title: ÐÑÐ¾Ð³Ð½Ð¾Ð· ÐºÐ°ÑÐµÑÑÐ²Ð° Ð²Ð¸Ð½Ð° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ñ Ð¸Ð¼Ð¸ÑÐµÑÐºÐ¸Ñ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ¾Ð² Ð¸ Ð¾ÑÐ·ÑÐ²Ð¾Ð² ÐºÐ»Ð¸ÐµÐ½ÑÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Prediction of wine quality based on chemical components and customer reviews
Creators: name: ÐÐ°ÑÑÐ¸Ð½Ð°, ÐÐ°Ð»ÐµÑÐ¸Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð²Ð¸Ð½Ð°
ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ
Flask
Pandas
Matplotlib
machine learning
wine quality
random forest

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description: The aim of the work is to develop a system for predicting the quality of wine based on its chemical characteristics and customer reviews using machine learning methods. This will allow winemakers to improve product quality and optimize business processes. Tasks addressed during the work: 1. Data collection and preprocessing. 2. Building and training a random forest model for predicting wine quality. 3. Evaluating the effectiveness of the model using various metrics. 4. Developing a web application to demonstrate the models functionality and predict wine quality based on user input. The constructed random forest model demonstrated high accuracy and stability in predictions, as evidenced by an average recall of 0.92 and a test recall of 0.95. The random forest model accounts for nonlinear dependencies between the chemical parameters of wine and its quality, enabling more accurate predictions. The confusion matrix showed that the model correctly classified most samples, minimizing both false positives and false negatives. Pandas and Matplotlib libraries were used for data visualization and analysis. The application provides producers with the opportunity to respond promptly to predictions, including enhancing marketing efforts in the event of low product quality. For example, if the prediction indicates that the wine quality is below expectations, the producer can invest more in advertising campaigns to boost sales. Additionally, data analysis allows identifying key factors affecting wine quality and making appropriate adjustments to the production process. This contributes to improving overall business efficiency and helps producers remain competitive in the market.