kietmcaproject/AI_AI101B_2024-25
Apr 2025 – May 2025
2 Months active
Languages Used
Python
Technical Skills
Data ScienceData VisualizationMachine LearningXGBoostNatural Language ProcessingPython
AnshuPatel645
PROFILE
Anshupatel645
Over a two-month period, contributed to the kietmcaproject/AI_AI101B_2024-25 repository by developing end-to-end machine learning solutions and enhancing project documentation. Built a house price prediction model using Python, pandas, scikit-learn, and XGBoost, implementing data loading, preprocessing, model training, and evaluation with R-squared and Mean Absolute Error metrics. Delivered a text classification script leveraging GPT-2 perplexity to distinguish AI-generated from human-written content, including upload and preview features. Maintained clear, traceable commits and added comprehensive documentation and presentation assets, supporting onboarding and stakeholder communication. Focused on reusable data science patterns and automation to accelerate experimentation and decision-making.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
4
Lines of code
110
Activity Months2
Your Network
139 peopleWork History
May 2025
3 Commits • 2 Features
May 1, 20252025-05 Monthly Summary: Delivered key assets and automation for the AI_AI101B_2024-25 project, enhancing information resources and enabling automated text classification. No major bugs reported this month. The work improves knowledge accessibility, accelerates content verification, and supports onboarding and stakeholder decision-making.
3 Commits • 2 Features
May 1, 20252025-05 Monthly Summary: Delivered key assets and automation for the AI_AI101B_2024-25 project, enhancing information resources and enabling automated text classification. No major bugs reported this month. The work improves knowledge accessibility, accelerates content verification, and supports onboarding and stakeholder decision-making.
May 2025
April 2025
2 Commits • 2 Features
Apr 1, 2025April 2025: Delivered an end-to-end House Price Prediction Model Script using pandas, numpy, seaborn, scikit-learn, and XGBoost. The script loads the California housing dataset, preprocesses features, splits data, trains an XGBoost regressor, and reports evaluation metrics (R-squared and Mean Absolute Error). Added AI project documentation: presentation file and a Mean Squared Error PDF to the AI TECH directory (no code changes). No critical bugs reported this month. Business impact includes a repeatable pricing-model prototype, faster data-driven decision-making, and clearer stakeholder communication through ready-to-share docs. Technologies demonstrated include Python data science stack, model training/evaluation, and documentation practices with strong version-control traceability.
April 2025
2 Commits • 2 Features
Apr 1, 2025April 2025: Delivered an end-to-end House Price Prediction Model Script using pandas, numpy, seaborn, scikit-learn, and XGBoost. The script loads the California housing dataset, preprocesses features, splits data, trains an XGBoost regressor, and reports evaluation metrics (R-squared and Mean Absolute Error). Added AI project documentation: presentation file and a Mean Squared Error PDF to the AI TECH directory (no code changes). No critical bugs reported this month. Business impact includes a repeatable pricing-model prototype, faster data-driven decision-making, and clearer stakeholder communication through ready-to-share docs. Technologies demonstrated include Python data science stack, model training/evaluation, and documentation practices with strong version-control traceability.
Activity
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Quality Metrics
Correctness92.0%
Maintainability92.0%
Architecture92.0%
Performance88.0%
AI Usage28.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data ScienceData VisualizationMachine LearningNatural Language ProcessingPythonText ClassificationXGBoost
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