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John Sung's Projects

blockchain_ledger icon blockchain_ledger

Online application that allows our users to build a blockchain ledger system. While Python was used for the back-end code, Streamlit enhances the front-end user experience. This ledger allows the user to conduct financial transactions and validate the blockchain.

crypto_arbitrage icon crypto_arbitrage

Evaluate arbitrage opportunities between two exchanges, Coinbase and Bitstamp. The following data used will be inside the Resouces folder called 'bitstamp.csv' and 'coinbase.csv'. The project will provde statistical summaries and visualizations in order to identify potential arbitrage profits.

divrein icon divrein

Tool that allows investors to compare and contrast the power of dividend paying stocks and comparing the results of three options. This app will give you the option of taking the cash dividend as income, reinvesting the dividend in the same stock or reinvesting in cryptocurrency.

etf_analyzer icon etf_analyzer

A financial database and web application by using SQL, Python, and the Voilà library to analyze the performance of a hypothetical fintech ETF.

financial_planning_api_simulations icon financial_planning_api_simulations

A planner to determine if they have enough reserves for an emergency fund. A planner for retirement that forecasts the performance of their retirement portfolio in 30 years.

fintech_finder_pay icon fintech_finder_pay

The FinTech Finder allow the user to identify and pay proffesionals for hours contracted using blockchain technology

joint_savings_account_solidity icon joint_savings_account_solidity

This program was designed to automate the creation of a joint savings accounts with the use of Solidity. This smart contract will use ether management functions to implement a financial institution’s requirements for providing the features of the joint savings account with the ability to deposit and withdraw funds from the account.

kaseicoincrowdsale icon kaseicoincrowdsale

Create a fungible token called kaseiCoin for a crowdsale using solidy code on Remix.

loan_qualifier icon loan_qualifier

See qualifying loans from a list of lenders using a daily_rate_sheet.csv of loan criteria from various loan providers, answer a number of questions to evaluate the loan eligibility, and then return a list of qualifying loans.

logistic_regression_model_identify_credit_worthiness icon logistic_regression_model_identify_credit_worthiness

Train and evaluate models with imbalanced classes. Use a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers. You will be able to use a logistic regression model to compare two versions of the dataset, original and resampled.

neural_network_model_alternatives icon neural_network_model_alternatives

Model that predicts whether applicants will be successful if funded by Alphabet Soup. More than 34,000 organizations that have received funding from Alphabet Soup over the years were evaluated.

praedico icon praedico

In our MVP, we have created a user friendly dashboard using Steamlit to help users view stats, summary and overall predicitons using interchangeable machine learning models. As we continue to work on our application, we will look to include other sports, the ability to place a wager and add additional features like betting odds.

proptech icon proptech

Find properties in the San Francisco market that are viable investment opportunities. It is a unique oportunity to review a seven year period of data to identify income streams for the potential investor.

risk_return_anaysis icon risk_return_anaysis

Data preparation, analysis, and visualizations for key risk and return metrics to compare the S&P 500 to four other portfolios. You will calculate risk and return metrics, like the daily returns, standard deviation, sharpe ratio and beta. It will provide the ability to identify the most appropriate risk-and-return portfolio to invest in.

robo_advisor icon robo_advisor

Using AWS Lex, RoboAdvisor was created and designed for clients searching for guidance towards their retirement.

time_series_analysis_google_colab icon time_series_analysis_google_colab

Analyze MercadoLibre's financial and user data in clever ways to provide insight to help the company grow. You will be able to predict search traffic so you can translate into the ability to successfully trade the stock.

unsupervised_machine_learning icon unsupervised_machine_learning

Machine learning model that groups cryptocurrencies to assemble investment portfolios that are based on the profitability of those cryptocurrencies. Evaluate and contrast the best value of KMeans, explained variance ratios, elbow curves and clusters.

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