Workshop Highlights:

• Purpose:
  • Equip participants with practical experience in data preparation, analysis, and machine learning modeling for predicting material strength in engineering contexts.
  • Emphasize the significance of understanding factors that influence compressive strength in cement, a core material in construction.

Project Outline and Methodology:

1. Data Preparation and Exploration:
   • Import essential libraries: Pandas, NumPy, Matplotlib, and Seaborn.
   • Perform descriptive statistics (mean, median, mode, standard deviation) to assess feature distribution and variability.
2. Feature Analysis:
   • Create a correlation heatmap using Matplotlib and Seaborn to identify relationships between features and their impact on cement strength.
3. Data Scaling:
   • Normalize dataset values between -1 and 1 to standardize features, addressing the disparity in magnitude among features (e.g., water vs. plasticizers).
4. Data Splitting:
   • Perform a train-test split to allow the model to learn relationships in the training data and test its predictive accuracy on unseen data.
5. Model Training and Evaluation:
   • Use Linear Regression to train the model on the training data.
   • Predict and evaluate model accuracy against actual values, with an expected accuracy of around 82%.

Outcomes:

• Insightful Analysis: Gain a clear understanding of influential factors impacting cement compressive strength.
• Model Accuracy: Develop a functional linear regression model with approximately 62% accuracy, forming a foundation for model refinement and future improvements.