https://github.com/nirmalyabag20/diabetes-prediction-using-machine-learning

This project focuses on predicting diabetes using machine learning algorithms based on health metrics like glucose levels, blood pressure, and BMI. By comparing different models, the goal is to identify the most accurate approach for early diabetes detection, showcasing the potential of machine learning in healthcare.
https://github.com/nirmalyabag20/diabetes-prediction-using-machine-learning

decision-tree-classifier jupyter-notebook kneighborsclassifier logistic-regression matplotlib numpy pandas python random-forest-classifier scikit-learn seaborn svc

Last synced: 3 days ago
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This project focuses on predicting diabetes using machine learning algorithms based on health metrics like glucose levels, blood pressure, and BMI. By comparing different models, the goal is to identify the most accurate approach for early diabetes detection, showcasing the potential of machine learning in healthcare.

• Host: GitHub
• URL: https://github.com/nirmalyabag20/diabetes-prediction-using-machine-learning
• Owner: nirmalyabag20
• Created: 2024-09-15T15:07:55.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-09-15T15:30:47.000Z (3 months ago)
• Last Synced: 2024-10-31T12:21:33.950Z (about 2 months ago)
• Topics: decision-tree-classifier, jupyter-notebook, kneighborsclassifier, logistic-regression, matplotlib, numpy, pandas, python, random-forest-classifier, scikit-learn, seaborn, svc
• Language: Jupyter Notebook
• Homepage:
• Size: 1.7 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
1.Project Overview

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This project focuses on predicting whether a person has diabetes based on medical diagnostic measurements. The goal is to build a machine learning model that can classify patients as diabetic or non-diabetic based on various health indicators.

2.Dataset

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The dataset used in this project contains medical information such as:

  . Pregnancies: Number of pregnancies

   

  . Glucose: Plasma glucose concentration

   

  .  BloodPressure: Diastolic blood pressure (mm Hg)

  . SkinThickness: Triceps skinfold thickness (mm)

   

  . Insulin: 2-Hour serum insulin (mu U/ml)

   

  . BMI: Body mass index (weight in kg/(height in m)^2)

   

  . DiabetesPedigreeFunction: A function that scores likelihood of diabetes based on family history

   

  . Age: Age (years)

The target variable is Outcome, where 1 indicates the presence of diabetes and 0 indicates absence.

3.Project Pipeline

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   . Data Preprocessing: Handle missing values, scaling, and splitting the dataset into training and testing sets.

   

   . Exploratory Data Analysis (EDA): Perform visualizations to understand the distribution of features and target classes.

   

   . Model Selection: Evaluate multiple machine learning models such as:

   

  (i). Logistic Regression

        

  (ii). Decision Tree

        

 (iii). Random Forest

        

  (iv). Support Vector Machine (SVM)

        

   . Model Evaluation: Assess model performance using metrics such as accuracy, precision, recall, and F1-score.

    

   . Final Model: Use the optimized model to predict diabetes.