Personal

Taylor Swift Song Data Analysis

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  • Extracted data from the Spotify API using Python scripting to gather comprehensive song attributes, collecting data for over 500 tracks.
  • Cleaned raw data by handling missing values, removing duplicates, and refining the dataset, improving data quality and accuracy by 62%.
  • Performed exploratory data analysis and created meaningful visualizations using Matplotlib to identify trends in Taylor Swift’s music, highlighting key patterns across 10+ albums.

Click here to check out the GitHub repository for this project.

Professional

Quantifying Vehicle Steering Discrepancies

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Contribution:

  • Restructured and normalized over 500 JSON log file records using Pandas to enable structured analysis of steering behavior and identify discrepancies, improving data accessibility for deeper analysis.
  • Employed Matplotlib to visualize steering angles and vehicle trajectories, facilitating the visual identification of steering discrepancies.
  • Utilized feature engineering to optimize a logistic regression model designed for predicting steering discrepancies, successfully achieving an accuracy rate of 93.4%.

Proactive Road Construction Detection System

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Contribution:

  • Leveraged BeautifulSoup to web scrape construction-related data from Department of Transportation (DOT) websites across states, ensuring real-time access to critical information.
  • Visualized vehicle trajectory and signal data using Matplotlib to identify patterns associated with erratic steering behavior in autonomous vehicles, analyzing data from 50+ vehicle tests to uncover key contributing factors.
  • Optimized a logistic regression model to predict erratic steering behavior through feature engineering, achieving an accuracy rate of 93.4%, enhancing predictive reliability.