racousin/data_science_practice_2024
Nov 2024 – Mar 2025
4 Months active
Languages Used
CSVPythonSQLShell
Technical Skills
Data AnalysisData CollectionData EngineeringJupyter NotebookMatplotlibPackage Management
Shuunii1001
PROFILE
Shuunii1001
Over four months, racousin developed a series of data science assets in the racousin/data_science_practice_2024 repository, focusing on practical workflows for data analysis and machine learning. They built a Python package for reusable utilities, created Jupyter Notebooks for data collection, cleaning, and exploratory analysis, and prepared datasets for time-series forecasting and regression exercises. Their technical approach emphasized reproducibility and modularity, leveraging Python, Pandas, and XGBoost to construct end-to-end pipelines for electricity demand prediction and model evaluation. The work demonstrated depth in data preprocessing, feature engineering, and evaluation metrics, providing robust templates and assets for ongoing data science experimentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
6
Lines of code
7,284
Activity Months4
Your Network
15 people
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for racousin/data_science_practice_2024: Delivered a new hands-on module (Module 6) focused on data collection, exploration, and model evaluation. This includes an end-to-end Jupyter Notebook that downloads datasets, performs exploratory data analysis, and evaluates regression models using cross-validation. A custom weighted accuracy metric was introduced to provide a nuanced performance signal for varied data regimes. The work supports practical data science training with reproducible workflows and ready-to-use templates for students and practitioners.
1 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for racousin/data_science_practice_2024: Delivered a new hands-on module (Module 6) focused on data collection, exploration, and model evaluation. This includes an end-to-end Jupyter Notebook that downloads datasets, performs exploratory data analysis, and evaluates regression models using cross-validation. A custom weighted accuracy metric was introduced to provide a nuanced performance signal for varied data regimes. The work supports practical data science training with reproducible workflows and ready-to-use templates for students and practitioners.
March 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for racousin/data_science_practice_2024: Delivered foundational data ingestion assets to accelerate time-series forecasting experiments. Key feature: Daily Electricity Demand Dataset Ingestion (2019) with 'date' and 'electricity_demand' columns, prepared for modeling and feature engineering. Commit 0b5918409ce2b25517eed4295e828e27416726c4 ('Add files via upload').
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for racousin/data_science_practice_2024: Delivered foundational data ingestion assets to accelerate time-series forecasting experiments. Key feature: Daily Electricity Demand Dataset Ingestion (2019) with 'date' and 'electricity_demand' columns, prepared for modeling and feature engineering. Commit 0b5918409ce2b25517eed4295e828e27416726c4 ('Add files via upload').
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for racousin/data_science_practice_2024 focusing on end-to-end Electricity Demand Prediction Analytics Pipeline. Delivered a robust data science workflow from data collection to model evaluation, with rigorous preprocessing and feature engineering, using XGBoost for electricity demand forecasting. This work enhances forecasting accuracy and data quality for electricity demand planning.
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for racousin/data_science_practice_2024 focusing on end-to-end Electricity Demand Prediction Analytics Pipeline. Delivered a robust data science workflow from data collection to model evaluation, with rigorous preprocessing and feature engineering, using XGBoost for electricity demand forecasting. This work enhances forecasting accuracy and data quality for electricity demand planning.
December 2024
November 2024
3 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for racousin/data_science_practice_2024: Key features delivered: - Created a new Python package named 'mysupertools' with a multiply utility and packaging setup to enable distribution (commit e8fe2c8811d1157ecb3bf75095887299be81c0e3). - Delivered a data science practice notebook covering data collection, cleaning, and initial exploratory analysis including basic statistics and outlier detection (commit f7491ed90b1b3eefd99ae0cc6725b9f21d95fde6). - Added an ML exercise dataset file submission.csv with id and SalePrice columns for the SalesPrice prediction exercise (commit 467f32697ef781f7531de2f36040d1eb9ecb75f0). Major bugs fixed: - No major bugs fixed this month. Focus remained on feature delivery and tooling enhancements. Overall impact and accomplishments: - Established a distribution-ready tooling package (mysupertools) to support learners across data science exercises, improving reusability and collaboration. - Enhanced learner experience and reproducibility by providing a ready-to-use notebook for end-to-end data collection, cleaning, and initial analysis. - Supplied a concrete dataset scaffold (submission.csv) for ML practice, enabling faster onboarding to the SalesPrice prediction exercise. Technologies/skills demonstrated: - Python packaging and distribution workflow (setup scripts, packaging metadata) - Jupyter Notebook development for data collection, cleaning, statistics, and outlier detection - Data preprocessing, exploratory data analysis, and dataset preparation for ML tasks - Version control discipline with structured commit history.
November 2024
3 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for racousin/data_science_practice_2024: Key features delivered: - Created a new Python package named 'mysupertools' with a multiply utility and packaging setup to enable distribution (commit e8fe2c8811d1157ecb3bf75095887299be81c0e3). - Delivered a data science practice notebook covering data collection, cleaning, and initial exploratory analysis including basic statistics and outlier detection (commit f7491ed90b1b3eefd99ae0cc6725b9f21d95fde6). - Added an ML exercise dataset file submission.csv with id and SalePrice columns for the SalesPrice prediction exercise (commit 467f32697ef781f7531de2f36040d1eb9ecb75f0). Major bugs fixed: - No major bugs fixed this month. Focus remained on feature delivery and tooling enhancements. Overall impact and accomplishments: - Established a distribution-ready tooling package (mysupertools) to support learners across data science exercises, improving reusability and collaboration. - Enhanced learner experience and reproducibility by providing a ready-to-use notebook for end-to-end data collection, cleaning, and initial analysis. - Supplied a concrete dataset scaffold (submission.csv) for ML practice, enabling faster onboarding to the SalesPrice prediction exercise. Technologies/skills demonstrated: - Python packaging and distribution workflow (setup scripts, packaging metadata) - Jupyter Notebook development for data collection, cleaning, statistics, and outlier detection - Data preprocessing, exploratory data analysis, and dataset preparation for ML tasks - Version control discipline with structured commit history.
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture83.4%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVPythonSQLShell
Technical Skills
Data AnalysisData CollectionData EngineeringData IngestionData PreprocessingData ScienceData StorageFeature EngineeringJupyter NotebookLightGBMLinear RegressionMachine LearningMatplotlibPackage ManagementPandas
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