In developing countries like India, the plan of subsidising basic domestic commodities for poor families is an important part of meeting people’s basic needs. When a self-contained system for ration distribution is available, it benefits cardholders in a variety of ways. The E-Ration is the most convenient way to purchase ration items. Its goal is to offer ration products on the internet. E-Ration Shop is an automated system that distributes the exact amount of ration to cardholders based on the type of ration card and the number of family members, as well as keeping track of transactions in a database. As a result, this approach will reduce dealer communication, manual calculations, and time spent in stores. The cardholder can access the rationing system at any time during the month, with no need for human participation in the ration shop. Each transaction is automatically logged into the database by this system. Customers who purchase ration items online will have their information saved in an internet database, which will be viewable to higher-ranking officers as well as the shop owner. A fair price store (FPS) or ration shop is another name for a public distribution shop. It is a part of the Government of India's public distribution system, which provides subsidised rations to the needy in India. The Civil Supplies Corporation is a key government agency that oversees and distributes basic supplies to all inhabitants. Various products like as rice, sugar, and kerosene are supplied utilising a traditional ration store method in that system. Some of the drawbacks of the traditional ration shop system include: The user is unable to obtain an accurate quantity of material due to laborious measurements in the traditional technique. Because it is a direct process done by online ration shop keeper, this application uploads the data immediately to the server, confirming the data. Because it is a direct process done by online ration shop keeper, they cannot do anything in these transactions as they do in paper work.

2. WHAT IS DIABETICS
Diabetes is a chronic medical
condition characterized by high levels of
glucose (sugar) in the blood. This occurs when
the body does not produce enough insulin or is
unable to use it effectively. Insulin is a
hormone that is produced by the pancreas and
is responsible for regulating glucose levels in
the blood.

5. PROBLEM STATEMENT
Diabetes mellitus, sometimes known as diabetes, is a condition characterized by a rise
in blood glucose levels. If diabetes is left untreated and undiagnosed by a doctor, it can lead to
plenty of problems. The traditional way of diagnosis is for patients to go to a diagnostic facility,
consult their doctor, and relax for a day or more to get their results. Furthermore, they must
waste their money in futile anytime they need to obtain their diagnosis report. By using this
method, user can identify the level of diabetic such as normal, type 1 or type 2 and its remedies
such as food control, yoga treatment, and they get more valuable ideas from most relevant
YouTube channels. User can also get the test report which is useful when they need to consult the
doctor for further treatment.

7. CURRENT SYSTEM
Lot of work has been carried out to predict diabetic diseases using dataset. Different levels of
accuracy have been attained using various machine learning techniques. The accuracy of the
existing system is around 90%. The main idea behind the proposed system after reviewing the
existing papers was to create a diabetic prediction system based on the required inputs. The
current system employs the random forest technique, which takes a long time to train because
it mixes a large number of decision trees to decide the class. It also requires a lot of computer
power and resources because it creates several trees and combines their outcomes. When the
number of classes is increased, many analyzers don't perform well. The existing system predict
only the user has diabetic or not it does not provide the level of diabetic as well as remedies
such as food control, treatment relevant videos, yoga etc. Then it does not provide the history of
patient diabetic report.

9. PROPOSED SYSTEM
Classification is one of the most important decision making techniques in many real
world problem. In this work, the main objective is to classify the data as diabetic or non-diabetic
and improve the classification accuracy. For many classification problem, the higher number of
samples chosen but it doesn’t leads to higher classification accuracy. In many cases, the
performance of algorithm is high in the context of speed but the accuracy of data classification is
low. The main objective of our model is to achieve high accuracy. Classification accuracy can be
increase if we use much of the data set for training and few data sets for testing. This survey has
analyzed various classification techniques for classification of diabetic and non-diabetic data.
Thus, it is observed that techniques like Support Vector Machine, Logistic Regression, and
Artificial Neural Network are most suitable for implementing the Diabetes prediction system.

10. ARCHITECTURE OF PROPOSED SYSTEM

11. MODULE SPECIFICATION

12. ALGORITHM DESCRIPTION

13. KNN ALGORITHM

15. LOGISTIC REGRESSION

16. SUPPORT VECTOR MACHINE

17. RANDOM FOREST

18. NAVIE BAYER’S

19. DECISION TREE ALGORITHM

20. DATA SET DESCRIPTION

21. FUTURE SCOPE

22. CONCLUSION
Machine learning has the great ability to revolutionize the diabetes risk prediction with the help of
advanced computational methods and availability of large amount of epidemiological and genetic
diabetes risk dataset. Detection of diabetes in its early stages is the key for treatment. This work has
described a machine learning approach to predicting diabetes levels. The technique may also help
researchers to develop an accurate and effective tool that will reach at the table of clinicians to help
them make better decision about the disease status.

23. THANKYOU