HUFS-DAT/2024-2_Seminar
Nov 2024 – Nov 2024
1 Month active
Languages Used
Python
Technical Skills
Data PreprocessingData ScienceDimensionality ReductionImage ProcessingJupyter NotebookMachine Learning
imsewonyyy
PROFILE
Imsewonyyy
Developed an end-to-end PCA dimensionality reduction notebook for the HUFS-DAT/2024-2_Seminar repository, focusing on Fashion MNIST analysis. The solution involved loading the dataset, converting images to tensors, and normalizing data before applying Principal Component Analysis with 50, 30, and 2 components. Using Python and Jupyter Notebook, the implementation reported explained variance ratios and reconstruction errors for each dimensionality setting, supporting quantitative evaluation of feature reduction. The notebook also included 2D visualizations to facilitate interpretation of reduced representations. This work enhanced exploratory data analysis and streamlined feature engineering, improving reproducibility and onboarding for future contributors to the repository.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
790
Activity Months1
Your Network
9 peopleSame Organization
@hufs.ac.kr3
Work History
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered an end-to-end PCA dimensionality reduction notebook for Fashion MNIST analysis in HUFS-DAT/2024-2_Seminar. The notebook loads Fashion MNIST, performs tensor conversion and normalization, and runs PCA with 50, 30, and 2 components. It reports explained variance ratios and reconstruction error and includes a 2D visualization of the reduced representations. This work enhances exploratory data analysis, accelerates feature engineering decisions, and supports model selection with quantitative variance capture. The feature was committed as 4fb638db1d73a05ed14ee00741eb0989c6e54765 (Add files via upload).
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered an end-to-end PCA dimensionality reduction notebook for Fashion MNIST analysis in HUFS-DAT/2024-2_Seminar. The notebook loads Fashion MNIST, performs tensor conversion and normalization, and runs PCA with 50, 30, and 2 components. It reports explained variance ratios and reconstruction error and includes a 2D visualization of the reduced representations. This work enhances exploratory data analysis, accelerates feature engineering decisions, and supports model selection with quantitative variance capture. The feature was committed as 4fb638db1d73a05ed14ee00741eb0989c6e54765 (Add files via upload).
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data PreprocessingData ScienceDimensionality ReductionImage ProcessingJupyter NotebookMachine LearningPCA
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