{"id":"https://openalex.org/W7124174398","doi":"https://doi.org/10.4018/979-8-3373-6746-0.ch006","title":"Machine Learning-Based Budget Prediction for Quantitative Decision-Making","display_name":"Machine Learning-Based Budget Prediction for Quantitative Decision-Making","publication_year":2026,"publication_date":"2026-01-14","ids":{"openalex":"https://openalex.org/W7124174398","doi":"https://doi.org/10.4018/979-8-3373-6746-0.ch006"},"language":"ng","primary_location":{"id":"doi:10.4018/979-8-3373-6746-0.ch006","doi":"https://doi.org/10.4018/979-8-3373-6746-0.ch006","is_oa":false,"landing_page_url":"https://doi.org/10.4018/979-8-3373-6746-0.ch006","pdf_url":null,"source":{"id":"https://openalex.org/S4306463409","display_name":"IGI Global eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advancements in Numerical Analysis and Quantitative Decision-Making","raw_type":"book-chapter"},"volume":{"id":"https://openalex.org/W7124154126","display_name":"Advancements in Numerical Analysis and Quantitative Decision-Making"},"type":"book-chapter","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123037193","display_name":"Yasmine El-Bakkouri","orcid":"https://orcid.org/0009-0008-8990-209X","observed_orcids":["https://orcid.org/0009-0008-8990-209X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yasmine El-Bakkouri","raw_affiliation_strings":["Research Laboratory in Management Sciences of Organizations, National School of Business and Management, Morocco"],"raw_orcid":"https://orcid.org/0009-0008-8990-209X","affiliations":[{"raw_affiliation_string":"Research Laboratory in Management Sciences of Organizations, National School of Business and Management, Morocco","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098735652","display_name":"Imane Aitouhanni","orcid":"https://orcid.org/0009-0004-1400-8027","observed_orcids":["https://orcid.org/0009-0004-1400-8027"]},"institutions":[{"id":"https://openalex.org/I126477371","display_name":"Mohammed V University","ror":"https://ror.org/00r8w8f84","country_code":"MA","type":"education","lineage":["https://openalex.org/I126477371"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Imane Aitouhanni","raw_affiliation_strings":["Mohammed V University, Rabat, Morocco"],"raw_orcid":"https://orcid.org/0009-0004-1400-8027","affiliations":[{"raw_affiliation_string":"Mohammed V University, Rabat, Morocco","institution_ids":["https://openalex.org/I126477371"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070548549","display_name":"Serkan Derici","orcid":"https://orcid.org/0000-0003-2581-6770","observed_orcids":["https://orcid.org/0000-0003-2581-6770"]},"institutions":[{"id":"https://openalex.org/I1281109512","display_name":"Nev\u015fehir Hac\u0131 Bekta\u015f Veli University","ror":"https://ror.org/019jds967","country_code":"TR","type":"education","lineage":["https://openalex.org/I1281109512"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Serkan Derici","raw_affiliation_strings":["Nev\u015fehir Hac\u0131 Bekta\u015f Veli University, Turkey"],"raw_orcid":"https://orcid.org/0000-0003-2581-6770","affiliations":[{"raw_affiliation_string":"Nev\u015fehir Hac\u0131 Bekta\u015f Veli University, Turkey","institution_ids":["https://openalex.org/I1281109512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108825239","display_name":"Jalal Azegagh","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I3121676899","display_name":"Universit\u00e9 Ibn-Tofail","ror":"https://ror.org/02wj89n04","country_code":"MA","type":"education","lineage":["https://openalex.org/I3121676899"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Jalal Azegagh","raw_affiliation_strings":["National School of Commerce and Management, University Ibn Tofail, Kenitra, Morocco"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National School of Commerce and Management, University Ibn Tofail, Kenitra, Morocco","institution_ids":["https://openalex.org/I3121676899"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04321872,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"155","last_page":"176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.8398000001907349,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.8398000001907349,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.1103999987244606,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.01510000042617321,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/construction-costs","display_name":"construction costs","score":0.5619999766349792},{"id":"https://openalex.org/keywords/film-budget","display_name":"film production budget","score":0.996999979019165},{"id":"https://openalex.org/keywords/regression-models","display_name":"regression models","score":0.5630000233650208},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"gradient boosting","score":0.7760000228881836},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9959999918937683},{"id":"https://openalex.org/keywords/linear-regression","display_name":"linear regression","score":0.8790000081062317},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.8090000152587891},{"id":"https://openalex.org/keywords/nonlinear-relationship","display_name":"nonlinear relationship","score":0.6589999794960022},{"id":"https://openalex.org/keywords/financial-decision-making","display_name":"financial