[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120349-en":3,"doc-seo-120349-105":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},120349,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","PREDICT THE SUCCESS OF CLEAN ENERGY STARTUPS USING A MACHINE LEARNING APPROACH - Master Thesis","The master thesis analyzes what drives the success of clean energy startups and provides a data-driven way to predict outcomes. It reviews general, financing, external, and founding-team factors, then examines global clean energy startup environment considerations and implications for investors. A supervised machine learning pipeline defines success, builds and fine-tunes an XGBoost classification model, evaluates performance using defined metrics, and interprets results through global and local interpretability methods, supported by structured data preprocessing and descriptive analysis.","WISEflow Europe/Oslo(CEST)  \n01 Jul 2024  \nHandelshøyskolen BIGRA 1 9 7 0 3 Master  \nT hes is  \nF i n a l T h e s i s M a s t e r o f S c i e  c 1 0 0 %  \nP r e d e f i n e r t i n f o r m a s j o n  \nStartdato: 08-01-2024 09:00 CET  \nSluttdato: 01-07-2024 12:00 CEST  \nEksamensform: T  \nTermin: 202410  \nVurderingsform: Norsk 6-trinns skala (A-F)  \nFlowkode: 202410||11436||IN00||W||T  \nExternal assessor: External assessor 1  \nInternal assessor: Internal assessor 1  \nD e l t a k e r  \n\n| Navn: Huyen\u003Cbr>Nguyen | Thi\u003Cbr>og |\n| --- | --- |\n\nCharlotte Asifa Nanyaro  \n\n| I n f o r m a s j o n f r a d e l t a k e r\u003Cbr>Tittel *: PREDICT THE SUCCESS OF CLEAN ENERGY STARTUPS USING A MACHINE LEARNING APPROACH Navn på veileder *: Sheryl Winston Smith |  |  |  |\n| --- | --- | --- | --- |\n| Inneholder besvarelsenkonfidensielt materiale?: | Nei | Kan besvarelsenoffentliggjøres?: | Ja |\n\nG r u p p e  \nGruppenavn: (Anonymisert)  \nGruppenummer: 207  \nAndre medlemmer igruppen:  \nMaster Thesis  \nPREDICT THE SUCCESS OF CLEAN ENERGY STARTUPS USING A MACHINE LEARNING APPROACH  \nHand-in date:  \n01.07.2024  \nSupervisor: Sheryl Winston Smith  \nCampus: BI Oslo  \nExamination code and name: GRA 1970 Master Thesis  \nProgramme:  \nMaster of Science in Entrepreneurship and Innovation  \nContents  \nChapter 1. INTRODUCTION........................................................................... 1  \nChapter 2. LITERATURE REVIEW ............................................................... 6  \n2.1. General Factors ............................................................................................... 6  \n2.1.1. Company Variables ................................................................................................... 7  \n2.1.2. Financing Variables .................................................................................................. 8  \n2.1.3. External Variables ..................................................................................................... 9  \n2.1.4. Founding Team Variables ....................................................................................... 10  \n2.2. Industry Specific Factors.............................................................................. 12  \n2.2.1. Global Review......................................................................................................... 12  \n2.2.2. Regional and Country review.................................................................................. 14  \n2.3. Methodologies Literature Review ................................................................ 16  \nChapter 3. CONTEXT: GLOBAL CLEAN ENERGY STARTUP ENVIRONMENT .................................................................................................19  \n3.1. Global Clean Energy Startup Environment ............................................... 19  \n3.2. Sector-Specific Analysis and Global Impact............................................... 20  \n3.3. Practical Implications for Investors ............................................................ 20  \nChapter 4. METHODOLOGY ........................................................................22  \n4.1. Defining success ............................................................................................. 22  \n4.2. Classification Machine Learning Model ..................................................... 22  \n4.2.1. Extreme Gradient Boosting ..................................................................................... 22  \n4.2.2. Fine-tune XGBoost model ...................................................................................... 24  \n4.3. Performance Metrics..................................................................................... 25  \n4.4. Machine Learning Interpretability.............................................................. 29  \n4.4.1. Global Interpretability ............................................................................................. 30  \n4.4.2. Local Inte","cbCaiujHv4D22nMJ","https://ap.wps.com/l/cbCaiujHv4D22nMJ","pdf",1531771,1,84,"English","en",105,"# Chapter 1. INTRODUCTION\n# Chapter 2. LITERATURE REVIEW\n## 2.1. General Factors\n## 2.2. Industry Specific Factors\n## 2.3. Methodologies Literature Review\n# Chapter 3. CONTEXT: GLOBAL CLEAN ENERGY STARTUP ENVIRONMENT\n## 3.1. Global Clean Energy Startup Environment\n## 3.2. Sector-Specific Analysis and Global Impact\n## 3.3. Practical Implications for Investors\n# Chapter 4. METHODOLOGY\n## 4.1. Defining success\n## 4.2. Classification Machine Learning Model\n## 4.3. Performance Metrics\n## 4.4. Machine Learning Interpretability\n# Chapter 5. DATA\n## 5.1. Data Source\n## 5.2. Feature Selection\n## 5.3. Data preprocessing\n## 5.4. Summary Statistics\n## 5.5. 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