Individual Data Analytics Assignment in KNIME

Individual Data Analytics Assignment in KNIME
Total points: 100
Due Date: November 10, 202 – 23:00 pm
Late submission penalty: 5% per day
Problem Context
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney
Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has
diabetes, based on certain diagnostic measurements included in the dataset. Several constraints
were placed on the selection of these instances from a larger database. In particular, all patients
here are females at least 21 years old of Pima Indian heritage.
Dataset Content
The datasets consists of several medical predictor variables and one target variable, Outcome.
Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin
level, age, and so on.
Tasks:
1. Using KNIME platform Examine Summary Statistics (20 Points)
2. Build a Decision Tree Workflow in KNIME (20 Points)
3. Do the Classification Task on the dataset based on the Decision Tree you built in the
previous step (35 Points)
4. Evaluate the Performance of your Decision Tree Model by Generate a Confusion Matrix
and Determine Accuracy Rate (25 Points)
What to submit:
1. Summary Statistics of dataset (in a word document)
2. Confusion Matrix and its interpretation (in a word document)
3. KNIME Workflows of your Decision Tree model
Note: For the step by step, instruction refers to the material on the Moodle about Classification
using Decision Tree in KNIME.