Predictive Analytics on Diabetes Data using Machine Learning Techniques

Predictive Analytics on Diabetes Data using Machine Learning Techniques

₹5,500.00 ₹4,000.00
Product Code: Python - Machine Learning
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Product Description

Aim:


        To help doctors and practitioners in early prediction of diabetes using machine learning techniques.


Abstract:


         Diabetes caused due to increase in amount of sugar or glucose which is condensed into the blood Identifying process of diabetes is the glucose and sugar levels needs to be checked before and after meal, there are fluctuations before and after meal, this whole process of patient visiting a doctor is tiresome. But in Machine Learning algorithms helps us to solve this issue. The motive of this study and research is to make use of features and to predict the likelihood of the disease, Decision Tree, Random Forest, K Nearest Neighbours, Naive Bayes and Support Vector Machine are the algorithm that have been applied to detect and predict diabetes at an early stage. A dataset of a patient’s medical record is obtained and different machine learning algorithms are applied on the dataset. Performance and accuracy of the applied algorithms is discussed and compared. Comparison of the different machine learning techniques used in this study reveals which algorithm is best suited for prediction of diabetes.


Proposed System:


      In this paper, we are using machine learning algorithms to predict diabetes disease. The client on their first login has to register themselves on the Web Application. The web Application created by Django. Once the user logins into the system he gets all the access for predicting the diabetic and by  using the input such as Pregnancies, Glucose, Blood Pressure, Skin Thickness, BMI, insulin level and age based on their own. After submitting the inputs, it’s move on to the trained model for comparison. Already trained model were trained by machine learning algorithms. Algorithms used for training a dataset are K Nearest Neighbours (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF). Comparison of the different machine learning techniques used in this study reveals which algorithm is best suited for prediction of diabetes.






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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.