[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117241-en":3,"doc-seo-117241-105":30,"detail-sidebar-cat-0-en-105":83},{"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},117241,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Machine Learning for B2B Manufacturing Price Prediction - Master’s Thesis 2023","B2B manufacturing pricing often relies on direct market research, but continuous analysis of transactional pricing and sales data can strengthen profitability. This thesis provides an overview of widely used machine learning algorithms, assessing decision trees, Bayesian networks, principal component regression, support vector regression, neural networks, linear regression, and Gaussian regression. It uses data visualization and correlation analysis to determine suitable modeling choices, then compares three selected models using a weighted decision matrix to evaluate practicality, constraints, and prediction performance.","MACHINE LEARNING FOR B2B MANUFACTURING PRICE PREDICTION  \nLappeenranta-Lahti University of Technology LUT  \nMaster’s Program in Computational Engineering, Master’s Thesis 2023  \nDaniela Maldonado Sada  \nExaminers: Professor Lassi Roininen  \nM.Sc. José Rodrigo Rojo García  \nABSTRACT  \nLappeenranta-Lahti University of Technology LUT School of Engineering Science  \nComputational Engineering  \nDaniela Maldonado Sada  \nMachine Learning for B2B Manufacturing Price Prediction  \nMaster’s thesis  \n2023  \n51 pages, 36 figures, 4 tables  \nExaminers: Professor Lassi Roininen and M.Sc. José Rodrigo Rojo García  \nKeywords: pricing, machine learning, regression, prediction  \nThe B2B industry encounters pricing challenges that usually require direct market research. However, real-time transactional data analysis can enhance business profitability by utilizing machine learning methods.  \nThe aim is to have an overview of various machine learning algorithms widely used intoday’s data-driven world. Among the models discussed are decision trees, Bayesian networks, principal component regression, support vector regression, neural networks, linear regression, and Gaussian regression. We will examine each model’s unique features to determine which is best for our data.  \nThis thesis analyzes data via visualization and correlation matrix to understand what typeof model we need according to our data. However, the primary objective is to assess the practicality and restrictions of each of the preselected models.  \nA weighted decision matrix will assist us to make the comparative analysis and select the optimal model from the three preselected models (decision trees, Gaussian processes, and linear regressions) . The findings of this study may be beneficial for scholars working in the areas of data science, statistics, and machine learning.  \nACKNOWLEDGEMENTS  \nI express my gratitude to my supervisors, Professor Lassi Roininen and M.Sc. José Rodrigo Rojo García, for their essential guidance throughout the execution of this thesis. I would want to extend appreciation to my spouse, mother, father, and sisters for their constant encouragement during my pursuit of the master’s degree. To my friends, both digitally and in person, who have followed me during this phase of my life.  \nLappeenranta, August 28, 2023  \nDaniela Maldonado Sada  \n4  \nCONTENTS  \n1 INTRODUCTION 5  \n1.1 Objectives and Challenges ......................... 6  \n2 DATA EXPLORATION AND PRE-PROCESSING 7  \n2.1 Visualization ................................ 7  \n2.1.1 Histogram .............................. 8  \n2.1.2 Correlation matrix ......................... 11  \n2.1.3 Minimum redundancy maximum relevance ............ 16  \n2.2 Type of variables .............................. 17  \n2.3 Selecting the variables ........................... 19  \n2.4 Categorical variables preprocessing .................... 22  \n2.5 Cleaning the data .............................. 24  \n3 BUILDING THE MODEL 25  \n3.1 Characteristics of models .......................... 25  \n3.2 Models for predicting prices ........................ 35  \n3.2.1 Decision tree ............................ 35  \n3.2.2 Gaussian process .......................... 36  \n3.2.3 Linear regression .......................... 37  \n3.3 Choosing the model ............................. 39  \n3.4 Tuning the model .............................. 44  \n4 CONCLUSION 47  \nREFERENCES 49  \n5  \n1 INTRODUCTION  \nThe business-to-business model (B2B) refers to the exchange of goods and services between businesses and organizations; an example could be a company contracting with another business to provide the raw materials required for manufacturing a product. As mentioned by Mark F [1], B2B is fully present in the primary sector, such as agriculture; it is predominant in the secondary sector, as in manufacturing companies; and occasionally, it is present in the tertiary sector, for example, in technology companies.  \nAs observed across various sectors, today’s conditi","cbCaifRoPm0Ofd9i","https://ap.wps.com/l/cbCaifRoPm0Ofd9i","pdf",9413960,1,51,"English","en",105,"# Introduction\n## Objectives and Challenges\n# Data Exploration and Pre-processing\n## Visualization\n## Type of variables\n## Selecting the variables\n## Categorical variables preprocessing\n## Cleaning the data\n# Building the Model\n## Characteristics of models\n## Models for predicting prices\n## Choosing the model\n## Tuning the model\n# Conclusion","[{\"question\":\"How does the thesis decide which model is most suitable?\",\"answer\":\"It analyzes data through visualization and correlation matrix to understand the data structure, then uses a weighted decision matrix to compare the three selected models and select the optimal one.\"}]","Machine Learning for B2B Manufacturing Price Prediction - Master’s Thesis 2023 | PDF",1785674631,129,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-for-b2b-manufacturing-price-prediction-masters-thesis-2023","",{"@graph":36,"@context":77},[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/machine-learning-for-b2b-manufacturing-price-prediction-masters-thesis-2023/117241/",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],{"name":72,"@type":73,"acceptedAnswer":74},"How does the thesis decide which model is most suitable?","Question",{"text":75,"@type":76},"It analyzes data through visualization and correlation matrix to understand the data structure, then uses a weighted decision matrix to compare the three selected models and select the optimal one.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,127],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":98,"slug":130},19,"General","general"]