Chameleon-company/MOP-Code
May 2025 – May 2025
1 Month active
Languages Used
CSVJupyter NotebookPython
Technical Skills
Data AnalysisDeep LearningGRUJupyter NotebookLSTMMachine Learning
Qasim khan
PROFILE
Qasim Khan
Qasim Zia enhanced traffic analysis capabilities for the Chameleon-company/MOP-Code repository by developing a deep learning model focused on Melbourne streets data. He applied LSTM and GRU neural networks within a Jupyter Notebook environment, leveraging Python for both data preprocessing and model implementation. The project delivered end-to-end artifacts, including model code and a formal report, enabling benchmarking and supporting data-driven decision-making for stakeholders. Qasim’s work demonstrated proficiency in time series analysis, geospatial data processing, and version control with Git. The depth of the solution is reflected in its comprehensive approach, integrating model development, evaluation, and clear documentation for future use.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
65,574
Activity Months1
Your Network
98 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025: Delivered Traffic Analysis Model Enhancement using LSTM/GRU for Melbourne streets dataset. Delivered new model code, a Jupyter notebook, and a formal report for Chameleon-company/MOP-Code. Commit 7006b92b05412cde49162a0a38ed8a426ccbf5a6. No major bugs fixed this month. This work strengthens traffic analytics capabilities with end-to-end artifacts enabling benchmarking and data-driven decisions. Technologies demonstrated include Python, LSTM/GRU neural networks, Jupyter notebooks, geospatial data processing, and Git-based version control.
1 Commits • 1 Features
May 1, 2025May 2025: Delivered Traffic Analysis Model Enhancement using LSTM/GRU for Melbourne streets dataset. Delivered new model code, a Jupyter notebook, and a formal report for Chameleon-company/MOP-Code. Commit 7006b92b05412cde49162a0a38ed8a426ccbf5a6. No major bugs fixed this month. This work strengthens traffic analytics capabilities with end-to-end artifacts enabling benchmarking and data-driven decisions. Technologies demonstrated include Python, LSTM/GRU neural networks, Jupyter notebooks, geospatial data processing, and Git-based version control.
May 2025
Activity
Loading activity data...
Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVJupyter NotebookPython
Technical Skills
Data AnalysisDeep LearningGRUJupyter NotebookLSTMMachine LearningPythonTime Series Analysis
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline