Student Work (Report) | CSC2034 Full Report Part 2 - Correlation Heatmap: Wine Quality | Newcastle

Student | Client Work (Report) | CSC2034 Full Report Part 2 - Alcohol vs Quality Correlation | Wine Dataset Output Video | Newcastle University

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🚀 Video Details:
📊 Part 2 – Correlation Matrix Analysis | CSC2034 (Data Science)
This short video is Part 2 of the CSC2034 Data Science project for Newcastle University, focusing on feature selection using a correlation matrix. The wine dataset is analyzed to determine which physicochemical features are most strongly associated with wine quality.

🔍 In this part:
– Visualizing a correlation matrix using Seaborn
– Highlighting relationships between alcohol, sulphates, pH, and quality
– Identifying redundant and irrelevant variables
– Feature selection insights to guide ML modeling in Part 3

📘 Module: CSC2034 – Introducing Contemporary Topics in Computing
🏫 University: Newcastle University
📍 Location: Newcastle upon Tyne, England
📂 Dataset: UCI Wine Quality Dataset (Red & White)

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