Wine Quality

By shrijeetverma13 · January 28, 2026

Data Source :

The wine quality ratings show a clear distribution pattern, with the majority of wines receiving middle-range ratings. Out of all wines analyzed, 13.2% achieve premium status with a quality rating of 7 or higher. The visualization displays the complete distribution across all quality levels, with a red dashed line marking the premium threshold at 6.5.

There is a clear positive relationship between alcohol content and wine quality scores. The analysis shows a correlation coefficient of 0.451, indicating that wines with higher alcohol content tend to receive better quality ratings.

High-rated wines (7+) have distinctly different chemical profiles compared to lower-rated wines. The analysis compared 225 high-rated wines against 1,474 lower-rated wines across 11 chemical properties. A grouped bar chart visualization shows these differences side-by-side for easy comparison.

The analysis reveals that higher sulphate concentrations consistently produce better quality wines. The sulphate range of 1.2+ produces the highest average quality score of 6.86, followed closely by the 0.9-1.0 range at 6.10. In contrast, lower sulphate concentrations (0.3-0.6) result in lower quality scores around 5.3-5.4.

The relationship between density and alcohol content has a clear impact on wine quality. Higher quality wines tend to have higher alcohol content and lower density, while lower quality wines show the opposite pattern. This relationship makes chemical sense since alcohol is less dense than water.

Wine quality scores significantly decline when volatile acidity exceeds 0.5-0.6 g/L. The analysis shows a clear negative relationship between volatile acidity and quality, with wines in the higher acidity ranges consistently receiving lower quality ratings.

The analysis reveals that pH levels and residual sugar interact in interesting ways to influence wine quality. Two visualizations were created: a heatmap showing average quality ratings for different pH-sugar combinations, and a scatter plot displaying the distribution of wines across pH and sugar levels colored by quality. The data shows that individually, both factors have very weak correlations with quality (pH: -0.053, sugar: 0.005), but their combination matters more.

The analysis reveals which chemical properties most strongly influence wine quality ratings. A correlation chart was created showing how each chemical property relates to quality scores, with both positive and negative relationships identified.

Chloride levels have a negative relationship with wine quality, meaning lower chloride concentrations are associated with better wines. The analysis shows a correlation of -0.127 between chlorides and quality scores. The acceptable range for chlorides is 0.0385 to 0.1225 g/L, with the typical range being 0.07 to 0.09 g/L. High-quality wines (rated 7 or above) average around 0.076 g/L of chlorides.

The analysis reveals that residual sugar levels show relatively consistent patterns across quality ratings. The visualizations display two comprehensive charts: a box plot showing the distribution of residual sugar across all quality ratings, and a bar chart comparing average residual sugar levels for each quality tier.

Based on analysis of 225 top-tier wines (quality rating ≥ 7), I've identified the optimal median values for all 11 chemical properties to use as production targets. A comparison chart shows how these targets differ from overall wine medians, helping you understand which properties most distinguish premium wines.

I've generated three detailed comparison tables that analyze the chemical differences between medium-quality wines (rated 5-6) and higher-quality wines. These tables show the chemical properties across different quality levels, allowing you to identify which adjustments would most likely improve wine quality.

The analysis reveals that the optimal ratio of free to total sulfur dioxide for wine quality is approximately 0.30 (30%). This means that about 30% of the total sulfur dioxide should be in free form for higher quality wines.

The analysis identified 82 wines that have unusual chemical profiles yet still achieve high quality ratings (7 or above). These represent about 36% of all high-quality wines in the dataset.