Features assignments and learning from course: Data Modelling and Analaysis ECE 657A Explore the files for :
- EDA on wine , iris data
- feature engineering on Wine color and quality dtaa, iris data.
- Comparison and hyperparmeter tuning using gridserach of techniques such as SVM, KNN, Decision Trees, Random Forest, XGBoost.
- Manifold learning techniques like kernel PCA, Isomap,LLE,T-SNE etc.
- Numpy based implementation of PCA
- Analysis of PCA, LDA components.
7.Feature selection and extraction techniques and explanations.