{"id":25407937,"url":"https://github.com/prathicashettym/concretestrengthprediction","last_synced_at":"2026-04-09T20:33:15.681Z","repository":{"id":273785587,"uuid":"920856992","full_name":"PrathicaShettyM/ConcreteStrengthPrediction","owner":"PrathicaShettyM","description":"Concrete Strength Prediction ML Model using XGBoost Algorithm","archived":false,"fork":false,"pushed_at":"2025-01-23T06:57:48.000Z","size":961,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-13T11:58:12.387Z","etag":null,"topics":["flask","python","streamlit"],"latest_commit_sha":null,"homepage":"https://prathicashettym-concretestrengthprediction-streamlit-app-7x861z.streamlit.app/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PrathicaShettyM.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-22T22:18:53.000Z","updated_at":"2025-01-31T16:27:18.000Z","dependencies_parsed_at":"2025-01-23T00:35:30.995Z","dependency_job_id":null,"html_url":"https://github.com/PrathicaShettyM/ConcreteStrengthPrediction","commit_stats":null,"previous_names":["prathicashettym/concretestrengthprediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PrathicaShettyM%2FConcreteStrengthPrediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PrathicaShettyM%2FConcreteStrengthPrediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PrathicaShettyM%2FConcreteStrengthPrediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PrathicaShettyM%2FConcreteStrengthPrediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PrathicaShettyM","download_url":"https://codeload.github.com/PrathicaShettyM/ConcreteStrengthPrediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248710435,"owners_count":21149188,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["flask","python","streamlit"],"created_at":"2025-02-16T07:19:26.054Z","updated_at":"2026-04-09T20:33:15.620Z","avatar_url":"https://github.com/PrathicaShettyM.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Concrete Strength Prediction\n\n![Concrete](https://img.icons8.com/ios-filled/50/000000/concrete.png)\n\n## Project Overview\nThis project leverages machine learning to predict the compressive strength of concrete based on its mix ingredients and age. The model is deployed using a Flask server and a Streamlit client for user interaction. Users can input the mix proportions and get the predicted concrete strength.\n\n## Features\n- 🔧 **Machine Learning Model**: Utilizes XGBoost for accurate strength predictions.\n- 🌐 **Flask Server**: Handles backend processes and model inference.\n- 📊 **Streamlit Client**: Provides a user-friendly interface for input and prediction visualization.\n- 🔍 **Feature Engineering \u0026 EDA**: Comprehensive Exploratory Data Analysis and feature engineering for improved model performance.\n\n## Table of Contents\n1. [Installation](#installation)\n2. [Running the Application](#running-the-application)\n3. [Input Fields](#input-fields)\n4. [Usage](#usage)\n5. [Contributing](#contributing)\n6. [License](#license)\n\n## Installation\n\n### Create Virtual Environment (Windows)\n1. Install virtual environment using bash:\n    ```sh\n    py -m venv myvenv\n    ```\n2. Activate virtual environment:\n    ```sh\n    myvenv\\Scripts\\activate\n    ```\n3. Install pip:\n    ```sh\n    py -m ensurepip --upgrade\n    ```\n4. Install `ipykernel` for Jupyter Notebook:\n    ```sh\n    pip install ipykernel\n    ```\n\n## Running the Application\n1. Install all the packages in the terminal after activating the virtual environment:\n    ```sh\n    pip install flask streamlit requests scikit-learn xgboost joblib\n    ```\n2. Run the Flask server:\n    ```sh\n    python flask_app.py\n    ```\n3. Run the Streamlit frontend:\n    ```sh\n    streamlit run streamlit_app.py\n    ```\n\n## Input Fields\n\nThe application takes the following input fields for user data:\n\n![Input](https://img.icons8.com/ios-filled/50/000000/submit-for-approval.png)\n- **Cement** (kg/m³)\n\n- **Blast Furnace Slag**  (kg/m³)\n\n- **Fly Ash**  (kg/m³)\n\n- **Water**  (kg/m³)\n\n- **Superplasticizer**  (kg/m³)\n\n- **Coarse Aggregate**  (kg/m³)\n\n- **Fine Aggregate**  (kg/m³)\n\n- **Age**  (kg/m³)\n\n## Usage\n1. Enter the mix proportions and age of the concrete.\n2. Click on the \"Predict\" button.\n3. The model will predict and display the compressive strength of the concrete.\n\n## Contributing\nContributions are welcome! Please open an issue or submit a pull request for any improvements or additions.\n\n## License\nThis project is licensed under the MIT License.\n\n---","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprathicashettym%2Fconcretestrengthprediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprathicashettym%2Fconcretestrengthprediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprathicashettym%2Fconcretestrengthprediction/lists"}