ¯Ã†â€™Ã… ¸ counter_train + 1   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   }    if (matrix_train[i,5] == 1) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   number_donation_instances_whole_train à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_train + 1    } } # apply business rule check count customers whose score >= 4 and that Have Donated, test file # check count for all customers that have donated in the test dataset count_test_predicted_donations à ¯Ã†â€™Ã… ¸ 0 counter_test à ¯Ã†â€™Ã… ¸ 0 number_donation_instances_whole_test à ¯Ã†â€™Ã… ¸ 0 false_positives_test_counter à ¯Ã†â€™Ã… ¸ 0 for(i in 1:nrow(matrix_test)) {    if ((matrix_test[i,8] >= 4) (matrix_test[i,5] == 1)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   count_test_predicted_donations = count_test_predicted_donations + 1   } if ((matrix_test[i,8] >= 4) (matrix_test[i,5] == 0)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   false_positives_test_counter = false_positives_test_counter + 1}    if (matrix_test[i,8] >= 4) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   counter_test à ¯Ã†â€™Ã… ¸ counter_test + 1   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   }    if (matrix_test[i,5] == 1) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   number_donation_instances_whole_test à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_test + 1   Ãƒâ€šÃ‚   } } # convert matrix_train to dataframe dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # remove the group of customers who are less likely to donate again in the future from train file dftrain_final à ¯Ã†â€™Ã… ¸ dftrain[c(1:counter_train),1:8] # convert matrix_train to dataframe dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # remove the group of customers who are less likely to donate again in the future from test file dftest_final à ¯Ã†â€™Ã… ¸ dftest[c(1:counter_test),1:8] # save final train dataframe as a CSV in the specified directory reduced target future customers write.csv(dftrain_final, file = C:\Users\Alexandros\Dropbox\MSc\2nd Semester\Data analysis\Assignment\train_output.csv, row.names = FALSE) #save final test dataframe as a CSV in the specified directory reduced target future customers write.csv(dftest_final, file = C:\Users\Alexandros\Dropbox\MSc\2nd Semester\Data analysis\Assignment\test_output.csv, row.names = FALSE) #train precision=number of relevant instances retrieved / number of retrieved instances collect.530 precision_train à ¯Ã†â€™Ã… ¸Ãƒâ€šÃ‚   count_train_predicted_donations / counter_train # train recall = number of relevant instances retrieved / number of relevant instances in collect.530 recall_train à ¯Ã†â€™Ã… ¸ count_train_predicted_donations / number_donation_instances_whole_train # measure combines PrecisionRecall is harmonic mean of PrecisionRecall balanced F-score for # train file f_balanced_score_train à ¯Ã†â€™Ã… ¸ 2*(precision_train*recall_train)/(precision_train+recall_train) # test precision precision_test à ¯Ã†â€™Ã… ¸ count_test_predicted_donations / counter_test # test recall recall_test à ¯Ã†â€™Ã… ¸ count_test_predicted_donations / number_donation_instances_whole_test # the balanced F-score for test file f_balanced_score_test à ¯Ã†â€™Ã… ¸ 2*(precision_test*recall_test)/(precision_test+recall_test) # error in precision error_precision à ¯Ã†â€™Ã… ¸ abs(precision_train-precision_test) # error in recall error_recall à ¯Ã†â€™Ã… ¸ abs(recall_train-recall_test) # error in f-balanced scores error_f_balanced_scores à ¯Ã†â€™Ã… ¸ abs(f_balanced_score_train-f_balanced_score_test) # Print Statistics for verification and validation cat(Precision with training dataset: , precision_train) cat(Recall with training dataset: , recall_train) cat(Precision with testing dataset: , precision_test) cat(Recall with testing dataset: , recall_test) cat(The F-balanced scores with training dataset: , f_balanced_score_train) cat(The F-balanced scores with testing dataset:   , f_balanced_score_test) cat(Error in precision: , error_precision) cat(Error in recall: , error_recall) cat(Error in F-balanced scores: , error_f_balanced_scores) # confusion matrix (true positives, false positives, false negatives, true negatives) # calculate true positives for train which is the variable count_train_predicted_donations # calculate false positives for train which is the variable false_positives_train_counter # calculate false negatives for train false_negatives_for_train à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_train count_train_predicted_donations # calculate true negatives for train true_negatives_for_train à ¯Ã†â€™Ã… ¸ (nrow(matrix_train) number_donation_instances_whole_train) false_positives_train_counter collect_trainà ¯Ã†â€™Ã… ¸c(false_positives_train_counter, true_negatives_for_train, count_train_predicted_donations, false_negatives_for_train) # calculate true positives for test which is the variable