- Course: Featurization, Model Selection & Tuning.
- Skills and Tools: Python, EDA, Regression, Feature Engineering, Linear Regression, Ridge Regressor, Lasso Regressor, Principal Component Analysis, KMeans Clustering, Decision Tree Regressor, XGBoost Regressor, Random Forest Regressor, Gradient Boosting Regressor, Bagging Regressor, K-Neighbors Regressor, Support Vector Machine Regressor.
This project involved feature exploration and selection to predict the strength of high-performance concrete. Used Regression models like Decision tree regressors to find out the most important features and predict the strength. Cross-validation techniques and Grid search were used to tune the parameters for best model performance.