The quality of concrete is determined by its compressive strength, which is measured using a conventional crushing test on a concrete cylinder. The strength of the concrete is also a vital aspect in achieving the requisite longevity. It will take 28 days to test strength, which is a long period. So, what will we do now? We can save a lot of time and effort by using Data Science to estimate how much quantity of which raw material we need for acceptable compressive strength.
A possible solution would be to create a Machine Learning model which would predict the compressive strength of the concrete given the quantity of the ingredients.
Dataset available in kaggle: Link
Sources: Original Owner and Donor Prof. I-Cheng Yeh Department of Information Management Chung-Hua University, Hsin Chu, Taiwan 30067, R.O.C. e-mail:icyeh@chu.edu.tw TEL:886-3-5186511 Date Donated: August 3, 2007
Data Characteristics: The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory. Data is in raw form (not scaled). Summary Statistics: Number of instances (observations): 1030 Number of Attributes: 9 Attribute breakdown: 8 quantitative input variables, and 1 quantitative output variable Missing Attribute Values: None
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├── Images
├── ineuron-logo.png
├── src
├── model_creation.py
├── preprocessing.py
├── Concrete Compressive Strength Prediction.ipynb
├── Procfile
├── app.py
├── reports
├── concrete_data.csv
├── requirements.txt
├── templates
├── setup.sh
├── strength.pkl
└── README.md
- Programming language : Python
- IDE : Visual Studio Code
- Visualization : Matplotlib and Seaborn
- Deployment platform : Heroku
- Front end development : HTML/CSS
- Back end development : Streamlit
- Version control system : GitHub
Web App Link: https://compressive-strength-concrete.herokuapp.com
In this web app, we just need to enter the amount of ingredients and the model will give a prediction on the compressive strength of the concrete if made with those amount of ingredients.