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https://github.com/bayunova28/hass_avocado_board

This repository contains about my final exam of Big Data Analytics II course at my college
https://github.com/bayunova28/hass_avocado_board

data-science machine-learning

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This repository contains about my final exam of Big Data Analytics II course at my college

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README

          

# Hass Avocado Board
* Instructor : Iwan Prasetiawan S.KOM., M.M..
* Place : Multimedia Nusantara University
* Course : Big Data Analytics II

## Table of Contents
* [Background](#background)
* [Requirement](#requirement)
* [Inspiration](#inspiration)
* [Schema](#schema)

## Background

HAB is the only avocado organization that equips the entire global industry for success by collecting, focusing and distributing investments to maintain and expand demand for avocados in the United States. HAB provides the industry with consolidated supply and market data, conducts nutrition research, educates health professionals, and brings people together from all corners of the industry to collectively work towards growth that benefits everyone. The organization also collects and reallocates funds to California and importer associations to benefit specific countries of origin in promoting their avocado brands to customers and consumers across the United States.



## Requirement
### SAS® OnDemand for Academics (ODA) Registration
* To gain access to ODA, you need to register with SAS Institute. Part of the registration process is to create a [SAS profile](https://welcome.oda.sas.com/login)
* Create a SAS Profile
* Verify the SAS Profile
* Register for SAS OnDemand for Academics with SAS Profile credentials

### SAS® Visual Data Mining & Machine Learning (VDMML)
* Open [SAS®Drive](https://auth.sas.com/) & login from your SAS® OnDemand for Academics (ODA) account
* Click tab on the top left
* Choose Explore and Visualize
* Import data in your local computer
* Save data in your public host repository dataset

## Inspiration
* Classification of avocado types from Agriculture Division
* Using 2 algorithms and model selection

### Gradient Boosting


### Neural Network


### Model Selection & Scoring

## Features
* `Date` - The date of the observation
* `AveragePrice` - The average price of a single avocado
* `type` - Conventional or organic
* `year` - The year
* `Region` - The city or region of the observation
* `Total Volume` - Total number of avocados sold
* `4046` - Total number of avocados with PLU 4046 sold
* `4225` - Total number of avocados with PLU 4225 sold
* `4770` - Total number of avocados with PLU 4770 sold