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absentee_owner_data_cleaning

analyzing_historical_employee_data--sql

A major corporation needs to analyze employee data from the 1980s and 1990s, which is stored in separate CSV files. The project involves designing table schemas, importing data into a SQL database, and conducting data analysis tasks to extract insights.

biodiversity_interactive_dashboard

This project entails creating an interactive dashboard to explore microbial data from the Belly Button Biodiversity dataset. Using D3 library, it will display top OTUs, sample data, and demographic information, with dynamic updates based on user selections.

chris-avallon-nj-datascientist

citi-bike-project

city_schools_analysis_with_pandas

This project involves analyzing school and standardized test data using Pandas DataFrames. The goal is to provide insights to the city's school district, aiding in strategic decisions regarding budgets and priorities by identifying trends in school performance.

credit-risk-classification

This project involves training and evaluating a machine learning model to predict loan risk using historical lending data. It includes steps such as data preprocessing, logistic regression modeling, and performance evaluation, culminating in a credit risk analysis report.

crowdfunding_etl

Developed an ETL (Extract, Transform, Load) pipeline using Python, Pandas, and Python dictionary methods to process data for crowdfunding campaigns, ensuring accuracy and quality. Implemented SQL to establish a Postgres database in PGAdmin for storing the refined data, alongside creating an entity relationship diagram for comprehensive visuals.

cryptoclustering

This project applies Python and unsupervised learning to predict cryptocurrency price changes over 24 hours or 7 days. It involves data preparation, clustering using K-means, and visualizing results to understand the impact of using fewer features in clustering.

deep-learning-funding-decisions

This project involves using machine learning and neural networks to create a binary classifier for Alphabet Soup's funding applicants. The dataset contains metadata about organizations, and the goal is to predict their success if funded.

drug_efficacy_analysis

A pharma company requires analysis of an animal study dataset to evaluate its drug of interest against other drug regimens for treating squamous cell carcinoma. By leveraging Matplotlib, the project involves summarizing study results, identifying trends, and performing statistical analyses to aid in decision-making processes.

grocery_price_comparison_app

This project consists of an interactive website comparing grocery prices from Kroger, Walmart, and Aldi. It uses web scraping and APIs to gather and store data in a sqlite database, enabling users to query for the cheapest ingredients across stores while addressing ethical considerations and potential data collection issues.

home_sales

This SparkSQL project analyzes home sales data, optimizing queries and calculating average prices. Results are saved in a Jupyter Notebook and uploaded to a GitHub repository named "Home_Sales."

leaflet_for_earthquake_visualization

This project aims to visualize earthquake data provided by the United States Geological Survey (USGS) to enhance public understanding of seismic activity worldwide. Using Leaflet and GeoJSON, the project plots earthquake locations, magnitudes, and depths on an interactive map.

mars_webscraping_data_analysis

In this project, I explore Mars through web scraping and data analysis. In part 1, I extract Mars news titles and preview text. In part 2, I analyze Mars weather data using automated browsing, Beautiful Soup, and Pandas.

ml_crypto_currency_analysis

The purpose of this project is to develop a neural network algorithm to forecast the price of bitcoin over a 10 day period. This is achieved using an LSTM model.

nyc-citi-bike-analysis

In this Tableau project, I analyzed the New York Citi Bike program, focusing on generating reports and uncovering trends since 2013. By designing visualizations and dashboards, I provided insightful analyses to inform programmatic changes for city administrators.

python_apis_weather_and_vacation_planning

This project uses Python to analyze weather data from over 500 cities at varying distances from the equator, exploring relationships between weather variables and latitude through scatter plots and linear regressions. In Part 2, the Geoapify API and geoViews Python library are employed to plan vacations based on ideal weather conditions.

python_finance_poll_analysis

This project automates financial and election analysis using Python. It calculates metrics like profit changes and vote percentages, printing results to terminal and text files, demonstrating Python's advantages over Excel.

reddit_yahoo_finance_data_analysis

This project retrieves data from Yahoo Finance and Reddit, performs analysis, and visualizes the relationship between Reddit upvotes and comments. It also fetches historical stock data for a specific symbol from Yahoo Finance and conducts a linear regression analysis.

sqlalchemy-flask-climate_analysis

Delve into Honolulu's climate data with Python and SQLAlchemy for precipitation and station analysis, followed by designing a Flask API for data access.

uk_food_standards_analysis_mongodb

Assessing UK food hygiene ratings for Eat Safe, Love magazine involves setting up and updating a MongoDB database and performing exploratory analysis. PyMongo and Pretty Print libraries are essential for database setup and updates, while querying MongoDB with PyMongo and converting results to Pandas DataFrames will facilitate exploratory analysis.

vba-stock-market-analysis

This project involves using VBA scripting to analyze stock market data, calculating quarterly changes, percentage changes, and total volumes for each stock. The script also identifies stocks with the highest percentage increase, decrease, and total volume, applying conditional formatting to highlight changes.