aks25eng / diabities-prediction

Diabities-prediction

Problem statement of Diabetes prediction via Machine Learning model

The objective of the dataset is to diagnostically predict if a patient has diabetes, established on definite diagnostic quantities incorporated in the dataset.

Content The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient had,

their BMI, their insulin level, their age, Blood Pressure, Glucose level and diebities pidgree function.

Dependencies imported / python libraries

sklearn , pandas , numpy , matplotlib , seaborn

Work flow -

1- Importing the dataset file ,

2- Data preprocessing to know the data we are working with ,

3- Data analysis using plots and graphs to understand the parameters ,

4- Spliting the data into training data and testing data ,

5- Model selection for prediction ( based on classification problem)

6- Fitting the data parameters into the model ,

7- Evaluating the performance of the model