Concrete Comprehensive Strength Regression

Overview

This project applies machine learning techniques to predict the compressive strength of concrete based on its material composition. Using regression models, the goal is to analyze how different components influence the final strength of concrete and improve predictive accuracy.

Dataset

The dataset consists of various concrete mix compositions, including:

• Cement
• Blast Furnace Slag
• Fly Ash
• Water
• Superplasticizer
• Coarse Aggregate
• Fine Aggregate
• Age (days)
• Compressive Strength

Technologies Used

• Python (Jupyter Notebooks)
• Pandas & NumPy (Data preprocessing and manipulation)
• Matplotlib & Seaborn (Data visualization)
• Scikit-Learn (Machine learning models & evaluation)

Methodology

1. Data Preprocessing
   • Handled missing values, outliers, and feature scaling
   • Exploratory Data Analysis (EDA) for insights and correlations
2. Model Training & Evaluation
   • Implemented multiple regression models (Linear Regression, Decision Trees, Random Forest, etc.)
   • Compared model performances using metrics such as R², RMSE, and MAE
3. Hyperparameter Tuning
   • Optimized the best-performing model for improved accuracy

Results

• Identified key features impacting concrete strength
• Achieved a high-performing predictive model for compressive strength estimation

Future Improvements

• Experiment with deep learning models for better predictions
• Feature engineering to enhance dataset quality
• Deployment as a web application for real-time predictions