v-parmar / diabetes-prediction-model

The objective of this project is to classify whether someone has diabetes or not. Dataset consists of several Medical Variables(Independent) and one Outcome Variable(Dependent). The independent variables in this data set are :-'Pregnancies', 'Glucose', 'Blood Pressure', 'Skin Thickness', 'Insulin', 'BMI', 'Diabetes Pedigree Function' and, 'Age'

Diabetes Prediction

What is Diabetes?

Diabetes is a chronic disease that occurs when the pancreas is no longer able to make insulin, or when the body cannot make good use of the insulin it produces. Learning how to use Machine Learning can help us predict Diabetes. Let’s get started!

About this project:-

• The objective of this project is to classify whether someone has diabetes or not.
• Dataset consists of several Medical Variables(Independent) and one Outcome Variable(Dependent)
• The independent variables in this data set are:-'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction' and, 'Age'
• The outcome indicates whether a person has diabetes or not. and it also shows which algorithm predicts the result with accuracy.

About the Dataset

• Pregnancies:- Number of times a woman has been pregnant
• Glucose:- Plasma Glucose concentration of 2 hours in an oral glucose tolerance test
• BloodPressure:- Diastolic Blood Pressure (mm hg)
• SkinThickness :- Triceps skin fold thickness(mm)
• Insulin :- 2 hour serum insulin(mu U/ml)
• BMI :- Body Mass Index ((weight in kg/height in m)^2)
• Age :- Age(years)
• DiabetesPedigreeFunction:-scores likelihood of diabetes based on family history)

Technology and Tools Used:

1. Python
2. Numpy for linear algebra
3. Pandas for data processing and CSV file I/O
4. Matplotlib to plot charts
5. Seaborn for data visualization
6. Sklearn for model building
7. Classification algorithms for prediction: KNN, Naive Bayes, SVM, Decision Tree, Random Forest, and Logistic Regression
8. tkinter for GUI
9. Jupyter Notebook and VS Code IDE