Merge pull request #1358 from LitZeus/add-diabetes-detection

Added Multimodal Diabetes Detection using 3 ensemble models

Author: sanjay-kv

Detection Models/MultiModel-Diabetes-Detection/README.md

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+# Multimodal Diabetes Detection Model- Using 3 Ensemble Models
+
+## Table of Contents
+- [Overview](#overview)
+- [Use Case](#use-case)
+- [Benefits](#benefits)
+- [Dataset](#dataset)
+- [Models Used](#models-used)
+- [Usage](#usage)
+- [Model Performance Metrics](#model-performance-metrics)
+- [Future Work](#future-work)
+- [Contribution Guidelines](#contribution-guidelines)
+- [Contact Information](#contact-information)
+
+## Overview
+
+This project aims to develop a diabetes detection system using the Pima Indians Diabetes dataset. It leverages three ensemble models: Stacking, Soft Voting, and Hard-Soft Voting to improve the accuracy and reliability of predictions.
+
+## Use Case
+
+The implemented feature enhances the project by:
+- Improving predictive accuracy for diabetes detection.
+- Providing more reliable and robust predictions.
+- Facilitating better decision-making in healthcare applications.
+
+## Benefits
+
+The benefits of this feature include:
+- Enhanced model robustness through ensemble learning.
+- Increased reliability in diabetes detection.
+- Contribution to improved patient outcomes in healthcare applications.
+- A valuable resource for the data science and machine learning community.
+
+## Dataset
+
+The project utilizes the Pima Indians Diabetes dataset from Kaggle. Below are the paths to the dataset files:
+
+- `kaggle/input/pima-indians-diabetes-database/diabetes.csv`
+- `kaggle/input/pimaindiansdiabetesdata/pima-indians-diabetes.data.csv`
+- `kaggle/input/diabetes-data-set/diabetes.csv`
+- `kaggle/input/pima-diabetes/pimaindians-diabetes.data.csv`
+- `kaggle/input/pimaindiansdiabetescsv/pima-indians-diabetes.csv`
+
+## Models Used
+
+1. **Stacking Model:** Combines multiple classifiers with a meta-learner for final predictions.
+2. **Soft Voting Model:** Averages the predicted probabilities from different models.
+3. **Hard-Soft Voting Model:** Integrates both hard voting (majority class) and soft voting (probability-based).
+
+## Usage
+
+1. Open the Jupyter notebook provided in this project.
+2. Load the dataset.
+3. Preprocess the data as needed.
+4. Train the ensemble models.
+5. Evaluate the model performance.
+
+## Model Performance Metrics
+
+The models are evaluated using the following metrics:
+- Accuracy
+- Precision
+- Recall
+- F1 Score
+
+## Future Work
+
+- Explore additional features for better model performance.
+- Implement more advanced ensemble techniques.
+- Conduct a comparative analysis with other machine learning algorithms.
+
+## Contribution Guidelines
+
+Contributions are welcome! Please follow these steps to contribute:
+1. Fork the repository.
+2. Create a new branch.
+3. Make your changes.
+4. Submit a pull request.
+
+## Contact Information
+
+For questions or feedback, please reach out to [email protected] 
+