count_test_predicted_donations # calculate false positives for test which is the variable false_positives_test_counter # calculate false negatives for test false_negatives_for_test à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_test count_test_predicted_donations # calculate true negatives for test true_negatives_for_testà ¯Ã†â€™Ã… ¸(nrow(matrix_test)-number_donation_instances_whole_test)- false_positives_test_counter collect_test à ¯Ã†â€™Ã… ¸ c(false_positives_test_counter, true_negatives_for_test, count_test_predicted_donations, false_negatives_for_test) TrueCondition à ¯Ã†â€™Ã… ¸ factor(c(0, 0, 1, 1)) PredictedCondition à ¯Ã†â€™Ã… ¸ factor(c(1, 0, 1, 0)) # print confusion matrix for train df_conf_mat_train à ¯Ã†â€™Ã… ¸ data.frame(TrueCondition,PredictedCondition,collect_train) ggplot(data = df_conf_mat_train, mapping = aes(x = PredictedCondition, y = TrueCondition)) +    geom_tile(aes(fill = collect_train), colour = white) +    geom_text(aes(label = sprintf(%1.0f, collect_train)), vjust = 1) +    scale_fill_gradient(low = blue, high = red) +    theme_bw() + theme(legend.position = none) #   print confusion matrix for test df_conf_mat_test à ¯Ã†â€™Ã… ¸ data.frame(TrueCondition,PredictedCondition,collect_test) ggplot(data =   df_conf_mat_test, mapping = aes(x = PredictedCondition, y = TrueCondition)) +    geom_tile(aes(fill = collect_test), colour = white) +    geom_text(aes(label = sprintf(%1.0f, collect_test)), vjust = 1) +    scale_fill_gradient(low = blue, high = red) +    theme_bw() + theme(legend.position = none) # MCC = (TP * TN FP * FN)/sqrt((TP+FP) (TP+FN) (FP+TN) (TN+FN)) for train values mcc_train à ¯Ã†â€™Ã… ¸ ((count_train_predicted_donations * true_negatives_for_train) (false_positives_train_counter * false_negatives_for_train))/sqrt((count_train_predicted_donations+false_positives_train_counter)*(count_train_predicted_donations+false_negatives_for_train)*(false_positives_train_counter+true_negatives_for_train)*(true_negatives_for_train+false_negatives_for_train)) # print MCC for train cat(Matthews Correlation Coefficient for train: ,mcc_train) # MCC = (TP * TN FP * FN)/sqrt((TP+FP) (TP+FN) (FP+TN) (TN+FN)) for test values mcc_test à ¯Ã†â€™Ã… ¸ ((count_test_predicted_donations * true_negatives_for_test) (false_positives_test_counter * false_negatives_for_test))/sqrt((count_test_predicted_donations+false_positives_test_counter)*(count_test_predicted_donations+false_negatives_for_test)*(false_positives_test_counter+true_negatives_for_test)*(true_negatives_for_test+false_negatives_for_test)) # print MCC for test cat(Matthews Correlation Coefficient for test: ,mcc_test) # print MCC err between train and err cat(Matthews Correlation Coefficient error: ,abs(mcc_train-mcc_test)) # Total = TP + TN + FP + FN for train total_train à ¯Ã†â€™Ã… ¸ count_train_predicted_donations + true_negatives_for_train + false_positives_train_counter + false_negatives_for_train # Total = TP + TN + FP + FN for test   total_test à ¯Ã†â€™Ã… ¸ count_test_predicted_donations + true_negatives_for_test + false_positives_test_counter + false_negatives_for_test # totalAccuracy = (TP + TN) / Total for train values totalAccuracyTrain à ¯Ã†â€™Ã… ¸ (count_train_predicted_donations + true_negatives_for_train)/ total_train # totalAccuracy = (TP + TN) / Total for test values totalAccuracyTest à ¯Ã†â€™Ã… ¸ (count_test_predicted_donations + true_negatives_for_test)/ total_test # randomAccuracy = ((TN+FP)*(TN+FN)+(FN+TP)*(FP+TP)) / (Total*Total)   for train values randomAccuracyTrainà ¯Ã†â€™Ã… ¸((true_negatives_for_train+false_positives_train_counter)*(true_negatives_for_train+false_negatives_for_train)+(false_negatives_for_train+count_train_predicted_donations)*(false_positives_train_counter+count_train_predicted_donations))/(total_train*total_train) # randomAccuracy = ((TN+FP)*(TN+FN)+(FN+TP)*(FP+TP)) / (Total*Total)   for test values randomAccuracyTestà ¯Ã†â€™Ã… ¸((true_negatives_for_test+false_positives_test_counter)*(true_negatives_for_test+false_negatives_for_test)+(false_negatives_for_test+count_test_predicted_donations)*(false_positives_test_counter+count_test_predicted_donations))/(total_test*total_test) # kappa = (totalAccuracy randomAccuracy) / (1 randomAccuracy) for train kappa_train à ¯Ã†â€™Ã… ¸ (totalAccuracyTrain-randomAccuracyTrain)/(1-randomAccuracyTrain) # kappa = (totalAccuracy randomAccuracy) / (1 randomAccuracy) for test kappa_test à ¯Ã†â€™Ã… ¸ (totalAccuracyTest-randomAccuracyTest)/(1-randomAccuracyTest) # print kappa error cat(Kappa error: ,abs(kappa_train-kappa_test))

Edlhodm Assignment

