https://github.com/djdurga/predictive_analysis_in_diabetes

This project applies Logistic Regression to predict diabetes in patients using the Pima Indians Diabetes Dataset. It covers the full data science lifecycle — from data imputation and exploration to model training, evaluation, and insights.
https://github.com/djdurga/predictive_analysis_in_diabetes

matplotlib numpy pandas

Last synced: about 2 months ago
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This project applies Logistic Regression to predict diabetes in patients using the Pima Indians Diabetes Dataset. It covers the full data science lifecycle — from data imputation and exploration to model training, evaluation, and insights.

• Host: GitHub
• URL: https://github.com/djdurga/predictive_analysis_in_diabetes
• Owner: Djdurga
• Created: 2025-06-16T16:41:57.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-06-16T16:49:27.000Z (about 1 year ago)
• Last Synced: 2025-06-16T17:49:27.640Z (about 1 year ago)
• Topics: matplotlib, numpy, pandas
• Language: Jupyter Notebook
• Homepage:
• Size: 243 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 🩺 Predictive Analysis in Diabetes using Logistic Regression

## 📌 Project Overview

This project applies **Logistic Regression** to predict diabetes in patients using the **Pima Indians Diabetes Dataset**. It demonstrates the full data science workflow — from data imputation and EDA to model training, evaluation, and extracting insights.

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## 🎯 Objective

To build a binary classification model that predicts whether a patient has diabetes (`Outcome: 1`) or not (`Outcome: 0`) using key health indicators such as glucose levels, BMI, insulin levels, and age.

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## 🧪 Dataset Details

- **Source**: [Kaggle - Pima Indians Diabetes Dataset](https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database)

- **Total Records**: 768

- **Target Feature**: `Outcome` (0 = No Diabetes, 1 = Diabetes)

- **Attributes**:

  - Pregnancies  

  - Glucose  

  - BloodPressure  

  - SkinThickness  

  - Insulin  

  - BMI  

  - DiabetesPedigreeFunction  

  - Age  

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## 📈 Workflow Summary

1. **📥 Data Import & Exploration**

   - Load dataset using pandas

   - Check structure, shape, missing or zero values

2. **🧹 Data Cleaning**

   - Impute zero values in `Glucose`, `BloodPressure`, `Insulin`, `BMI`, etc.

3. **📊 Exploratory Data Analysis (EDA)**

   - Summary statistics

   - Correlation matrix

   - Visualizations with Seaborn & Matplotlib

4. **⚙️ Model Building**

   - Logistic Regression with Scikit-learn

   - Train-test split

5. **📉 Evaluation**

   - Accuracy Score

   - Confusion Matrix

   - Precision, Recall, F1-Score

   - ROC-AUC Curve

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## 🛠 Tools & Technologies

- Python

- Pandas, NumPy

- Matplotlib, Seaborn

- Scikit-learn

- Jupyter Notebook

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## 📂 Repository Structure