<ul><li>A project utilizing big data and machine learning aims to predict concrete compressive strength in 28 seconds, complementing traditional lab testing.</li><li>Data from Kaggle is used, incorporating features like water-to-cement ratio and weights of aggregates to enhance accuracy.</li><li>Visualizations like heatmaps and pair plots are used to assess correlations between input features and compressive strength.</li><li>Data is split into training, cross-validation, and test sets for model evaluation using SGD, XGBoost, and ANN algorithms.</li><li>Hyperparameter tuning is done for each model, with XGBoost showing an accuracy of 91% to 92% on cross-validation and test sets.</li><li>ANN, designed after biological neurons, performs well but with slightly lower accuracy compared to XGBoost at 86.8% on the test set.</li><li>An early stopping setting at 100 patience and 200 epochs is used to address overfitting in the ANN model.</li><li>XGBoost algorithm demonstrated superior performance in this concrete compressive strength prediction project.</li><li>A web app deploying the ANN model for interactive use is provided for further exploration.</li></ul>

️ Predicting Concrete’s Compressive Strength with Machine Learning

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