Diabetes_Prediction
Diabetes is a chronic condition in which the body develops a resistance to insulin, a hormone which converts food into glucose. Diabetes affect many people worldwide and is normally divided into Type 1 and Type 2 diabetes. Both have different characteristics. This article intends to analyze and create a model on the PIMA Indian Diabetes dataset to predict if a particular observation is at a risk of developing diabetes, given the independent factors. This article contains the methods followed to create a suitable model, including EDA along with the model.
About Dataset
The dataset can be found on the Kaggle website. This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases and can be used to predict whether a patient has diabetes based on certain diagnostic factors. Starting off, I use Python 3.3 to implement the model. It is important to perform some basic analysis to get an overall idea of the dataset.
Task
1)Exploratory Data Analysis (EDA)
2)Checking for Multi collinearity
3)Treating Outliers and Non-Normality
4)Splitting the dataset into Training and Test data
5)Model Fitting
Conclusion
Based on the feature importance
1)Glucose is the most important factor in determining the onset of diabetes followed by BMI and Age.
2)Other factors such as Diabetes Pedigree Function, Pregnancies, Blood Pressure, Skin Thickness and Insulin also contributes to the prediction.