Table of Contents QUESTION 12 1. 1Role of communication2 1. 2 Positive educator-learner relationships2 1. 3Learner participation in a multicultural classroom2 QUESTION 23 2. 1 Improve learner motivation in classroom3 2. 2 Draw up the following of a positive classroom policy:3 2. 2. 1Aims and objectives of our class3 2. 2. 2Rules of our classroom3 2. 2. 2Task division3 2. 3 Define the following concepts:3 2. 3. 1 Leadership3 2. 3. 2 Control3 2. 3. 3 Intrinsic motivation3 2. 3. 4 Communication4 2. 3. 5 Cooperative learning4 2. 4 Autocratic and democratic styles4 2. 5 Conveying message4 QUESTION 35 Introduction5Five elements of delictual liability5 1. Act or conduct5 2. Wrongfulness5 3. Fault5 4. Causation6 5. Harmful consequence6 Contributory Fault6 Conclusion6 REFERENCES7 QUESTION 1 1. Role of communication Any relationship, without communication would collapse. To create a positive atmosphere in classroom – communication has to occur. What is communication? Coetzee, van Nieker k and Wyderman (2008: 82) describe communication as the transmitting of an idea by someone (the sender) and the understanding thereof by another (the receiver). Thus, the educator must be understood by the learner and learner must be understood by educator when conversing.Role of communication involves creating an understanding by the setting of ground rules, creating open professional dialogue with learners, holding personal discussions and creation of better relationships with learner. For the above responsibilities to be of impact, the educator involved need to adhere and fully commit him or herself into achieving each task profoundly. 1. 2 Positive educator-learner relationships According to Pianta (1999:1), positive educator-learner relationships are characterized by open communication, as well as emotional and academic support that exist between learners and educators.Positive educator-learner relationships become particularly important during early adolescence, as learner mov e from the supportive environment of primary school to the more disjointed atmosphere of a high school. They also become important for ensuring good academic performance from learners. I know this because the classes I enjoyed (when I was still a learner) were the ones I did well in. So for me to do well in those classes – I had to be internally happy in the class. This goes inline with what a theorist once wrote that any performance – including academic performance – is a product of ability multiplied by motivation.Motivation is intrinsic and involves emotion. If educator requires learners to perform – the educator has to motivate the learner in order for the learner to perform at the best of his or her ability. A motivated learner will perform well academically and then the educator will be satisfied by the outcome, resulting in a positive atmosphere in the classroom. 3. Learner participation in a multicultural classroom The first thing to do is to lear n about the different cultures in the classroom from cultural insiders, learners, books and internet.Adopt a story-telling teaching method whereby the learner will get an opportunity to share an experience using his or her past experience in his or her cultural background environment e. g. having a Zimbabwean in class should lead you to asking that learner about how certain thing in South Africa will he or she perform in Zimbabwe. They should share this knowledge also in oral and written form. Team work or group work should be adopted and the desks in the class should arrange as such. How the learners sit in class does also promote their participation. Each group should reflect diversity.When the individual learner or group ask question, the educator, is recommended to respond in a positive unbiased way to the learner question so to encourage repeated questioning behaviour. It is essential for the school to allow educator to undergo diversity development workshops so that there can be an understanding and respect of cultural differences in the classroom. Acknowledge each culture hero and communicate all culture holidays. Treat multicultural learners equally do not have culture favourites. QUESTION 2 2. 1 Improve learner motivation in classroom a) Reward learners (Tom 2008:1). ) Make sure course has real value (Tom 2008:1). c) Help learners perform better (Tom 2008:1). d) Set clear expectations for the course (Tom 2008:1). e) Tell them they’re wrong when wrong (Tom 2008:1). 2. 2 Draw up the following of a positive classroom policy: 2. 2. 1Aims and objectives of our class The objectives are a breakdown of the classroom vision. These objectives must be SMART objectives (Specific, Measurable, Achievable, Realistic and Time-limited) (Coetzee et al. 2008: 6). 2. 2. 2Rules of our classroom There must be an organization and management plan in place that will enforce efficient rules and procedures.They must be consistently followed and in which the educator and the learner clearly understand expectation of the learner behavior (Coetzee 2006: 40). 2. 2. 2Task division The task division must be unambiguous and clear. It must be according the class ability and standard of achievement. 2. 3 Define the following concepts: 2. 3. 