In the data mining project focused on analyzing the quality and type of wine, I undertook a comprehensive analysis of the provided dataset. The dataset was divided into a training set and a test set, containing information about various features of wines, such as acidity levels, alcohol content, and other relevant characteristics.
To begin the project, I carefully examined and explored the dataset, gaining insights into the distributions of variables and identifying any missing or inconsistent data. Through data preprocessing techniques like data cleaning, handling missing values, and feature engineering, I ensured that the data was suitable for analysis.
Next, I performed in-depth analysis and modeling on the dataset. This involved applying appropriate data mining techniques, such as classification algorithms, to predict the quality and type of wines based on the given features. I evaluated the performance of the models using relevant evaluation metrics and fine-tuned the models to enhance their accuracy and predictive power. By conducting this analysis, I aimed to gain a deeper understanding of the factors influencing wine quality and type.