racousin/data_science_practice_2024
Nov 2024 – Feb 2025
2 Months active
Languages Used
CSVJupyter NotebookPythonShell
Technical Skills
API IntegrationBeautifulSoupCode OrganizationData AnalysisData CollectionData Engineering
tmaiuri
PROFILE
Tmaiuri
Worked on the racousin/data_science_practice_2024 repository, delivering eight features over two months focused on data engineering and machine learning workflows. Developed end-to-end pipelines for multi-source data ingestion, exploratory analysis, and quality checks across housing, weather, and financial datasets using Python and Pandas. Built baseline and advanced regression models with outlier handling, and implemented a deep learning MNIST classifier with early stopping in PyTorch. Enhanced electricity demand forecasting by refactoring preprocessing with KNN imputation and temporal feature extraction, and evaluated models using XGBoost and time-series cross-validation. Also created reinforcement learning experiments with Q-learning agents, emphasizing reproducibility and clean data management.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
8
Lines of code
38,801
Activity Months2
Your Network
15 peopleShared Repositories
15
Work History
February 2025
2 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for racousin/data_science_practice_2024. Key features delivered include a rewritten electricity demand forecasting data preprocessing pipeline and an RL exercise notebook. Specifically, the data pipeline now standardizes features, imputes missing values with KNN, and extracts temporal features from the date column, followed by a robust evaluation of an XGBoost model using time-series cross-validation. Separately, FrozenLake module 13: Q-learning exercise notebook was delivered, establishing the environment, defining agent classes (Agent and DecayAgent) with epsilon-greedy action selection and exponential epsilon decay, running experiments to compare performance, and visualizing cumulative rewards per episode to analyze parameter effectiveness. Major bugs fixed: none reported this month. Overall impact: improved data quality and forecasting reliability, plus hands-on RL experimentation capabilities, enabling faster iteration and better decision support. Technologies/skills demonstrated: Python data processing, feature engineering, KNN imputation, temporal feature extraction, XGBoost, time-series cross-validation, Q-learning, RL agents, epsilon-greedy strategies, experimentation, and data visualization.
2 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for racousin/data_science_practice_2024. Key features delivered include a rewritten electricity demand forecasting data preprocessing pipeline and an RL exercise notebook. Specifically, the data pipeline now standardizes features, imputes missing values with KNN, and extracts temporal features from the date column, followed by a robust evaluation of an XGBoost model using time-series cross-validation. Separately, FrozenLake module 13: Q-learning exercise notebook was delivered, establishing the environment, defining agent classes (Agent and DecayAgent) with epsilon-greedy action selection and exponential epsilon decay, running experiments to compare performance, and visualizing cumulative rewards per episode to analyze parameter effectiveness. Major bugs fixed: none reported this month. Overall impact: improved data quality and forecasting reliability, plus hands-on RL experimentation capabilities, enabling faster iteration and better decision support. Technologies/skills demonstrated: Python data processing, feature engineering, KNN imputation, temporal feature extraction, XGBoost, time-series cross-validation, Q-learning, RL agents, epsilon-greedy strategies, experimentation, and data visualization.
February 2025
November 2024
12 Commits • 6 Features
Nov 1, 2024Month: 2024-11 — Performance-focused feature delivery and quality improvements in racousin/data_science_practice_2024. Completed end-to-end data ingestion across multiple datasets (housing, weather/electricity, financial time series) with initial Exploratory Data Analysis, data quality checks (missing values/duplicates) and distribution visualizations. Implemented multi-source data integration and a baseline quantity prediction model with automated submission file generation. Refactored regression modeling to incorporate outlier detection/removal and to compare alternative models, improving prediction reliability. Built an MNIST handwritten digit classifier with data loading/ preprocessing, training with early stopping, and submission generation. Conducted module-level housekeeping: Module 3 and Module 5 submission data management and notebook cleanup to ensure clean artefacts and reproducibility. No critical bugs reported; focus remained on delivering business-value features and enhancing data/code quality. Impact includes faster, more reliable data pipelines, ready-for-submission artifacts across modules, and stronger data governance supporting informed business decisions.
November 2024
12 Commits • 6 Features
Nov 1, 2024Month: 2024-11 — Performance-focused feature delivery and quality improvements in racousin/data_science_practice_2024. Completed end-to-end data ingestion across multiple datasets (housing, weather/electricity, financial time series) with initial Exploratory Data Analysis, data quality checks (missing values/duplicates) and distribution visualizations. Implemented multi-source data integration and a baseline quantity prediction model with automated submission file generation. Refactored regression modeling to incorporate outlier detection/removal and to compare alternative models, improving prediction reliability. Built an MNIST handwritten digit classifier with data loading/ preprocessing, training with early stopping, and submission generation. Conducted module-level housekeeping: Module 3 and Module 5 submission data management and notebook cleanup to ensure clean artefacts and reproducibility. No critical bugs reported; focus remained on delivering business-value features and enhancing data/code quality. Impact includes faster, more reliable data pipelines, ready-for-submission artifacts across modules, and stronger data governance supporting informed business decisions.
Activity
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Quality Metrics
Correctness87.2%
Maintainability87.2%
Architecture85.8%
Performance77.2%
AI Usage22.8%
Skills & Technologies
Programming Languages
CSVJupyter NotebookPythonShell
Technical Skills
API IntegrationBeautifulSoupCode OrganizationData AnalysisData CollectionData EngineeringData EntryData ManagementData PreprocessingData ScienceData VisualizationDeep LearningFeature EngineeringFile ManagementFinancial Time Series Analysis
Repositories Contributed To
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