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apis_planmytrip_weatherdatavisualiztion

Using SciPy statistics, Python, Pandas, Matplotlib, decision and repetition statements, and data structures, I will assist PlanMyTrip, a hypothetical travel company, in visualization and statistical analysis by retrieving and analyzing weather data from APIs.

bigdata_amazon_vine_analysis

Using PySpark, I performed the ETL process on a large dataset (170,000 rows) of Video Games. Next, I created an AWS relational database instance & transformed the data to be loaded into PostgreSQL. Once in PgAdmin, exported the Video Game Review Table as a CSV file. Afterward, I loaded the data into Python to create Dataframes using Pandas. Then analyzed the data to determine if there was bias in paid reviews vs. unpaid reviews.

brockchain_creatingbasicblockchain

chainshotdevsetup

Set up of ChainShot dev environment!

contract-puzzles

cryptocurrencies_unsupervised_machine_learning

Using Sklearn, Python, & Plotly, I analyzed a dataset of cryptocurrencies with unsupervised machine learning algorithms to spot any trends that would make a client want to invest in them.

deploycontract_hardhat-ethers.js

ecdsa-exchange

A Simple Exchange

election_analysis_tom

Election Audit using Python

etherstransactions

Practice Problems using ethers.js

etl_movies

faucethardhattesting

Using mocha and chai to test a faucet smart contract sample project

hardhat_games

The goal of the games is to get more familiar with using Hardhat by writing scripts to emit win

javascript-simplified

javascript_ufo_sightings_webpage

Using JavaScript, HTML, and CSS, I created a custom webpage that showcases different UFO sightings worldwide. To display this data, I created a table to organize UFO data stored as a JavaScript array. This table can filter the data table array using text box search criteria set up using the JS D3 library. JavaScript was the primary coding language to program the webpage using HTML. At the same time, CSS and Bootstrap were used to help style the HTML webpage.

json_rpc

Practice building basic JSON_RPC scripts for Ethereum call and sending transactions

kickstarter-analysis

Reviewing Kickstarter Data

mapping_earthquakes_with_js_-_apis

Using JavaScript's Leaflet and D3 libraries along with Mapbox's API, I populated a geographical map using GeoJSON earthquake data from the U.S. Geological Survey URL. Each earthquake is visually represented by a color and a circle, where a higher magnitude has a larger diameter and is darker in color..

matplotlib_pyber_analysis

Using Matplotlib, Python, Pandas, SciPy, and Jupyter Labs, I created visualizations of rideshare data for PyBer to help improve access to ride-sharing services and determine affordability for underserved neighborhoods. With Pandas and Jupyter Lab, I cleaned the data into Data Series or DataFrames, so we could use Matplotlib's features to create and annotate charts that visualize data. In this module, I created charts, scatter plots, bubble charts, pie charts, and box-and-whisker plots, and make them visually compelling and informative by adding titles, axes labels, legends, and custom colors.

mauseranalysis

modifystate

multisig-hardhat

MultiSig Decentralized Application built with Hardhat

neural_network_charity_analysis

pandas_school_district_analysis

Using Jupyter Lab, Python, and the Pandas library, I analyzed PyberCitySchools district data and showcased trends in school performance. During this analysis, I read raw data from CSV files, inspected and cleaned data, merged datasets, performed mathematical calculations, and visualized the data with charts and graphs to tell a story.

plotly.js_data_visualization_belly_button_biodiversity

Using JavaScript and Plotly.js, and HTML, I created an interactive data visualization on the web to explore the data on bacteria biodiversity of belly buttons, which could help create real tasting fake meat. D3.json() helped me fetch the external data from csv files and web APIs, which that data was then parsed and manipulated, so I could use event handlers to add interactivity to my data visualization. Afterward, I then deployed my interactive chart on the Github Pages cloud server.

pow-chain

A Simple Proof of Work Chain

ratings_on_demand

Ratings on Demand is a Machine Learning Project using Python to predict IMDb movie ratings using features available before a movie’s release like genre, duration, budget, and Oscar nominations. Using datasets provided by Kaggle and data we scraped from IMDb, we created a database on PostgreSQL to hold our data. Using our database and Python, we used several machine learning models such as linear and ridge regression to determine which model provided the most accurate predictions. After collecting all our results, we built a web page with dashboard-like features to showcase our findings to investors.

salesanalysis-visualization

simplecryptodex

solidity_syntax