https://github.com/r-mahesh45/diabitic-ml-model
Using a sample of diabitic data sets, I created a prediction model. To prepare and clean the data, I used pandas and the matplot library. Here are a few specifics from my project description for the diabitic model.
https://github.com/r-mahesh45/diabitic-ml-model
feature-engineering feature-extraction svm svm-classifier
Last synced: 6 months ago
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Using a sample of diabitic data sets, I created a prediction model. To prepare and clean the data, I used pandas and the matplot library. Here are a few specifics from my project description for the diabitic model.
- Host: GitHub
- URL: https://github.com/r-mahesh45/diabitic-ml-model
- Owner: R-Mahesh45
- Created: 2024-01-30T20:24:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-04T12:04:07.000Z (9 months ago)
- Last Synced: 2025-01-30T07:16:12.263Z (8 months ago)
- Topics: feature-engineering, feature-extraction, svm, svm-classifier
- Language: Python
- Homepage:
- Size: 20.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Diabetic Prediction Model
## Project Overview
This project involves the development of a **Diabetes Prediction Model** using a sample diabetic dataset. The goal is to predict diabetic outcomes based on various health indicators. The model employs **Support Vector Machine (SVM)** for classification and utilizes **Pandas** for data preprocessing and cleaning. Insights from the data are visualized using **Matplotlib**, which enhances the understanding of patterns and trends.
## Objective
- Develop a model that can predict the likelihood of a person having diabetes.
- Improve the accuracy of predictions and decision-making for healthcare professionals.
## Methodology
- **Data Collection:** A sample diabetic dataset was used for model training and evaluation.
- **Data Preprocessing:**
- Data was cleaned and prepared using **Pandas**.
- Missing values, duplicates, and irrelevant columns were handled to make the data ready for analysis.
- **Model Building:**
- **Support Vector Machine (SVM)** was used as the classification model.
- The model was trained on the preprocessed dataset and tested for accuracy.
- **Visualization:**
- **Matplotlib** was used for visualizing key insights from the data, including distributions and correlations.
## Impact
- The model provides improved accuracy in predicting diabetic outcomes.
- It can serve as a tool for healthcare professionals to enhance decision-making and identify patients at risk for diabetes.
## Technologies Used
- **Python** (Core programming language)
- **Pandas** (Data preprocessing and cleaning)
- **Matplotlib** (Data visualization)
- **Scikit-learn** (SVM model and other ML utilities)
## How to Run the Project
1. Clone the repository:
```bash
git clone https://github.com/your-username/diabetic-prediction-model.git
```
2. Navigate to the project directory:
```bash
cd diabetic-prediction-model
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Python script for training and testing the model:
```bash
python diabetes_prediction_model.py
```
## File Structure
- **diabetes_prediction_model.py**: Main Python script for model training and testing.
- **requirements.txt**: List of required Python packages.
- **data/diabetic_data.csv**: Sample dataset used for model training and prediction.
- **visualizations/**: Folder containing visualizations and graphs created during the project.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.