Concrete Compressive Strength Prediction

This notebook explores the Concrete Compressive Strength dataset from the UCI Machine Learning Repository. The goal is to build a regression model to predict the compressive strength of concrete based on its composition and age.

Dataset

The dataset contains 1030 instances and 9 attributes (8 input variables and 1 output variable). The input variables are:

• Cement (kg in a m3 mixture)
• Blast Furnace Slag (kg in a m3 mixture)
• Fly Ash (kg in a m3 mixture)
• Water (kg in a m3 mixture)
• Superplasticizer (kg in a m3 mixture)
• Coarse Aggregate (kg in a m3 mixture)
• Fine Aggregate (kg in a m3 mixture)
• Age (day)

The output variable is:

• Concrete compressive strength (MPa)

Exploratory Data Analysis

The notebook includes an exploratory data analysis section to understand the dataset characteristics, visualize the distribution of features and the target variable, and identify potential outliers.

Modeling

The goal is to apply regression techniques to predict the concrete compressive strength. The notebook will cover steps like:

1. Data preprocessing (handling outliers, scaling)
2. Model selection and training
3. Model evaluation
4. Hyperparameter tuning

How to run the notebook

1. Clone the repository.
2. Install the required libraries: pip install ucimlrepo pandas seaborn matplotlib numpy
3. Run the Jupyter notebook.

Acknowledgements

• UCI Machine Learning Repository for providing the dataset.
• The creators of the dataset: I-Cheng Yeh