{"id":24520427,"url":"https://github.com/maryem-jlassi/prediction","last_synced_at":"2026-05-07T01:04:52.291Z","repository":{"id":272988442,"uuid":"918372045","full_name":"Maryem-Jlassi/prediction","owner":"Maryem-Jlassi","description":"In my academic machine learning module, I collaborated with five colleagues to analyze a restaurant and health inspection dataset, applying various machine learning methods to extract insights and make predictions. 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The app allows users to interact with various predictive models related to food safety, employee compliance, and restaurant risk assessment within the restaurant industry in Los Angeles.\n## **Features**\nPredictive Models:\n\n -Food Quality Score Prediction\n\n -Employee Compliance Rate Prediction\n\n -Restaurant Risk Level Prediction\n\nBusiness Intelligence: Display of integrated Power BI dashboards.\n\nData Insights: Detailed analytics on restaurant inspections and food safety, including map visualizations.\n\n## **Technologies Used**\nStreamlit: Framework to build the interactive web app.\n\nMachine Learning Libraries:\n\nscikit-learn for data preprocessing, feature selection, and evaluation.\n\nXGBoost for predictive models.\n\n\njoblib for saving/loading models.\n\nFolium: For displaying interactive maps with geographic data.\n\nPower BI: Integrated for business analytics dashboards.\n## **Installation**\n**Prerequisites**\nBefore running the app, ensure you have the following libraries installed:\n\nPython 3.x\n\nStreamlit\n\nscikit-learn\n\nXGBoost\n\npandas\n\nnumpy\n\nmatplotlib\n\njoblib\n\nfolium\n\nTo install the required libraries, you can use the following command:\n\npip install streamlit scikit-learn xgboost pandas numpy matplotlib joblib folium\n\n**Clone the Repository**\ngit clone https://github.com/Maryem-Jlassi/prediction.git\n\ncd prediction\n\n**Running the App**\n\nTo run the app, navigate to the directory where the repository is located and execute the following command:\n\nstreamlit run project.py\n\nThis will launch the app in your default web browser.\n\n\n## **App Pages**\n\n-Main Page: Provides an overview of the platform, featuring links to predictive analytics, business intelligence, and data insights.\n\n-Predictive Models Page: Choose between three predictive models related to food quality, employee compliance, and restaurant risk level.\n\n-Power BI Page: Embedded Power BI analytics dashboard for deeper insights into the restaurant inspection data.\n\n-Data Insights Page: Displays key data insights and a map showing restaurant inspection locations.\n\n## **Contributing**\n\nFeel free to fork this project, open issues, and submit pull requests. Contributions are always welcome!\n\n## **License**\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaryem-jlassi%2Fprediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmaryem-jlassi%2Fprediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmaryem-jlassi%2Fprediction/lists"}