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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.
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.
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.
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.
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.