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
Developed an end-to-end machine learning dataset preparation and encoding pipeline, delivered in the AabidMK/SafeBite_Infosys_Internship_Oct2024 repository. The work focused on building robust data preprocessing scripts in Python, utilizing 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. By integrating data visualization with Matplotlib and Seaborn, the pipeline improved data quality assessment. No major bugs were addressed during this period, with efforts concentrated on feature engineering and repeatable, maintainable 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
Repositories Contributed To
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