t800-101a Goto Github PK

crowdfunding-etl

deep-learning-venture_company

Creating a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup Co.

etl_for_crowdfunding

Building an ETL pipeline using Pandas, Python dictionary methods and regular expressions to extract and transform the data.

gaussian-splatting_3d

Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"

impact_vehicule_covid-19

The objective of this study is to analyze the impact of the COVID-19 pandemic on the values of used and new vehicles in the market from 2020 to 2023. We are looking at how the pandemic and its related economic effects influenced consumer behavior, vehicle demand, and market trends in the automotive industry during this period.

leaflet_for_usgs_visualization

Developing a way to visualize USGS data that will allow to better educate the public and other government organizations.

microbial_java_visualization

Will build an interactive dashboard to explore the Belly Button Biodiversity datasetLinks to an external site., which catalogs the microbes that colonize human navels.

ml_cryptoclustering_unsupervise

With the use of Python and unsupervised learning, to predict if cryptocurrencies are affected by 24-hour or 7-day price changes.

nosql_uk_foodstandar_rating

Using MongoDB we'll to evaluate some of the ratings data in order to help journalists and food critics decide where to focus future articles.

pandas-challenge

Module 4 Challenge

practical_statistics

Applying Excercices from Book "Practical Statistics" by Peter/Andrew Bruce & Peter Gedeck

project_1

Impact of Vehicule by COVID-19

pymaceuticals

python-api-challenge

This activity is broken down into two deliverables, WeatherPy and VacationPy.

python-challenge

Module 3 Challenge

scraping_mars

Extracting information via both automated browsing with Splinter and HTML parsing with Beautiful Soup.

singingbowl_spectrumanalisys_ml

Study to analyze Tibetan Singing Bowls Spectrum.

smu

sparksql_home_sales

Determine key metrics about home sales data. Then with Spark to create temporary views, partition the data, cache and uncache a temporary table, and verify that the table has been uncached.

sql-challenge

This Challenge is divided into three parts: Data Modeling, Data Engineering, and Data Analysis.

sql_alchemy-challenge

Holiday vacation in Honolulu, Hawaii! Trip planning! Let's start a climate analysis about the area.

stock-success-web

Stock-Success, analyzes live data from Global Industry Classification Standard (GICS) stock market sectors.

supervised_ml_credit-risk-classification

Train and Evaluate a model based on loan risk. Using 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.

vba-challenge

Module 2 Challenge - Excel VBA