In this project, we will predict the concrete compressive strength using regression techniques. This is a measure of the ability of concrete to withstand compression, and is a nonlinear relationship of both the age of the concrete and the ingredients used to create the concrete. This dataset comes directly from Kaggle.

This project is implemented using Python, Jupyter Notebooks, and associated tools from Data Science, most importantly scikit-learn. This library makes implementations of many common machine learning algorithms quite simple and allows for easy parameter tuning to find the best models.

The data is given with the following features:

• Cement (component 1) -- quantitative -- kg in a m3 mixture -- Input Variable
• Blast Furnace Slag (component 2) -- quantitative -- kg in a m3 mixture -- Input Variable
• Fly Ash (component 3) -- quantitative -- kg in a m3 mixture -- Input Variable
• Water (component 4) -- quantitative -- kg in a m3 mixture -- Input Variable
• Superplasticizer (component 5) -- quantitative -- kg in a m3 mixture -- Input Variable
• Coarse Aggregate (component 6) -- quantitative -- kg in a m3 mixture -- Input Variable
• Fine Aggregate (component 7) -- quantitative -- kg in a m3 mixture -- Input Variable
• Age -- quantitative -- Day (1~365) -- Input Variable

The data has the following as an output variable:

• Concrete compressive strength -- quantitative -- MPa -- Output Variable

• The main notebook file is the place to view a technical analysis and visualizations.
• The data folder contains the dataset as a CSV file.