The Informational Content of Geographical Indications

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.

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.

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.

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.

Red wine data exploration and analysis

White Wine Exploratory Analysis

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.

The Prediction and Classification of Wine Quality

Wine Vision - Rediscover Quality

Exploratory Data Analysis on White Wine Quality Using r

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.

This project investigates the quality of red wine and its correlation with various factors to enhance understanding and improve wine production processes.