Diabetes Prediction using Machine Learning

Abstract :

Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. Where we develop into the fascinating world of machine learning and its applications in healthcare. In today’s post, we will explore a specific area of interest – diabetes classification. Diabetes is a complex and prevalent condition that affects millions of people worldwide. With advancements in technology and data analysis, machine learning algorithms have the potential to revolutionize the way we diagnose and classify diabetes. Join us as we dive into the intricacies of this topic, exploring the various approaches and techniques used in diabetes classification using machine learning. Whether you’re a healthcare professional, a data scientist, or simply someone with a curious mind. The proposed system utilizing machine learning for diabetes classification.  Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results. Diabetes Prediction using Machine Learning

Diabetes Classification

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Diabetes Prediction using Machine Learning

Software Requirements: –

• Operating System :    Windows OS
• Front End,Back-End:    Jupyter ,Python 3.10 ,
• Tool                     :    Miniconda3-latest-Windows-x86_64, Python 3.10

Hardware Requirements: –

• System                : Dual Core
• Hard Disk           : 40 GB
• Monitor              : 15 VGA Colour
•  Mouse                : Logitech
• RAM                   :  2 GB

System Modules:

1. User Give the input as dataset link from website URL or local file dataset .
2. Reading the Data Understanding to get Dataset as Data Frame.
3. Calculating Pairplot of the all the features
4. Calculating Pearson correlation and feature selection help to analyze Only age and pregnancies shows a significant strong correlation from Diabetes data set.
5. Calculating Class distribution in Diabetes dataset.
6. Splitting the Data as two category as X and Y.
7. Calculate the Model Accuracy by using K-Nearest Neighbours
8. Calculate the Model Accuracy by using Logistic Regression
9. Calculate the Model Accuracy by using Decision Tree
10. Calculate the Model Accuracy by using Random Forest
11. Calculate the Model Accuracy by using Gradient Boosting
12. Calculate the Model Accuracy by using   Support Vector Machine
13. Calculate the Model Accuracy by using Neural Networks
14. Calculate the Model Accuracy by using XG Boost
15. Compare the all Model Accuracy Performance