Data-modeling-and-Analysis-ECE657A

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.

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
A2 ECE 657A final submission - Jupyter Notebook.pdf		A2 ECE 657A final submission - Jupyter Notebook.pdf
Analyis and comparison on non linear dimesionslity reduction techniques .pdf		Analyis and comparison on non linear dimesionslity reduction techniques .pdf
Analysis of various ML algorithms on iris data.pdf		Analysis of various ML algorithms on iris data.pdf
EDA_feature_engineering_on_wine_color_quality_data.pdf		EDA_feature_engineering_on_wine_color_quality_data.pdf
Exploratory_analysis_on_iris_data.pdf		Exploratory_analysis_on_iris_data.pdf
Feature engineering on Wine color-quality and MNIST data .ipynb		Feature engineering on Wine color-quality and MNIST data .ipynb
Iris dataset EDA, fitting ML models and algorithm comparisons.pdf		Iris dataset EDA, fitting ML models and algorithm comparisons.pdf
KNN and SVM models.pdf		KNN and SVM models.pdf
PCA_LDA_SVD_based_PCA_ implementation.pdf		PCA_LDA_SVD_based_PCA_ implementation.pdf
README.md		README.md
iris_EDA_comparion_of_ML_algos_grid_search.ipynb		iris_EDA_comparion_of_ML_algos_grid_search.ipynb

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