Fernando-JAL/Neurociencias-2025-2
Jan 2025 – May 2025
3 Months active
Languages Used
TextJupyter NotebookPythonJSON
Technical Skills
DocumentationConfusion MatrixData VisualizationDecision TreesJupyter NotebooksMachine Learning
CaroDaniells
PROFILE
Carodaniells
Carolina Daniells developed a suite of machine learning and data analysis tools for the Fernando-JAL/Neurociencias-2025-2 repository over three months. She created Jupyter Notebooks demonstrating decision tree modeling on the Iris dataset, including pruned and unpruned variants with confusion matrix evaluation, and delivered a brain tumor classification workflow using both convolutional neural networks and Random Forests on MRI images. Her work incorporated Python, scikit-learn, and TensorFlow, emphasizing reproducibility and clear reporting. She also contributed foundational documentation to clarify project learning goals, supporting onboarding and collaboration. The features addressed data-driven neuroimaging tasks and improved the project’s analytical and educational capabilities.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
4
Lines of code
1,156
Activity Months3
Your Network
37 peopleSame Organization
@comunidad.unam.mx17
Work History
May 2025
3 Commits • 2 Features
May 1, 2025May 2025 performance summary for Fernando-JAL/Neurociencias-2025-2: Delivered two high-value features enhancing data exploration, reporting, and ML diagnosis capabilities. Implemented a new Jupyter Notebook (Tarea2MOD_Carolina.ipynb) for data visualization and reporting with embedded images, and built brain tumor classification models using CNN and Random Forest with training, evaluation, and a comparative analysis showing Random Forest as more accurate and consistent. No major bugs fixed this month; focus was on feature delivery and ML experimentation. The work strengthens data-driven decision support and reproducibility for neuroimaging tasks.
3 Commits • 2 Features
May 1, 2025May 2025 performance summary for Fernando-JAL/Neurociencias-2025-2: Delivered two high-value features enhancing data exploration, reporting, and ML diagnosis capabilities. Implemented a new Jupyter Notebook (Tarea2MOD_Carolina.ipynb) for data visualization and reporting with embedded images, and built brain tumor classification models using CNN and Random Forest with training, evaluation, and a comparative analysis showing Random Forest as more accurate and consistent. No major bugs fixed this month; focus was on feature delivery and ML experimentation. The work strengthens data-driven decision support and reproducibility for neuroimaging tasks.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Fernando-JAL/Neurociencias-2025-2. Key feature delivered: Machine Learning Notebook: Decision Trees on Iris with Pruned/Unpruned Trees and Evaluation. This work provides an end-to-end notebook demonstrating creation, training, pruning variants, and visualization of decision trees, plus generation of confusion matrices to evaluate model performance. No major bugs reported this period. Overall impact: delivers a reusable, educational ML notebook suite that supports reproducible experiments, demos for stakeholders, and faster onboarding for ML tasks in the Neurociencias project. Technologies demonstrated: Python, Jupyter notebooks, scikit-learn, data visualization, confusion matrix construction, and model evaluation. Commit references: a1441b6d58d564eecfdbf8bb81a7292a683fb04a; 9071a739c60cde3abbb23f1ea9540d2c8dc04fe5.
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for Fernando-JAL/Neurociencias-2025-2. Key feature delivered: Machine Learning Notebook: Decision Trees on Iris with Pruned/Unpruned Trees and Evaluation. This work provides an end-to-end notebook demonstrating creation, training, pruning variants, and visualization of decision trees, plus generation of confusion matrices to evaluate model performance. No major bugs reported this period. Overall impact: delivers a reusable, educational ML notebook suite that supports reproducible experiments, demos for stakeholders, and faster onboarding for ML tasks in the Neurociencias project. Technologies demonstrated: Python, Jupyter notebooks, scikit-learn, data visualization, confusion matrix construction, and model evaluation. Commit references: a1441b6d58d564eecfdbf8bb81a7292a683fb04a; 9071a739c60cde3abbb23f1ea9540d2c8dc04fe5.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary: Delivered foundational documentation in Fernando-JAL/Neurociencias-2025-2 by creating 'expectativascaro.txt' containing a single line expressing a learning goal related to machine learning, virtual reality, and rehabilitation. This clarifies objectives for ML/VR rehab initiatives and improves onboarding and cross-team collaboration. No major bugs reported this month. Commit: 1d42bedd62bb8a4b2b37435883c2a1b5e5435d80.
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary: Delivered foundational documentation in Fernando-JAL/Neurociencias-2025-2 by creating 'expectativascaro.txt' containing a single line expressing a learning goal related to machine learning, virtual reality, and rehabilitation. This clarifies objectives for ML/VR rehab initiatives and improves onboarding and cross-team collaboration. No major bugs reported this month. Commit: 1d42bedd62bb8a4b2b37435883c2a1b5e5435d80.
January 2025
Activity
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Quality Metrics
Correctness76.6%
Maintainability76.6%
Architecture76.6%
Performance73.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPythonText
Technical Skills
Confusion MatrixData AnalysisData VisualizationDecision TreesDeep LearningDocumentationImage ClassificationJupyter NotebookJupyter NotebooksKerasMachine LearningMatplotlibNumPyOpenCVPandas
Repositories Contributed To
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