/Analysis-to-MachineLearning

A comprehensive data-analysis, followed by model-building employing various algorithms and Hyperparameter-Optimization on the 'concrete' dataset

Primary LanguageJupyter Notebook

Analysis-to-MachineLearning

Concrete is the most important material in civil engineering. The concrete compressive strength, which is what our Target variable is, is a highly nonlinear function of age and ingredients. Data Notebook

Overview of what's been done in the notebook:

  • Univariate Analysis
  • Outliers identification and appropriate treatment
  • Bivariate Analysis
  • Cluster Analysis using K-Means Clustering
  • Data Preprocessing
  • Model-Building with Hyperparameter-optimization

Comparison of:

  • SVM
  • Linear Regression
  • Lasso
  • Random Forest (Ensemble)
  • Bagging with SVM (Ensemble)
  • Grading Boosting (Ensemble)

Data Source