DataBytes-Organisation/DiscountMate_new
Dec 2024 – May 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData PreprocessingData VisualizationExcel File HandlingLogistic RegressionMachine Learning
Lachlan McDonald
PROFILE
Lachlan Mcdonald
Lachlan McDonald developed machine learning and data visualization features for the DiscountMate_new repository, focusing on product analysis and pricing insights. He implemented a category classification model using Python, Pandas, and Scikit-learn, achieving 88% test accuracy with TF-IDF features and logistic regression, and initiated a price prediction workflow with data quality checks. In a subsequent phase, he delivered Python-based visualizations addressing business questions around pricing and promotions, including SKU trends and discount indices, using Matplotlib and Numpy. His work emphasized reproducibility, clear documentation, and actionable reporting, providing a foundation for scalable analytics and supporting data-driven decision making.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
24,078
Activity Months2
Your Network
41 people
Work History
May 2025
2 Commits • 1 Features
May 1, 2025Monthly performance summary for 2025-05 focused on delivering data visualization capabilities for pricing and promotions, improving reporting clarity, and enabling data-driven decision making in DiscountMate_new.
2 Commits • 1 Features
May 1, 2025Monthly performance summary for 2025-05 focused on delivering data visualization capabilities for pricing and promotions, improving reporting clarity, and enabling data-driven decision making in DiscountMate_new.
May 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for DataBytes-Organisation/DiscountMate_new: Delivered ML-based product analysis capabilities, including category classification and price prediction models. Implemented category classifier with TF-IDF features, StandardScaler, Logistic Regression achieving 88% test accuracy; initiated price prediction workflow with exploratory data loading for Woolworths product data and duplicate detection for data quality. Committed artifacts with message 'Uploading Models and Documents'. No major bugs fixed this month; focus on feature delivery and documentation for reproducibility. The work provides business value by enabling data-driven discounts, improved catalog classification, and pricing insights, laying groundwork for scalable analytics and future model improvements.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for DataBytes-Organisation/DiscountMate_new: Delivered ML-based product analysis capabilities, including category classification and price prediction models. Implemented category classifier with TF-IDF features, StandardScaler, Logistic Regression achieving 88% test accuracy; initiated price prediction workflow with exploratory data loading for Woolworths product data and duplicate detection for data quality. Committed artifacts with message 'Uploading Models and Documents'. No major bugs fixed this month; focus on feature delivery and documentation for reproducibility. The work provides business value by enabling data-driven discounts, improved catalog classification, and pricing insights, laying groundwork for scalable analytics and future model improvements.
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData PreprocessingData VisualizationExcel File HandlingLogistic RegressionMachine LearningMatplotlibNumpyPandasPythonRandomForestClassifierScikit-learnStandardScalerTfidfVectorizer
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline