Welcome to my Python Projects Portfolio, a curated collection of data science and analytics projects designed to showcase my technical skills, problem-solving ability, and storytelling with data. Each project addresses real-world datasets and business questions using Python and industry-relevant tools.
Throughout these projects, I have utilized a wide range of tools and libraries, including:
- Languages: Python, SQL
- Libraries: pandas, NumPy, matplotlib, seaborn, scikit-learn, plotly, statsmodels
- Techniques: Data Cleaning, EDA, Machine Learning, Clustering, Predictive Modeling, Hypothesis Testing
- Data Formats: CSV, Excel, TSV, Parquet
- Other: Jupyter Notebooks, Git, Markdown
Each folder contains a standalone project with its own README.md, dataset(s), and Jupyter notebook(s). Here's a high-level overview:
Analyze crime patterns across LA neighborhoods to uncover trends and hotspots.
Skills: Geospatial analysis, time series trends, data visualization.
Clean, aggregate, and analyze e-commerce data for Walmart to generate insights.
Skills: Data wrangling, pipeline design, customer segmentation.
Prepare and explore marketing data for a Portuguese bank to improve campaign outcomes.
Skills: Feature engineering, exploratory analysis, data cleaning.
Apply unsupervised learning to cluster penguin species based on biological features.
Skills: Clustering (K-Means), EDA, classification-ready data prep.
Explore customer behavior to prepare data for future predictive modeling.
Skills: Data preprocessing, segmentation, dimensionality reduction.
Understand listing performance and pricing dynamics in NYC's Airbnb market.
Skills: Time-series, text analysis, multi-format data handling (CSV, TSV, Excel).
Investigate disparities in standardized SAT test results across New York City schools.
Skills: Educational data analysis, socioeconomic impact assessment.
Clean and format messy video game data from FIFA 21 for analytical use.
Skills: Advanced cleaning, missing value imputation, data reshaping.
Statistically compare the performance of men's and women's national soccer teams.
Skills: Hypothesis testing, inferential statistics, visual storytelling.
Dive into Netflix movie metadata to explore genres, trends, and viewer engagement.
Skills: EDA, categorical analysis, content trends.
Predict the likelihood of a customer filing a car insurance claim.
Skills: Binary classification, predictive modeling, ROC/AUC evaluation.
Review and improve a colleague’s notebook analyzing smartphone data.
Skills: Peer review, visualization improvements, best practices.
Build a predictive model to estimate movie rental durations using customer behavior.
Skills: Regression modeling, EDA, business use case simulation.
Use soil quality data to determine conditions for optimal crop yield.
Skills: Environmental data modeling, agriculture analytics, supervised learning.
Analyze and visualize over a century of Nobel Prize winners to uncover global trends.
Skills: Historical data storytelling, visualization, gender/nationality distribution.
- Proven ability to analyze structured and unstructured data across various domains.
- Strong focus on clean, reproducible code and storytelling with visualizations.
- Applied end-to-end project lifecycle: from data collection to modeling and insights.
If you're an employer, collaborator, or fellow data enthusiast, feel free to reach out. I'm always open to new opportunities and challenges.