1 Leadership Leadership is about inspiring persons or groups to such an extent that they willingly and enthusiastically work to accomplish set aims (van Niekerk 1995: 4). 2. 3. 2 Control Controlling is assessing the work done and being done to re-align and correct it when necessary (Study guide 2006: 25). . 3. 3 Intrinsic motivation Intrinsic motivation means that a person works because of an inner desire to be successful at a certain task (Coetzee et al 2008: 103). 2. 3. 4 Communication Communication can be described as the transmitting of an idea by someone (the sender) and the understanding thereof by another (the receiver) (Coetzee et al 2008: 82). 2. 3. 5 Cooperative learning Can be defined as a team approach to lea rning where each member of the group is dependent on the other members to accomplish a specific learning task or assignment (Coetzee et al 2008: 108). 2. Autocratic and democratic styles Autocratic style It is characterised by the strong leadership role of the educator namely: †¢ One-way communication. †¢ Little opportunity for creative thinking. †¢ Learner participation is usually more passive. †¢ Rigid discipline. †¢ The educator is more reserved (unapproachable). Democratic style It is characterised by a calm and inviting teaching attitude, namely: †¢ Self-expression by learners. †¢ A team spirit between educator and learners. †¢ The use of variety of sources, so that the educator is not the only source. 2. 5 Conveying messageIn a model for understanding communication, the communication process is described as: the steps between a source and a receiver that result in the transference of meaning. There is a need for a purpose (expressed as m essage) before communication can take place. To create that message the source had to initiate the process by a thought (idea, instruction, request). Then the source converts the message into a symbolic form. The message is then communicated through the medium called the channel. The receiver then decodes the message by assigning meaning to the message.Through feedback it will be then determined whether the understanding is achieved or not (Coetzee et al 2008: 86). QUESTION 3 Introduction The law of delict is a section of private law. This branch of law deals with civil wrongs against another person that cause the injured party to go to court to seek compensation from the wrongdoer for damages (Coetzee et al, 2008: 226). In the law of delict, also called â€Å"tort law† in some countries, a duty of care has to be established before anyone can be held liable for damages suffered because of his or her negligent behaviour (Beloff, Kerr & Demetriou in Rossouw, 1999:112).In this a ssignment, an analysis would be made regarding the duty of care that should have existed and was owed by the team coach and the school. The analysis would be made in reference to the five elements of a delict: action or conduct, wrongfulness, fault, causation and harmful consequence. The elements are then applied to the scenario and then it will be concluded if the team coach is liable or not and if there is not any contributory fault of the player. Five elements of delictual liability 1. Act or conduct According to Coetzee et al (2008: 226) to constitute a delict, one person (e. g. he educator) must have caused harm or damage to another by his or her action or conduct. The conduct must be a voluntary human action and may be either a positive action (i. e. doing something) or an omission (i. e. failure to do something). In the scenario, due to the team coach’s conduct of not inspecting the basket ball ground (i. e. failure to do something). and also, instructing the injured ( bleeding) player to phone his parents while bleeding- this requirement is met (i. e. doing something) or. 2. Wrongfulness Coetzee et al (2008: 226) state that the act (conduct) that causes harm must be wrongful i. e. t must be legally reprehensible or unreasonable in terms of legal convictions of the community, To test for unlawfulness, the boni mores principle is applied. The question here is whether the harm caused was unjustified in the circumstances. Most types of sport have ordinary as well as unexpected dangers. Referring to these dangers, Smith (2002:1) states that â€Å"it is prudent for a coach in the discharging of his or her duty to provide players with adequate warning†. This is called the disclosure requirement and implies that coaches cannot assume that participants know the dangers, even when they are very obvious.Therefore, the team coach was wrong for not inspecting the ground before the players practice on it. He was also wrong for telling the player to do t he phone call while injured. This requirement is met. 3. Fault The act must be the result of fault in the form of an intent (dolus) or negligence (culpa). The ‘fault’ refers to the blameworthy attitude or conduct of someone who has acted