[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117344-en":3,"doc-seo-117344-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117344,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Wine Quality Prediction using Machine Learning - IJMRSET Research Paper","Wine quality prediction is an important problem in the wine industry, enabling producers and consumers to assess quality through objective chemical measurements rather than slow, subjective tasting. The study reviews multiple machine learning regression algorithms, describes the Wine Quality Dataset with key physicochemical features, and outlines preprocessing, train-test splitting, and model evaluation. Results compare models using MAE, MSE, RMSE, and R² to determine predictive effectiveness and support improved wine classification decisions.","International Journal of Multidisciplinary  \nResearch in Science, Engineering and Technology  \n(A Monthly, Peer Reviewed, Refereed, Scholarly Indexed, Open Access Journal)  \nImpact Factor: 8.206 Volume 8, Issue 2, February 2025  \n| © 2025 IJMRSET | Volume 8, Issue 2, February 2025| DOI: 10.15680/IJMRSET.2025.0802056 |\n| --- |\n|  |\n| Wine Quality Prediction using Machine Learning\u003Cbr>Prajwal Wadghule, Abhishek Rathor\u003Cbr>Department of Information Technology, AISSMS ’s Polytechnic, Pune, Maharashtra, India\u003Cbr>ABSTRACT: Wine quality prediction is a significant task in the wine industry, as it helps producers and consumers determine the quality of a wine based on its chemical properties. Traditional methods of evaluating wine quality are subjective and time-consuming, relying on human tasters. However, with the advancement of machine learning (ML), it is now possible to predict wine quality in a more objective, scalable, and efficient manner. This paper explores various machine learning algorithms for predicting wine quality, evaluates their performance, and demonstrates how these models can be applied to improve wine classification systems.\u003Cbr>I. INTRODUCTION\u003Cbr>Wine quality is traditionally assessed by professional sommeliers or tasters, who evaluate the wine based on various sensory attributes such as taste, aroma, and color. However, these subjective evaluations can vary and are not always reliable. The advent of machine learning provides an opportunity to predict wine quality based on objective chemical features such as alcohol content, acidity, pH levels, and more.\u003Cbr>The objective of this paper is to explore how machine learning techniques can be used to predict the quality of wine from various chemical features, enhancing the decision-making process in wine production, marketing, and consumer choice.\u003Cbr>Dataset Description\u003Cbr>The dataset used in this study is the Wine Quality Dataset, which consists of red and white wine data. The dataset includes the following features:\u003Cbr>1. Fixed Acidity: The amount of fixed acids (e.g., tartaric acid) in the wine.\u003Cbr>2. Volatile Acidity: The amount of volatile acids (e.g., acetic acid) .\u003Cbr>3. Citric Acid: The amount of citric acid in the wine.\u003Cbr>4. Residual Sugar: The amount of sugar remaining after fermentation.\u003Cbr>5. Chlorides: The amount of chloride in the wine.\u003Cbr>6. Free Sulfur Dioxide: The amount of free sulfur dioxide.\u003Cbr>7. Total Sulfur Dioxide: The total amount of sulfur dioxide.\u003Cbr>8. Density: The density of the wine.\u003Cbr>9. pH: The acidity level of the wine.\u003Cbr>10. Sulphates: The amount of sulphates in the wine.\u003Cbr>11. Alcohol: The percentage of alcohol in the wine.\u003Cbr>12. Quality: The target variable, representing the quality of the wine (a score from 0 to 10) .\u003Cbr>II. METHODOLOGY\u003Cbr>Machine Learning Models\u003Cbr>The following machine learning algorithms are used to predict wine quality:\u003Cbr>1. Linear Regression (LR): A linear approach to modeling the relationship between the features and the target variable.\u003Cbr>2. Decision Tree Regressor (DTR): A tree-based model that splits data into subsets based on feature values.\u003Cbr>3. Random Forest Regressor (RFR): An ensemble method that combines multiple decision trees to improve accuracy and robustness.\u003Cbr>4. Support Vector Regressor (SVR): A model that uses a hyperplane to separate data points and predict continuous values.\u003Cbr>5. Gradient Boosting Regressor (GBR): An ensemble method that builds models sequentially, focusing on correcting errors from previous models.\u003Cbr>IJMRSET © 2025 | An ISO 9001:2008 Certified Journal | 986 |\n\n\n| © 2025 IJMRSET | Volume 8, Issue 2, February 2025| DOI: 10.15680/IJMRSET.2025.0802056 |\n| --- |\n|  |\n| Evaluation Metrics\u003Cbr>The models will be evaluated using the following metrics:\u003Cbr>• Mean Absolute Error (MAE): The average of the absolute differences between predicted and actual values.\u003Cbr>• Mean Squared Error (MSE): The average of the squared differences between predicted and actual values.\u003Cbr>• Root Me","cbCaiuraZUj5zrJt","https://ap.wps.com/l/cbCaiuraZUj5zrJt","pdf",1703069,1,6,"English","en",105,"# ABSTRACT\n# I. INTRODUCTION\n## Dataset Description\n# II. METHODOLOGY\n## Machine Learning Models\n## Evaluation Metrics\n## Data Preprocessing\n## Model Training and Evaluation\n# III. RESULTS\n## Data Preprocessing\n## Model Performance\n## Interpretation of Results\n## Model Tuning","[{\"question\":\"What problem does the paper address in the wine industry?\",\"answer\":\"The paper targets wine quality prediction, aiming to replace subjective and time-consuming tasting with objective prediction from chemical properties.\"},{\"question\":\"Which chemical features are included in the dataset?\",\"answer\":\"The dataset includes fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free and total sulfur dioxide, density, pH, sulphates, alcohol, and the target quality score (0–10).\"},{\"question\":\"How are the machine learning models evaluated?\",\"answer\":\"Models are evaluated with MAE, MSE, RMSE, and R² after preprocessing, including missing-value handling, feature scaling, and an 80%/20% train-test split.\"}]","Wine Quality Prediction using Machine Learning - IJMRSET Research Paper | PDF",1785675291,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"wine-quality-prediction-using-machine-learning-ijmrset-research-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/wine-quality-prediction-using-machine-learning-ijmrset-research-paper/117344/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in the wine industry?","Question",{"text":75,"@type":76},"The paper targets wine quality prediction, aiming to replace subjective and time-consuming tasting with objective prediction from chemical properties.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which chemical features are included in the dataset?",{"text":80,"@type":76},"The dataset includes fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free and total sulfur dioxide, density, pH, sulphates, alcohol, and the target quality score (0–10).",{"name":82,"@type":73,"acceptedAnswer":83},"How are the machine learning models evaluated?",{"text":84,"@type":76},"Models are evaluated with MAE, MSE, RMSE, and R² after preprocessing, including missing-value handling, feature scaling, and an 80%/20% train-test split.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]