https://github.com/prathicashettym/concretestrengthprediction
Concrete Strength Prediction ML Model using XGBoost Algorithm
https://github.com/prathicashettym/concretestrengthprediction
flask python streamlit
Last synced: 3 months ago
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Concrete Strength Prediction ML Model using XGBoost Algorithm
- Host: GitHub
- URL: https://github.com/prathicashettym/concretestrengthprediction
- Owner: PrathicaShettyM
- Created: 2025-01-22T22:18:53.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-23T06:57:48.000Z (over 1 year ago)
- Last Synced: 2025-04-13T11:58:12.387Z (about 1 year ago)
- Topics: flask, python, streamlit
- Language: Jupyter Notebook
- Homepage: https://prathicashettym-concretestrengthprediction-streamlit-app-7x861z.streamlit.app/
- Size: 938 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Concrete Strength Prediction
## Project Overview
This 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.
## Features
- 🔧 **Machine Learning Model**: Utilizes XGBoost for accurate strength predictions.
- 🌐 **Flask Server**: Handles backend processes and model inference.
- 📊 **Streamlit Client**: Provides a user-friendly interface for input and prediction visualization.
- 🔍 **Feature Engineering & EDA**: Comprehensive Exploratory Data Analysis and feature engineering for improved model performance.
## Table of Contents
1. [Installation](#installation)
2. [Running the Application](#running-the-application)
3. [Input Fields](#input-fields)
4. [Usage](#usage)
5. [Contributing](#contributing)
6. [License](#license)
## Installation
### Create Virtual Environment (Windows)
1. Install virtual environment using bash:
```sh
py -m venv myvenv
```
2. Activate virtual environment:
```sh
myvenv\Scripts\activate
```
3. Install pip:
```sh
py -m ensurepip --upgrade
```
4. Install `ipykernel` for Jupyter Notebook:
```sh
pip install ipykernel
```
## Running the Application
1. Install all the packages in the terminal after activating the virtual environment:
```sh
pip install flask streamlit requests scikit-learn xgboost joblib
```
2. Run the Flask server:
```sh
python flask_app.py
```
3. Run the Streamlit frontend:
```sh
streamlit run streamlit_app.py
```
## Input Fields
The application takes the following input fields for user data:
- **Cement** (kg/m³)
- **Blast Furnace Slag** (kg/m³)
- **Fly Ash** (kg/m³)
- **Water** (kg/m³)
- **Superplasticizer** (kg/m³)
- **Coarse Aggregate** (kg/m³)
- **Fine Aggregate** (kg/m³)
- **Age** (kg/m³)
## Usage
1. Enter the mix proportions and age of the concrete.
2. Click on the "Predict" button.
3. The model will predict and display the compressive strength of the concrete.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or additions.
## License
This project is licensed under the MIT License.
---