Portfolio
Data Analytics, Business Cases and Python Programs
This portfolio showcases a selection of my recent data analytics and business strategy projects built around realistic business scenarios. Each case applies professional analytical methods to real-world challenges, illustrating how I translate business objectives into data-driven insights. The portfolio includes R-based case studies with written summaries and R code, highlighting my ability to design and implement effective analytical solutions.
Additionally, you’ll find a selection of Python programs that highlight my practical problem-solving skills and technical fluency in applying programming concepts to diverse challenges.
From Data to Dollars: Identifying the Next 100 Credit Line Customers with Predictive Modeling
Built a decision-tree propensity model to identify the 100 prospective customers most likely to accept a new used-car line of credit. Combined exploratory analysis, SEMMA workflow, and model scoring to guide targeted marketing efforts.
Customer Loyalty Insights: Modeling Household Spending with Machine Learning
Used XGBoost and other machine-learning models to predict household spending among prospective loyalty members. Delivered actionable profiles and insights on current and potential customers to inform segmentation and marketing strategy.
Predicting Diabetes Risk Using Logistic Regression and Random Forest
This project examined which health and demographic factors most strongly predict diabetes risk and compared the performance of logistic regression and random forest models. By analyzing variables such as glucose levels, BMI, insulin, and age, I identified glucose as the dominant predictor and found that the random forest model achieved near-perfect accuracy (AUC = 0.99), demonstrating the power of non-linear algorithms in capturing complex health relationships.
Beyond the technical work, this analysis held personal meaning for me. Having lost close family members to diabetes, I approached the data with both analytical rigor and empathy for those affected by the disease. The project reinforced how data science can reveal patterns that deepen understanding and potentially inform healthier choices—while also highlighting the tradeoff between model interpretability and predictive strength in real-world analytics.
Python Projects and Code Examples
A selection of my favorite Python scripts from my classwork, highlighting data processing, automation, and algorithmic problem-solving techniques. Each example demonstrates clear logic, strong commenting, and reproducible workflows designed to showcase analytical thinking in code
Data and Text Processing
Romeo and Juliet Word Analysis
Analyzes the full text of Romeo and Juliet to compute a histogram of word lengths. Demonstrates string processing, dictionary accumulation, and proportion calculations in Python
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DNA Sequence Analyzer
Compares two DNA strands and reports mismatched or missing nucleotides. Demonstrates nested loops, conditional logic, and counting with dictionaries
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Random Sentence Generator
Builds random sentences based on word transition probabilities. Demonstrates randomness, list operations, and simple Markov chain logic
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Algorithms and Problem Solving
Caesar Cipher Tool
Encrypts or decrypts text using a configurable letter shift. Demonstrates file I/O, command-line arguments, and modular arithmetic for character manipulation
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Palindrome Checker
Determines whether a string reads the same forward and backward. Demonstrates recursion, string slicing, and base-case handling
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Roman Numeral Converter
Converts Roman numerals to integers using right-to-left subtraction logic. Demonstrates control flow, iteration, and algorithmic reasoning
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Automation and Integration
Clock Formatter
Converts 24-hour times to 12-hour AM/PM format with input validation. Demonstrates class design, string formatting, and conditional logic
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Stock Price Fetcher
Retrieves the last 100 daily closing prices for a stock using the Alpha Vantage API. Demonstrates API integration, JSON parsing, and data extraction
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Pokémon Stat Search
Filters Pokémon data for entries within a chosen stat range. Demonstrates user input handling, list filtering, and sorting in Python
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