wrongfully (Coetzee et al 2008: 226). Regarding the playing field, surrounding grounds and other facilities, proper measures should be in place to safeguard all participants.Dangerous objects in the vicinity of playing fields should be removed or properly covered (Rossouw 2004:37). According to the scenario, it was the coach fault the player was injured. He should have inspected the ground so that the protruding steel could be identified. This requirement is met. 4. Causation There must be a causal link between the conduct of the perpetrator and the harm suffered by the victim (Coetzee et al 2008: 227). When injuries do occur, the coach should assess whether a player is fit to train, and training should be supervised in a proper way.Nor mally these assessments can be done without any immediate pressure, but when an on-field injury occurs, the liability of the coach may become a real issue (Rossouw 2004:37). Smith (2002:2) refers to Mogabgob v Orleans Parish School Board 239 2d 456 (1970) where a coach sent a player to hospital after two hours, whilst he actually needed urgent attention due to heat stroke and exhaustion. The player subsequently died and the court held the coach liable, because evidence suggested that the player would have survived if medical treatment had been administered sooner.In the scenario, the injury of the player might complicate because it is a head injury. The coach did not assess (according to the given scenario) the injured player and seems to care less and instructs the player to phone his or her parents. This is simple negligence from the couch and will result to a medical complication. This requirement is met. 5. Harmful consequence Since a delict is a wrongful and culpable act which has a harmful consequence, damages (causing harm) in the form of patrimonial (material) loss or non -patrimonial loss must be present.It is a basic duty of a coach to do everything in his or her power to prevent injuries to players (Coetzee et al 2008: 227). In the scenario the damages the player has suffered non-patrimonial damages. This requirement is met. Contributory Fault Contributory fault involves some of fault (in the form of negligence) on the part of injured person. This results when learner fails to exercise duty of care for someone in his or her age, then the court may decide that the negligent educator is not solely liable for damages resulting from an injury (Coetzee et al 2008: 230).According to the scenario, the player’s conduct was good because he was on the ground practising. The team coach – on behalf of his school – had to inspect the Discipline High School basketball ground. That was not the responsibility of the player. Regarding phone call to his parents – if he carries on according to the coach’s instruction – he cannot be held liable simply because head injuries can be associated with brain malfunctioning. Thus, he might not be thinking clearly. Conclusion It can then be concluded that there was no contributory fault on the player part. All the five required elements have been met.In South African law, when these five elements are present, the team coach (educator) can be found guilty of delict. This is due to the fact that the team coach by acting negligently caused damages to the injured player. Now, the player will need to be compensated for the loss suffered in the court of law (Basson & Loubser, 2001: Ch5, 11). REFERENCES Basson JAA & Loubser MM 2001. Sport and the Law in South Africa. Butterworths, Durban. In: Rossouw, J. P. 2004. â€Å"Where education law and sport law meet: the duty of care of the educator-coach in South African schools† North-West University, Potchefstroom Campu s.SA-Educ JOURNAL Volume 1, Number 2, pp. 28-40. Coetzee, SA, van Niekerk, EJ & Wyderman JL. 2008. â€Å"An educator’s guide to effective classroom management†. Pretoria: Van Schaik. McInnes-Wilson Lawyers. In: Rossouw, J. P. 2004. â€Å"Where education law and sport law meet: the duty of care of the educator-coach in South African schools† North-West University, Potchefstroom Campus. SA-Educ JOURNAL Volume 1, Number 2, pp. 28-40. Pianta, R. C. , 1999. Enhancing Relationships between Children and Teachers. Washington, D. C. : American Psychological Assn. In â€Å"Forming positive student-teacher relationships† [Online] Available: http://www. edu. niu. edu/~shumow/itt/StudentTchrRelationships. pdf Rossouw, J. P. 2004. â€Å"Where education law and sport law meet: the duty of care of the educator-coach in South African schools† North-West University, Potchefstroom Campus. SA-Educ JOURNAL Volume 1, Number 2, pp. 28-40. Smith F 2002. Liability for coac hes and school authorities in school spo rt. MW Education Update. Brisbane: Tom. S. 2008. â€Å"Motivate Your Learners with These 5 Simple Tips† [Online] Available: http://www. articulate. com/rapid-elearning/motivate-your-learners-with-these-5-simple-tips/