decision-making","score":0.8009999990463257}],"concepts":[{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6765000224113464},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6481999754905701},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6335999965667725},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5167999863624573},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5041999816894531},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.46160000562667847},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4544000029563904},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4431999921798706},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4203000068664551},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.40470001101493835},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.37389999628067017},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.323199987411499},{"id":"https://openalex.org/C46889948","wikidata":"https://www.wikidata.org/wiki/Q2755024","display_name":"Nonlinear regression","level":3,"score":0.323199987411499},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3102000057697296},{"id":"https://openalex.org/C2984755018","wikidata":"https://www.wikidata.org/wiki/Q17118374","display_name":"Nonlinear model","level":3,"score":0.3084000051021576},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.298799991607666},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.2980000078678131},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4018/979-8-3373-6746-0.ch006","doi":"https://doi.org/10.4018/979-8-3373-6746-0.ch006","is_oa":false,"landing_page_url":"https://doi.org/10.4018/979-8-3373-6746-0.ch006","pdf_url":null,"source":{"id":"https://openalex.org/S4306463409","display_name":"IGI Global eBooks","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"ebook platform"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advancements in Numerical Analysis and Quantitative Decision-Making","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W432175119","https://openalex.org/W1992236347","https://openalex.org/W2024188017","https://openalex.org/W2025101959","https://openalex.org/W2035923633","https://openalex.org/W2071607705","https://openalex.org/W2416069211","https://openalex.org/W2576683119","https://openalex.org/W2891092129","https://openalex.org/W2897312458","https://openalex.org/W2914665148","https://openalex.org/W2920551025","https://openalex.org/W2930018783","https://openalex.org/W2934302500","https://openalex.org/W2989845548","https://openalex.org/W3002519755","https://openalex.org/W3017205423","https://openalex.org/W3048488037","https://openalex.org/W3059742641","https://openalex.org/W3114183436","https://openalex.org/W3124373098","https://openalex.org/W4200061424","https://openalex.org/W4281696066","https://openalex.org/W4286256308","https://openalex.org/W4288050009","https://openalex.org/W4303648677","https://openalex.org/W4319297962","https://openalex.org/W4321111680","https://openalex.org/W4376278176","https://openalex.org/W4381425968","https://openalex.org/W4383340187","https://openalex.org/W4386826322","https://openalex.org/W4392291652","https://openalex.org/W4392896772","https://openalex.org/W4393972967","https://openalex.org/W4395089181","https://openalex.org/W4398222795","https://openalex.org/W4401562608","https://openalex.org/W4402378055","https://openalex.org/W4404224037","https://openalex.org/W4405925452","https://openalex.org/W4406407657","https://openalex.org/W4407242328","https://openalex.org/W4409488714","https://openalex.org/W4409947736","https://openalex.org/W4413645249","https://openalex.org/W4415210506","https://openalex.org/W4415767055"],"related_works":["https://openalex.org/W4389636284","https://openalex.org/W4411283280","https://openalex.org/W7217051458","https://openalex.org/W7218623044","https://openalex.org/W3188209726","https://openalex.org/W4404292724","https://openalex.org/W7218041683","https://openalex.org/W7167357752","https://openalex.org/W4214601896","https://openalex.org/W7023920701"],"abstract_inverted_index":{"Accurate":[0],"budget":[1,45,116],"prediction":[2,46],"is":[3],"a":[4,128],"fundamental":[5],"challenge":[6],"for":[7,44,131],"quantitative":[8,133],"decision-making":[9],"in":[10],"both":[11],"public":[12],"and":[13,31,56,64,70,79,107,119],"private":[14],"sectors.":[15],"Traditional":[16],"statistical":[17],"approaches":[18],"often":[19],"fail":[20],"to":[21,53],"capture":[22],"the":[23,37,80,103,112,121],"complex,":[24],"nonlinear":[25,113],"relationships":[26],"that":[27,89],"characterize":[28],"modern":[29],"financial":[30,134],"economic":[32],"data.":[33],"This":[34],"chapter":[35],"investigates":[36],"effectiveness":[38],"of":[39,82,115,124],"machine":[40,125],"learning-based":[41],"regression":[42],"models":[43,67,91],"using":[47,72],"three":[48],"heterogeneous":[49],"real-world":[50],"datasets":[51],"related":[52],"construction":[54],"costs":[55],"film":[57],"production":[58],"budgets.":[59],"Linear":[60],"Regression,":[61],"Random":[62],"Forest,":[63],"Gradient":[65,100],"Boosting":[66,101],"are":[68],"implemented":[69],"evaluated":[71],"standard":[73],"performance":[74],"metrics,":[75],"including":[76],"MAE,":[77],"RMSE,":[78],"coefficient":[81],"determination":[83],"(R2).":[84],"The":[85,109],"experimental":[86],"results":[87],"demonstrate":[88],"ensemble-based":[90],"significantly":[92],"outperform":[93],"linear":[94],"methods":[95],"across":[96],"all":[97],"datasets,":[98],"with":[99],"providing":[102],"highest":[104],"predictive":[105],"accuracy":[106],"robustness.":[108],"findings":[110],"confirm":[111],"nature":[114],"estimation":[117],"problems":[118],"highlight":[120],"strong":[122],"potential":[123],"learning":[126],"as":[127],"numerical":[129],"tool":[130],"supporting":[132],"decision-making.":[135]},"counts_by_year":[],"updated_date":"2026-10-10T09:11:00.788812","created_date":"2026-01-15T00:00:00"}