"# Wine-quality-prediction" In this ML project I have mainly used Seaborn and RandomforestClassifier Seaborn is used in data analysis and machine learning for data visualization. It helps in understanding the dataset by providing clear and aesthetically pleasing plots. It is particularly useful for: Exploratory Data Analysis (EDA): Helps in understanding patterns, trends, and relationships between features. Correlation Analysis: Heatmaps in Seaborn can show feature correlations, which help in feature selection. Distribution Analysis: KDE plots and histograms help understand feature distributions, which can guide preprocessing steps like normalization or outlier handling.