yawavi92

-Climate_Analysis

Airbnb-Data-Analysis

The purpose of this project was to tell a story of Airbnb accommodations using data visualizations. Here, we propose to use visualizations to graphically display different filters based on price points, cities, and room types. In addition, we propose to use both plotly heatmap and cluster graphing strategies to display bookings that may differ by price points, cities, and room type.

api-oo

citiBike

city_bike

Using Python and Tableau to visualize CitiBike data downloaded from NYC CitiBike System. The data history analyzed covers the period from January 2019 to December 2019.

Credit_Risk

Using supervised machine learning to predict whether a customer's default risk is low or high to help the bank choose a creditworthy customer.

data-_modeling

Data modeling / tables / keys & constraints / string manipulation / subquery / window function / set operator

earthquake_visualization

Visualizing earthquake history data downloaded from United States Geological Survey. This utilized JavaScript, Leaflet, CSS and HTML.

ML-Housing_price_predection

Housing price prediction using Supervised machine learning model in order to help people living in Melbourne price their future house.

portfolio

This is my project portfolio.

yawavi92/ML-Housing_price_predection

Housing price prediction using Supervised machine learning model in order to help people living in Melbourne price their future house.

yawavi92/citiBike

yawavi92/portfolio

This is my project portfolio.

yawavi92/-Climate_Analysis

yawavi92/Airbnb-Data-Analysis

The purpose of this project was to tell a story of Airbnb accommodations using data visualizations. Here, we propose to use visualizations to graphically display different filters based on price points, cities, and room types. In addition, we propose to use both plotly heatmap and cluster graphing strategies to display bookings that may differ by price points, cities, and room type.

yawavi92/api-oo

yawavi92/city_bike

Using Python and Tableau to visualize CitiBike data downloaded from NYC CitiBike System. The data history analyzed covers the period from January 2019 to December 2019.

yawavi92/Credit_Risk

Using supervised machine learning to predict whether a customer's default risk is low or high to help the bank choose a creditworthy customer.

yawavi92/data-_modeling

Data modeling / tables / keys & constraints / string manipulation / subquery / window function / set operator

yawavi92/earthquake_visualization

Visualizing earthquake history data downloaded from United States Geological Survey. This utilized JavaScript, Leaflet, CSS and HTML.

yawavi92/awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

yawavi92/Belly_Button_Biodiversity_Dashboard

yawavi92/Big-Data

yawavi92/Charity_Funding_Predictor

yawavi92/Cryptocurrencies_Unsupervised_Machine_learning

Clustering cryptocurrency using unsupervised machine learning on purpose to help news crypto investor.

yawavi92/Employee_SQL

Designing the tables to hold data in the CSVs, import the CSVs into a SQL database. Set primary and foreign keys for tables and retrieving employees information on purpose to help the HR.

yawavi92/ETL_project

yawavi92/Getting-funded

Analyzing a database of 4,000 past projects submitted for crowdfunding in order to uncover any hidden trends. The trends can organizations made it through the funding process with positive outcome. Excel was used for this project

yawavi92/learn-databases

yawavi92/Machine_learning_titanic

The project was about to predict survival or death on the Titanic boat with supervised model. The dataset was unbalanced and the there were some outliers. but some python codes had help address those issues.

yawavi92/Mars_scapring

yawavi92/project-1-earthquake-analysis-1

yawavi92/python-engineer-notebooks

yawavi92/Python_automation

yawavi92/PythonOOP

The Original Code repository for my Python OOP Series

yawavi92/Resources

A list of resources to get you started in data science.

yawavi92/SQL_kickstarter

The purpose of this project is found hidden trends through data in order to help project leader found success. Python and SQL queries were used to find those answers as the number of projects per category, the Average goal by category, the average pledged by category , the State per category and main category, the Percentage of projects that met their goal by main category and the Average time from the launched to the deadline.

yawavi92/Wall_Street_stocks

yawavi92/Weather_API

yawavi92/weather_web