AabidMK/SafeBite_Infosys_Internship_Oct2024
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSVPython
Technical Skills
Category EncodersData PreprocessingData VisualizationFeature EngineeringMachine LearningMatplotlib
Kishore
PROFILE
Kishore
During November 2024, Arumaikannan Kishore developed an end-to-end machine learning dataset preparation and encoding pipeline in the AabidMK/SafeBite_Infosys_Internship_Oct2024 repository. He focused on building robust data preprocessing scripts in Python, leveraging Pandas and Scikit-learn to clean raw data, visualize outliers, and encode categorical variables using both Label Encoding and Leave-One-Out Encoding. The processed dataset was saved for future machine learning experiments, streamlining setup and ensuring reproducibility. While the work was limited to a single feature and did not involve bug fixes, it demonstrated a solid grasp of feature engineering, data visualization, and reproducible data preparation workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,168
Activity Months1
Your Network
6 peopleWork History
November 2024
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key deliverable: ML Dataset Preparation and Encoding Pipeline. Delivered a preprocessed dataset with encoded features and supporting scripts for data preparation (data cleaning, outlier visualization, and encoding of categorical variables via Label Encoding and Leave-One-Out Encoding). The processed data is saved for future machine learning model use. No major bugs fixed this month. Impact: reduced setup time for ML experiments, improved data quality and reproducibility, and a repeatable feature engineering pipeline. Technologies/skills demonstrated: Python data processing, dataset management, encoding techniques, data visualization, and Git version control.
1 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Key deliverable: ML Dataset Preparation and Encoding Pipeline. Delivered a preprocessed dataset with encoded features and supporting scripts for data preparation (data cleaning, outlier visualization, and encoding of categorical variables via Label Encoding and Leave-One-Out Encoding). The processed data is saved for future machine learning model use. No major bugs fixed this month. Impact: reduced setup time for ML experiments, improved data quality and reproducibility, and a repeatable feature engineering pipeline. Technologies/skills demonstrated: Python data processing, dataset management, encoding techniques, data visualization, and Git version control.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVPython
Technical Skills
Category EncodersData PreprocessingData VisualizationFeature EngineeringMachine LearningMatplotlibPandasScikit-learnSeaborn
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