This is a small presentation on my project , diabetes prediction using R language.The method used is knn(K nearest neighbour). it the basic Machine learning algorithm.

1. Presented by :
Tanuja Joshi
Deogiri Institute of Engineering and Management Studies.
Subject :
Data science with R
Topic :
Diabetes Prediction .

2. Contents :
• Introduction
• Dataset
• Method or algorithm used.
• Predicting values of k

3. Introduction :
• The project aims at a building a model using machine
learning techniques and automating the diabetes
prediction process .
• The algorithms are applied on the standard datasets
and ten are trained and depending on the training
and the accuracy of the algorithm output is given .

4. Dataset :
• The dataset used is the
“PimaIndiansDiabetes.csv”
• There are total 9 attributes in the dataset .
• Some are glucose, BMI, Age, insulin, and the
target variable whether +ve or –ve.

5. Method :
• The method used will b knn (k Nearest Neighbour)
• The accuracy of the foling method is proved to be
78%-82% .
• The prime imortance is given to the insulin and
glucose factors.

6. Values of K
• The values of K for huge dataset are ideally between
3-10.
• But for small datasets the ideal method for finding the value of K is the
trial and error method.
• This can be done automatically using thecaret package, which chooses a
value of k that minimize the cross-validation error.

8. Overview of dataset
1)Pregnancy(times)
2)plasma glucose
3)Diastolic B.P.
4)Tricep skin fold thickness.
5)insulin
6)BMI
7)Diabetes prediction Dataset
8)Age
9)Class Variable.

9. Dataset:

11. Thank you!!