In this problem you are required to apply various classification techniques on a benchmark

dataset: Diabetes Risk Prediction given as a CSV file in the attachment $($source: Kaggle$).$

This dataset is a collection of medical and demographic data from patients, along with their

diabetes status $($positive or negative$).$ The data includes features such as age, gender, polyuria,

polydipsia, sudden weight loss, weakness, polyphagia, visual blurring, itching, irritability,

delayed healing etc. This dataset can be used to build machine learning models to predict

diabetes in patients based on their medical history and demographic information.

In order to assess the performance of the classification techniques, you are going to apply a

simplified version of the k$-$fold cross validation method. The simplified k$-$fold cross validation

method proceeds in this way:

$$ Shuffle the dataset

$$ Divide the dataset into k equal partitions

$$ For each of the partitions:

o Apply the classification model using the union of $($k$-1)$ other partitions as

training set

o Test it on the current partition

o Analyze the performance $($precision$,$ recall, accuracy$)$ for this case

$$ Generate the overall performance report, taking the averages of the found results.

$2$

Apply a $5-$fold cross validation methodology, as described above, for all the classification

techniques that we have studied in our course:

i$.$Decision trees

ii$.$KNN

iii.Support Vector Machines

iv$.$Logistic Regression

v$.$Na$$ve Bayes

For each classification technique that is applied, the overall performance report should be

printed. Briefly compare the results.