Exploratory Data Analysis on White Wine Quality Using r
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Updated
Mar 27, 2017 - HTML
Exploratory Data Analysis on White Wine Quality Using r
White Wine Exploratory Analysis
Use R and apply exploratory data analysis techniques to explore relationships in one variable to multiple variables and to explore the red wine data set for distributions, outliers, and anomalies.
Udacity's Nanodegree program built in partnership with Kaggle and Mode that aims at training new Data Analysts mastering Python, R, SQL, and statistics to uncover insights, communicate critical findings, and create data-driven solutions.
Contains projects needed to complete Udacity's Data Analyst Nanodegree Program
This repo contains EDA of red and white wine and how it relates to quality.
We build a machine learning model to predict if a wine is considered as good or not. The model takes as input some wine characteristics (alcohol content, acidity, etc), and then describes the quality of the wine as (“Good” or “Bad”). We starts with decision tree and then use Random Forest to improve our classification scores.
Wine Vision - Rediscover Quality
In R markdown, with heavy use of ggplot2, I explored the physiochemical properties and quality ratings of over 6,000 wines with an eye to ML classification and prediction.
The Informational Content of Geographical Indications
The Prediction and Classification of Wine Quality
This is a wine dataset containing 1599 rows and 12 columns with factors like alcohol, color, PH, residual sugar, sulfur-dioxide was used to determine the quality of wine varying with color.
This project investigates the quality of red wine and its correlation with various factors to enhance understanding and improve wine production processes.
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