Concrete Strength Prediction

• Project Overview

  • Predicts concrete's compressive strength.
  • Uses ensemble methods:
    • RandomForestRegressor.
    • XGBoostRegressor.
  • Combines predictions using a weighted average.

• File Structure

  • main.ipynb – Jupyter Notebook with code implementation.
  • LICENSE – Project license.
  • readme.md – Project documentation.

• Features

  • Data loading and preprocessing.
  • Train-test split of the dataset.
  • Model training with RandomForest and XGBoost.
  • Prediction evaluation using:
    • Mean Squared Error (MSE).
    • R2 Score.
  • Weighted average of predictions for final output.

• Requirements

  • Python 3.x
  • Libraries:
    • numpy
    • pandas
    • matplotlib
    • scikit-learn
    • xgboost

• Setup Instructions

  • Clone the repository.
  • Install required packages:
    • pip install numpy pandas matplotlib scikit-learn xgboost
  • Open main.ipynb using Jupyter Notebook or any compatible IDE.

• Usage

  • Run all cells in main.ipynb sequentially to:
    • Load the dataset.
    • Train the RandomForest and XGBoost models.
    • Compute predictions and evaluate model performance.
    • Combine predictions using the weighted average.

• License

  • Refer to the LICENSE file for license details.