https://github.com/elfgk/diabetes-data-analysis

diabetes data analysis
https://github.com/elfgk/diabetes-data-analysis

analysis data-analysis diabetes-data-analysis eda jupiter-notebook

Last synced: 6 days ago
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diabetes data analysis

• Host: GitHub
• URL: https://github.com/elfgk/diabetes-data-analysis
• Owner: elfgk
• License: apache-2.0
• Created: 2024-12-22T23:36:13.000Z (19 days ago)
• Default Branch: main
• Last Pushed: 2024-12-23T00:14:49.000Z (19 days ago)
• Last Synced: 2024-12-23T00:29:12.307Z (19 days ago)
• Topics: analysis, data-analysis, diabetes-data-analysis, eda, jupiter-notebook
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/elfgkk/diyabet
• Size: 0 Bytes
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Diabetes Data Analysis

This project aims to analyze diabetes data and develop machine learning models for diabetes diagnosis. The analysis was performed using 768 samples and 8 features. Each feature is used to predict whether a person has diabetes or not.

Dataset: https://www.kaggle.com/datasets/shantanudhakadd/diabetes-dataset-for-beginners

## Project Overview

1. **Dataset Overview:**

   - The dataset includes various features such as age, body mass index (BMI), glucose level, blood pressure, etc.

2. **Data Cleaning:**

   - Missing values handling, outlier detection, and normalization were performed.

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

   - Visualizations and basic statistical analyses were conducted to understand the data better.

4. **Modeling:**

   - Various machine learning models (e.g., logistic regression, decision trees) were applied for diabetes diagnosis.

5. **Model Evaluation:**

   - Models were evaluated using accuracy, precision, recall, F1 score, and other metrics.

## Libraries Used

- `pandas`: For data manipulation and analysis.

- `numpy`: For numerical computations.

- `matplotlib` and `seaborn`: For data visualizations.

- `sklearn`: For machine learning models and evaluation.

## Getting Started

To get started with this project, follow these steps:

1. Clone or download the repository:

   ```bash

   git clone https://github.com/username/repository_name.git

   ```

2. Install the required Python libraries:

3. Open the diabetes_analysis.ipynb Jupyter notebook and follow the steps.

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