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diabetes-detector-ml-model's Introduction

Predicting the Chances of having Diabetes using Machine Learning Algorithms.

Diabetes :

It is a condition that impairs the body’s ability to process blood glucose, otherwise known as blood sugar. Without ongoing, careful management, diabetes can lead to a buildup of sugars in the blood, which can increase the risk of dangerous complications, including Stroke and Heart Disease.

3 Major Types of Diabetes:: Type 1 diabetes: This type occurs when the body fails to produce insulin. People with type I diabetes are insulin-dependent, which means they must take artificial insulin daily to stay alive.

Type 2 diabetes: This affects the way the body uses insulin. While the body still makes insulin, unlike in type I, the cells in the body do not respond to it as effectively as they once did. This is the most common type of diabetes and it has strong links with obesity.

Gestational diabetes: This type occurs in women during pregnancy when the body can become less sensitive to insulin. Gestational diabetes does not occur in all women and usually resolves after giving birth.

_Things to do and to take care of :

  • Eating a Balanced Diet.
  • Avoiding High sugar foods.
  • By not drinking excessive amount of Alcohol.
  • Daily exercise is Mandatory and consistency is appreciated always.
  • Constantly Monitoring Oneself.

iabetes in women

What i have done in this project -

  • Collected the data and organized it to form a meaningful dataset.
  • Checked for null values and took care of it.
  • Observed the data to form meaningful insights.

  • Did Exploratory Data Analysis on the dataset.
  • Used correlations to form a heatmap.
  • Visualizations were made by using Matplotlib and Seaborn Libraries..

And then I made my model for the Prediction :

  • Did Data Preprocessing.
  • Did Train-Test split !

Trained my Model using :

Random Forest Classifier

  • Predicted for the data
  • Finded Accuracy score
  • Plotted Confusion Matrix
  • And at last, Classification report.

Logistic Regression

  • Predicted for the data
  • Finded Accuracy score
  • Plotted Confusion Matrix
  • And at last, the Classification report.

Support Vector Machine

  • Predicted for the data
  • Finded Accuracy score

Using Gradient Boosting Classifier

  • Predicted for the data
  • Finded Accuracy score
  • Plotted Confusion Matrix
  • And at last, Classification report. !
THE FINAL LINE -
With Around 80percent accuracy, my trained model can predict whether the given individual is having diabetes or not !

diabetes-detector-ml-model's People

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