GitHub / esvs2202 / Concrete-Compressive-Strength-Prediction
The aim of this project is to develop a solution using Data science and machine learning to predict the compressive strength of a concrete with respect to the its age and the quantity of ingredients used.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/esvs2202%2FConcrete-Compressive-Strength-Prediction
PURL: pkg:github/esvs2202/Concrete-Compressive-Strength-Prediction
Stars: 18
Forks: 9
Open issues: 0
License: gpl-3.0
Language: Jupyter Notebook
Size: 4.54 MB
Dependencies parsed at: Pending
Created at: almost 4 years ago
Updated at: 5 months ago
Pushed at: about 2 years ago
Last synced at: 4 months ago
Topics: anaconda, data-visualization, flask, gunicorn-web-server, heroku-deployment, html5, joblib, jupyter-notebook, machine-learning-algorithms, matplotlib-pyplot, numpy, pandas, pycharm-ide, python3, randomizedsearchcv, scikit-learn, seaborn, statsmodels, xgboost-regression