Saurav Kar If you want to use just MATLAB, you can collect data on the mix proportions and concrete properties. You should have a dataset with observations of different concrete mixes and their corresponding properties. Then you should prepare your data by cleaning it, removing outliers, and handling missing values if necessary. Also, you need to decide which concrete ingredients' proportions are your independent variables (e.g., cement, fly ash, water, or aggregates) and which concrete properties you want to predict as dependent variables (e.g., compressive strength, workability, durability). And after that you can choose a fit regression models in Matlab. There are a quite range of functions for regression analysis. You can use fitlm for linear regression, fitglm for generalized linear models, or other specialized functions depending on the nature of your data. If we assume that X is your independent variable (e.g., fly ash proportion) and Y is your dependent variable (e.g., compressive strength) you will have: mdl = fitlm(X, Y). Do not forget to assess the statistical significance of your regression models using relevant metrics like R-squared, or p-values.

For python the first steps are something similar just from sklearn.linear_model import LinearRegression # Assuming X and Y are your independent and dependent variables model = LinearRegression() model.fit(X, Y)