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Projects

Welcome to my portfolio! As a graduate student in the MSBA program, I am actively seeking data analyst and business analyst job opportunities. This portfolio showcases my skills and passion in the field of data analytics. 

Uber and Lyft Trip Price Prediction with Supervised Machine Learning

Team Project

  • Developed machine learning models using Python libraries like Pandas, NumPy, Matplotlib, and Scikit-learn to predict Uber and Lyft surge pricing based on factors like weather, location, and time.

  • Engineered features from raw datasets and optimized hyperparameters of regression algorithms including Linear, Decision Tree, XGBoost, and Elastic Net using GridSearch, RandomizedSearch and BayesSearch.

  • Built an end-to-end price prediction pipeline that ingests data, generates features, and feeds them into the tuned machine learning model to output price forecasts.

  • Identified most important features driving surge pricing through feature importance analysis. Distance traveled had the highest influence on predicted price for both Uber and Lyft models.

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Steam Library Analysis: Uncovering User Behaviour and Video Game Popularity

Team Project

  • Conducted exploratory data analysis using BigQuery with complex SQL queries, Google Colab for collaboration, and Tableau to visualize user behavior and video game popularity patterns.

  • Applied data cleaning, merging, and aggregation techniques to prepare and manipulate data from multiple sources and formats, handle missing values, outliers, and align inconsistent data entries.

  • Generated Tableau dashboards and visualizations to communicate key findings and recommendations to game developers and stakeholders.

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Deciphering Key Influences on Restaurant Success within Uber Eats 

Team Project

  • Performed exploratory data analysis on restaurant attributes and customer preferences utilizing Python packages such as: Pandas, NumPy, Seaborn, and Plotly to uncover insights that can boost client revenue.

  • Identified optimal restaurant locations, cuisine types, menu sizes, and pricing strategies through geospatial, statistical, and visual analysis to provide data-driven expansion recommendations.

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