Concrete-Strength-Prediction

Problem Statement:

• In the project, the objective was to develop a machine learning model capable of accurately predicting the compressive strength of concrete based on its constituent materials.

Methodology:

• Utilized a regression-based machine learning approach to predict strength of concrete.
• Implemented data preprocessing techniques such as feature scaling and feature extraction.
• Explored various regression algorithms including linear regression, decision trees, knn, svr, random forests, adaptive boosting, gradient boosting and XG boost.

Results:

• The Gradient Boosting Regressor outperformed other algorithms, achieving a RMSE of 4.56 N/mm2 and accuracy of 92.5% on the test data.
• Identified important features influencing concrete strength through feature importance analysis.

Skills Demonstrated:

• Data preprocessing, data visualization, regression modeling, hyperparameter tuning, model evaluation.

Technologies Used:

• Python, pandas, scikit-learn, matplotlib, seaborn, Jupyter Notebook.