HaiAu2501/EL4TF
Mar 2025 – May 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData PreprocessingData ScienceFeature EngineeringMachine LearningMatplotlib
Kouhakouu
PROFILE
Kouhakouu
During two months on the HaiAu2501/EL4TF repository, Phong developed and enhanced stock price forecasting workflows using Python and Jupyter Notebook. He implemented decision tree and random forest regression models, incorporating time-series and lag features to improve predictive accuracy for Tesla stock prices. Phong later introduced an end-to-end LSTM-based forecasting pipeline, including model training, validation, and evaluation with metrics such as RMSE and R². He also built reproducible data preprocessing and model comparison workflows, and added automated autocorrelation analysis using Statsmodels. His work emphasized robust model evaluation, reproducibility, and data quality checks, reflecting a strong foundation in applied machine learning.
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
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
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