HaiAu2501/EL4TF
Mar 2025 – May 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData PreprocessingData ScienceFeature EngineeringMachine LearningMatplotlib
Kouhakouu
PROFILE
Kouhakouu
Developed a stock price forecasting system in the HaiAu2501/EL4TF repository, focusing on end-to-end predictive modeling and data diagnostics. Built tree-based and LSTM workflows in Jupyter Notebooks using Python and Pandas, starting with decision tree regression and evolving to Random Forest and deep learning models with lag features and model tuning. Enhanced reproducibility and model evaluation by implementing train/test splits, version comparisons, and CSV-based results storage. Introduced autocorrelation analysis with statistical tests to assess data quality. Emphasized robust data preprocessing, visualization, and automated diagnostics, supporting business insight generation and reliable forecasting without reported critical bugs during the development period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
9Total
Bugs
0
Commits
9
Features
4
Lines of code
6,527
Activity Months2
Your Network
13 peopleSame Organization
@sis.hust.edu.vn5
Shared Repositories
8
Your NameMember
bin1st090104Member
doclautc-1808Member
pqnhoangMember
HaiAu2501Member
LamMember
khangMember
Tamnt240904Member
Work History
May 2025
6 Commits • 3 Features
May 1, 2025May 2025 monthly summary for HaiAu2501/EL4TF focused on delivering end-to-end forecasting, reproducibility improvements, and data quality checks that drive business insights and forecast accuracy.
6 Commits • 3 Features
May 1, 2025May 2025 monthly summary for HaiAu2501/EL4TF focused on delivering end-to-end forecasting, reproducibility improvements, and data quality checks that drive business insights and forecast accuracy.
May 2025
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for HaiAu2501/EL4TF: Focused on delivering a predictive modeling feature for stock price forecasting. Implemented a tree-based forecasting pipeline in a Jupyter notebook, starting with a decision tree regression using Open, High, Low, and Volume to predict Adj Close, and evolving to incorporate time-series features and a Random Forest Regressor with lag features and model tuning to improve accuracy. No critical bugs reported; stabilized baseline workflow and prepared results for decision support.
March 2025
3 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for HaiAu2501/EL4TF: Focused on delivering a predictive modeling feature for stock price forecasting. Implemented a tree-based forecasting pipeline in a Jupyter notebook, starting with a decision tree regression using Open, High, Low, and Volume to predict Adj Close, and evolving to incorporate time-series features and a Random Forest Regressor with lag features and model tuning to improve accuracy. No critical bugs reported; stabilized baseline workflow and prepared results for decision support.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture73.4%
Performance62.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData PreprocessingData ScienceData VisualizationDecision TreesDeep LearningFeature EngineeringMachine LearningMatplotlibModel EvaluationModel TrainingPandasPythonRandom Forest RegressionRegression
Repositories Contributed To
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