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
Anshu contributed to the kietmcaproject/AI_AI101B_2024-25 repository by developing an end-to-end house price prediction model and an AI text classifier, both implemented in Python. Leveraging libraries such as pandas, scikit-learn, and XGBoost, Anshu built a reusable workflow for data preprocessing, model training, and evaluation on the California housing dataset, reporting metrics like R-squared and Mean Absolute Error. Additionally, Anshu created a text classification script using GPT-2 perplexity to distinguish AI-generated from human-written content. The work included comprehensive documentation and asset management, supporting onboarding, stakeholder communication, and repeatable experimentation, with clear version control and no reported bugs.
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 people
Work 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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