Ji Yeol (Eric) Yang's repositories
LEGO_Price_Dashboard
Created a dashboard using Tableau Public to visualize the evolution of the prices and the number of bricks in a LEGO set. Data was organized using Python.
Toronto_Bike_Share_Tableau_Project
Tableau interactive dashboard that visualizes the usage of Toronto Bike Share across different months and time blocks.
Netflix_Content_Distribution_and_Trends
Analyze the Netflix dataset using Python to gain insights into the content available on the platform and create an interactive dashboard using Tableau Public.
Study_of_the_Efficacy_of_Different_Anti-Cancer_Treatments
This Python-based Exploratory Data Analysis (EDA) aims to assess the effectiveness of Capomulin, an anti-cancer medication, in comparison to other treatment regimens. The initial phase involves an inter-drug comparison, followed by a detailed analysis of Capomulin, which includes the application of linear regression.
Python_Analysis_of_Financial_and_Election_Data
This Python project demonstrates a simple data analysis workflow without relying on the pandas library. The goal is to read a CSV file, perform basic data analyses, and generate a text file with the results.
Data_Engineering_for_Pewlett_Hackard
Creates an entity relationship diagram; uses SQL for data engineering and data analysis of employee data for a fictitious company.
Surfs_Up
Analyzes climate and precipitation in Honolulu, Hawaii using database stored in SQLite. Extracts and transforms the data using SQLite and visualizes the analysis by using Matplotlib.
Crowdfunding_ETL
An ETL project that builds a pipeline of crowdfunding projects using Python, Pandas, and SQL database. An ERD and a table schema is created for support.
School_Budgeting_Analysis
Analyzes the district-wide standardized test results of students in PyCity to help the school board and mayor make strategic decisions regarding future school budgets and projects. The data includes every student’s standardized math and reading scores, as well as various information on the school.
Food_Hygiene_Rating_Analysis
The purpose of this project is to lay the groundwork for evaluation of various establishments across the UK. It uses MongoDB to create a database and to import the data and PyMongo to make different queries that are needed for analytical work.
Machine_Learning_Crypto_Clustering
Uses Python, Scikit-Learn, and unsupervised learning - specifically KMeans Algorithm - to predict if cryptocurrencies such as Bitcoin, Ethereum, and Ripple are affected by 24-hour or 7-day price changes.
Credit_Risk_Classification
Uses various techniques to train and evaluate a model based on loan risk. Uses a dataset of historical lending activity from a peer-to-peer lending services company t o build a model that can identify the creditworthiness of borrowers.
Deep_Learning-Venture_Success
Uses neural network to create a binary classification model to predict if a venture organization will be successful after receiving funding. The model is built on a target variable, SUCCESS, that interacts with various feature inputs. If successful, the model can be used by organizations to select for funding promising venture applicants.
Machine_Learning_Titanic_Survival
Predicting the outcome of passenger survival on the Titanic tragedy using neural networks, logistic regression, and random forest. Generates an interactive dashboard using Tableau.
Dashboard_Soccer_Performance_vs_Money
Dashboard that visualizes the market value and performance of the top soccer teams across Europe. Uses Python, Pandas, and SQL to process and manipulate data, and JavaScript to build the dashboard.
Netflix_Through_the_Pandemic
Uses Python to analyze the performance of Netflix stock pre to post-pandemic. Includes multiple stock indicators and a comparison to Disney stock.