Description and Project Overview This project will be based on a dataset obtained from the UCI Repository. The dataset consists of 1030 observations under 9 attributes. The attributes consist of 8 quantitative inputs and 1 quantitative output. The dataset does not contain any missing values. The dataset is focused on the compressive strength of a concrete. The attributes include factors that affect concrete strength such as cement, water, aggregate (coarse and fine), and fly ash etc… The objective of this project is trying to predict the concrete compressive strength based important predictors. The study will consist of evaluating the impact of different factors such as cement, water, age, fly ash, and or additives. We will evaluate the components that are highly correlated with concrete compressive strength and other components that are less influential and can be neglected through visualization or correlation matrix. In this study, we will use different machine learning techniques to predict the concrete compressive strength. Different modeling techniques will be used for the prediction. The modeling technique will include multiple linear regression, decision tree, and random forest, etc. A comparative analysis will be performed to identify the best model for our prediction in terms of accuracy. The best model will be helpful for civil engineers in choosing the appropriate concrete for bridges, houses construction.