manuelruizf Goto Github PK

3er-entregable-js

3rapreentregaruizfaller

amazon_vine_analysis

bikesharing

Created visualizations using Tableau to analyze New York bike sharing data.

credit_risk_analysis

cryptocurrencies-unsupervised-machine-learning

Use unsupervised machine learning, PCA algorithm, and K-Means clustering to analyze and classify a database of cryptocurrencies.

election_analysis

Created a python code with tuples and dictionaries that counts and classifes an county election votes. Shows the winner (most votes), with the total number of votes and the percentage

kickstarter-analysis-classlessons

Performing analysis on Kickstarter data to uncover trends

kickstarter_analysis_challenge

Kicistarter Analysis, Challenge 1 Module 1

manuelruizf

Config files for my GitHub profile.

mapping_earthquakes

Created an earthquake map with two different maps and the earthquake overlay. In this maps you are able to see: 1. The earthquake data in relation to the tectonic plates’ location on the earth. 2. All the earthquakes with a magnitude greater than 4.5 on the map. 3. The data on a third map.

mechacar_statistical_analysis

Performed multiple linear regression analysis to identify which variables in the dataset predict the mpg of MechaCar prototypes. Ran t-tests to determine if the manufacturing lots are statistically different from the mean population

mission-to-mars

movies-etl

Created an automated pipeline that takes in new data from a movie set. Performed the appropriate transformations, and loaded the data into existing tables. Performed the ETL process by adding the data to a PostgreSQL database.

neural_network_charity_analysis

Created a binary classifier that is capable of predicting whether applicants will be successful if funded by investors

pewlett-hackard-analysis

plotly

plotly_bellybutton

project-one

proyectofinal_ruizfaller

pyber_analysis

Created a summary DataFrame of the ride-sharing data by city type of a Uber-like company (Pyber). Then, using Pandas and Matplotlib, I created a multiple-line graph that shows the total weekly fares for each city type. Finally, I made a written report that summarizes how the data differs by city type and how those differences can be used by decision-makers at PyBer.

python

school_district_analysis

stock-analysis

surfs_up

tercerapreentrega_ruizfaller

testjs

ufos

webdesing

weddinggift