-explainable_recommender_systems
Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.0%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: ZEDz318
- Language: Jupyter Notebook
- Default Branch: main
- Size: 8.16 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
Project Title: Revolutionizing Diabetes Prediction: Integrating Machine Learning with Explainable AI in Healthcare Analytics
This project was created using the EMRBots dataset to create an Explainable Recommender System capable recommending diagnosis to be looked out for, for patients based on their test results.
File Description
This project involves the use of data analysis, machine learning, and visualization techniques to explain the procedures used. The notebook is structured to guide through the process of data exploration, model building, and result visualization.
Installation
The project is implemented in a Jupyter Notebook. To run this notebook, ensure that you have Jupyter Notebook installed in your environment. You can install it via Anaconda or directly using PIP. The following major libraries are used:
Pandas: for data manipulation and analysis. NumPy: for numerical operations. Scikit-learn: for machine learning algorithms and model evaluation. Matplotlib & Seaborn: for data visualization. To install these libraries, you can use the following command: pip install pandas numpy scikit-learn matplotlib seaborn
Usage
To use this notebook: 1. Clone or download this repository to your local machine. 2. Ensure you have the above-mentioned libraries installed. 3. Open the Jupyter Notebook in your environment and run the cells sequentially to understand the workflow.
Structure
The notebook is divided into several sections: 1. Data Loading and Exploration: Loading the dataset and performing initial exploratory data analysis. 2. Feature Engineering: Preprocessing the data and preparing it for machine learning models. 3. Model Building: Implementing various machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, etc. 4. Model Evaluation: Evaluating the performance of the models using accuracy metrics. 5. Visualization: Visualizing the results and findings using Matplotlib and Seaborn.
Contributing
Contributions to this project are welcome. Please ensure to update tests as appropriate.
License MIT License
Owner
- Login: ZEDz318
- Kind: user
- Repositories: 1
- Profile: https://github.com/ZEDz318
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Asamoah Bamfo " given-names: "Israel Kenneth" - family-names: "Zeidan" given-names: "Daniel" title: "-explainable_recommender_systems" version: 0.1.0 doi: 10.5281/zenodo.10496075 date-released: 2024-01-12 url: "https://doi.org/10.5281/zenodo.10496075"
GitHub Events
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- Push event: 1
Last Year
- Push event: 1