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An Exploratory Analysis of the characteristic properties of Concrete Using a Civil Engineering Dataset.
The primary incentive of this research is to utilize various techniques from Data Analysis and Feature Engineering concepts in order to acquire more readable and useable data that can be applied to various Machine Learning models in the future to improve performance and add more convenience to the field of Civil Engineering.
├── LICENSE
├── Makefile <- Makefile with commands.
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── filtered <- Sanitized and filtered Data for better accessibility.
│ ├── model_ready <- The final, canonical data sets for modeling.
│ ├── processed <- Intermediate data that has been transformed.
│ └── raw <- The original, immutable data dump.
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├── features <- Scripts to construct a more readable and useable data.
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├── figures <- Generated graphics and figures to be used in reporting (includes IDE and Notebooks generated graphs).
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├── notebooks <- Additional script for Jupyter Notebooks for better visualization.
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
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├── setup.py <- makes project pip installable (pip install -e .) so src can be imported.
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├── visualization <- Create exploratory and results oriented visualizations.
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└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
• Python 3.11
• pandas 2.0.0
• missingno 0.5.2
• matplotlib 3.7.1
• seaborn 0.12.2
A myriad of visualization techniques have been applied while analyzing the acquired dataset. Examples include Bar charts, Histograms, Pearson Correlation Heatmaps, Covariance Matrix between features, and so forth. An additional Jupyter notebook has been added for better visualization of the codes as well as the figures.