Purchasing Psychology Essay Topics List

Purchasing Psychology Essay Topics List Psychology Essay Topics List and Psychology Essay Topics List - The Perfect Combination Certainly, psychology is among the most intriguing branches of science out there. Forensic psychology is a fairly young area of scholarship. Cognitive psychology proved to be a legitimate revolution in psychology. Development psychology is centered on the lifespan of human beings, so you've got several topics to select from. You are necessary to understand the procedure for selecting a superior topic. In the procedure for writing research articles, it's required to adhere to a specific topic. Begin with retracing each one of your steps because you should remind readers your topic and arguments. The very first step in looking for a great dissertation topic on psychology is to dedicate a little time and research for a topic one is acquainted with. 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An argumentative essay requires you to choose a topic and have a position on it. Choosing Good Psycholo gy Essay Topics List In any event, you'll probably be asked to finish a research paper during the class, and it's essential to select a topic that interests you. As a way to compose a paper, you need to compose a research question. If you still feel you need help, even if you've managed to decide on a topic, you may always seek the services of a custom writing service to assist you produce a fabulous research paper of which you'll be proud and will guarantee you a nice mark. In addition, you can observe various topics for your research paper on the website! Things You Should Know About Psychology Essay Topics List If you're a student of psychology, you'd be asked to pick a specific topic for the last research paper. Psychology is a well-known science that has received a great deal of attention recently. 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You've got to keep lots of things in mind in addition to the pattern of write up and the styles that you're required to follow. When you're picking your topic, bear in mind that it's much simpler to write about something which you currently have interest ineven in case you don't know a good deal about it. You comprehend the fundamental approach for picking a very good topic, but nothing appears to be catching your interest. Top Psychology Essay Topics List Choices If you're going to compose a psychology paper, the very first thing you have to do is pick a great topic. At the exact same time that it limits the life span of a student within four walls of the house. You need to make a convincible point facing your readers so that even they cannot deny whatever you want to explain. If you select a concept which is too broad, you will remain on the surface without touching the most important thing in any way.

Essay about Witchcraft in Salem - 1406 Words

Witchcraft in Salem In the past, the word Salem has always been somewhat synonymous with the infamous witch trials. Thanks to works such as Arthur Miller’s â€Å"The Crucible†, many people find it hard not to envision a community torn apart by chaos, even though Miller’s play was not so much about the witch trials but instead a commentary on the rampant McCarthyism going on at the time he wrote it. Paul Boyer and Stephen Nissenbaum, however, see a very different picture when the Salem witch trials are mentioned. Rather than overlook the â€Å"ordinary† people living in the towns in which they write about (in the case of Salem Possessed, the town of Salem, Massachusetts), they instead take the instance of the witch trials of 1692 and†¦show more content†¦Scores of accusations of witchcraft followed, and soon the jails became full to the brink of overflowing. At the start of June, 1692, the first trial was held, resulting in a death sentence. B y the second trial, a system had been worked out that allowed five women to be tried in a single day, resulting in five death sentences. By the summer’s end, nearly twenty people had been put to death; by the year’s end, Governor William Phips, in an attempt to end the hysteria and fade the event into obscurity, both pardoned the remaining prisoners and dissolve the court that the trials took place in. Phips was perhaps hoping to distill any public interest in the trials by acting as if it had been just one big misunderstanding; this, of course, was a completely ineffective gesture. That is where the public’s knowledge of the Salem witch trials generally comes to an end. Boyer and Nissenbaum, however, have taken the liberty to tediously search through countless church archives, including tax assessments, community votes, and lists of loyal officials, allowing them to organize a more complete record of the events that occurred up to and during 1692. 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