Sabrina L. (LegallyNotBlonde)

LegallyNotBlonde

User data from Github https://github.com/LegallyNotBlonde

Location:San Francisco Bay Area

GitHub:@LegallyNotBlonde

Sabrina L.'s repositories

Fire_Trends_Impact_Analysis

Transformed wildfire data into SQL, leveraging Python, Pandas, and interactive JavaScript dashboards to uncover trends in wildfire frequency, damage, and economic impact (2008-2022).

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:2Issues:1Issues:0

Movie_Revenue_Forecast

Analyzed 5,000+ movies with Pandas and Colab to build a machine learning model predicting movie revenue.

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:2Issues:1Issues:0

ai_stocks_5y_analysis_using_pandas

Group project analyzing top AI stocks versus the S&P 500 and Dow Jones, using cumulative returns, volatility, and ANOVA to evaluate profitability and risk

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:0Issues:0Issues:0

cancer_treatment_analysis_using_data_visualization_python

Evaluated SCC tumor treatments by analyzing drug effectiveness, including Capomulin, through tumor growth measurements.

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climate_analysis_using_sqlalchemy

Conducted climate analysis using historical weather data, leveraging queries, visualizations, and a Flask API to serve dynamic insights.

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Crowdfunding_ETL

Built an ETL pipeline to extract, transform, and load crowdfunding data into PostgreSQL, using Python and Pandas for transformation and ensuring data integrity with foreign key constraints

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CryptoClustering

Applied machine learning with K-Means clustering and PCA to uncover cryptocurrency behavior patterns for enhanced market analysis.

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Loan_Risk_Predictor

Built a Logistic Regression model for loan risk prediction, focusing on credit risk and improving high-risk loan detection.

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MegaData_SparkProject_Home_Sales

This project used SparkSQL and PySpark to analyze home sales data, optimizing performance with caching and partitioning by build date.

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Tableau_Citi_Bike

Analyzed Citi Bike data to uncover hidden issues and trends in ride duration, peak usage, and station demand, providing recommendations to optimize bike availability.

Language:Jupyter NotebookLicense:GPL-3.0Stargazers:0Issues:1Issues:0

Data_Collection_Scraping

Scraped Mars news and weather data, transforming it into insights with automated collection, JSON and CSV organization, and Pandas visualizations.

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Earthquake_Insights_Map

Created an interactive map with Leaflet and D3.js to visualize earthquake data, using markers for magnitude and depth with a legend for clarity.

Language:JavaScriptLicense:GPL-3.0Stargazers:0Issues:0Issues:0

employee_department_analysis_using_postgresql

Executed SQL queries on a PostgreSQL database to analyze employee and department data, utilizing primary and foreign keys across six tables.

Language:PythonLicense:GPL-3.0Stargazers:0Issues:0Issues:0

financial_and_election_analysis_using_python

Analyzed financial trends and election results, uncovering profit fluctuations, monthly changes, and vote distributions for key insights.

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Interactive_Microbiome_Dashboard

Built an interactive dashboard to explore microbial data from human belly buttons, using dynamic visualizations to reveal patterns across individuals.

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LegallyNotBlonde

Config files for my GitHub profile.

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NoSQL_Food_Data_Analysis

Managed and analyzed food establishment data with MongoDB, leveraging Python and Pandas to uncover insights on hygiene scores and ratings.

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schools_analysis_using_pandas

Analyzed student performance across district schools, uncovering correlations between outcomes, school size, type, and budget for actionable insights.

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sql-for-data-analysis-3271025

This is a repository for the LinkedIn Learning course SQL for Data Analysis

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stock_market_analysis_using_vba

Developed a VBA script to analyze quarterly stock performance, calculating percentage changes, identifying top and declining stocks, and tracking trading volume.

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Success_Prediction_Model

This project built a binary classifier to help Alphabet Soup predict applicant success using machine learning and feature selection.

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weather_data_analysis_and_visualization_python

Collected, analyzed, and visualized weather data via APIs, using correlation analysis and regression, with results exported for map-based visualizations.

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