Reference no: EM133696117
Assessment Details
Introduction
This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to improve student collaborative skills in a team environment and to grve students experience in constructing a range of documents as deliverables form different stages of the Intelligent Systems for Analytics.
Task
This assignment is to be completed in team; of 3 or 0 students. You should begin by submitting at the end of week 7) the signed group participation form provided In the Moodle. This form needs to be completed and signed by all group members. Once submitted, the teams will remain unchanged, and no member additions or deletions will be allowed unless by approval of your subject coordinator. Any person not part of a group by the end of week 7 will be asugned randomly to a group by your lecturer.
Question: The Diabetes prediction dataset comprises medical and demographic records of patients, alongside their diabetes status (positive or negative). It encompasses diverse attributes such as age, gender, body mass index (PAO, hypertension, heart disease, smoking history, HBA1c level, and blood glucose level. This dataset facilitates the creation of machine learning models aimed at forecasting diabetes occurrence based on patients' medical backgrounds and demographic particulars.
Your goal is to use different classifiers to build a training model based on training data points and then test its performance on test data points.
Students must use the following classifiers. The selection of the classifiers depends upon the members of the group. e g. if the group has four members, then they will use the four classifiers from the following five classifiers.
1. Neural network
2. Support vector machine
3. Nearest Neighbour algorithm
4. Decision tree
5. Naive Bayes
The group must prepare a report which Include the followings.
1. Explain the process of building each classifier using the training dataset (add the screenshots),
2. Create the confusion matrix based on training/ testing
3. Explain how you evaluated the classifier.
4. Predict the category of the values in table used for Testing set.
5. Compare the results between the different classifiers and discuss which one is the best and why.