Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction.
To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential and likelihood of success. This thesis explores whether online data about a company, particularly general company data, previous funding events, published news articles, internet presence, and social media activity can be used to identify fast-growing startups. Data collected from Crunchbase, the Google Search API, and Twitter was used to predict whether a company will raise a round of funding within a fixed time horizon.
A total of ten machine learning models were evaluated and the CatBoost ensemble method achieved the best performance with precision, recall, and F1 scores of 0.663, 0.827, and 0.736 respectively for predicting funding within 3 years. The same ensem- ble method achieved F1 scores of 0.528, 0.683, 0.736, 0.763, and 0.777 at predicting funding 1-5 years into the future. The final objective was to predict whether a startup that had already raised an angel or seed round would raise another investment within a one-year horizon. The CatBoost model with a 0.75 cutoff achieved precision and F0.1 scores of 0.790 and 0.774, beating the results of previous work in this field.
","abstract_html":"<p>Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction.</p> <p>To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential and likelihood of success. This thesis explores whether online data about a company, particularly general company data, previous funding events, published news articles, internet presence, and social media activity can be used to identify fast-growing startups. Data collected from Crunchbase, the Google Search API, and Twitter was used to predict whether a company will raise a round of funding within a fixed time horizon.</p> <p>A total of ten machine learning models were evaluated and the CatBoost ensemble method achieved the best performance with precision, recall, and F1 scores of 0.663, 0.827, and 0.736 respectively for predicting funding within 3 years. The same ensem- ble method achieved F1 scores of 0.528, 0.683, 0.736, 0.763, and 0.777 at predicting funding 1-5 years into the future. The final objective was to predict whether a startup that had already raised an angel or seed round would raise another investment within a one-year horizon. The CatBoost model with a 0.75 cutoff achieved precision and F0.1 scores of 0.790 and 0.774, beating the results of previous work in this field.</p>","abstract_has_math":false,"creators":["Gavrilenko, Emily"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Foaad Khosmood","Computer Science","College of Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12-01T08:00:00Z","date_published":"2022-12-01T08:00:00Z","updated_at":"2026-07-24T01:32:13Z","subjects":["AI","Machine Learning","Artificial Intelligence","Startup","Computer Science","Artificial Intelligence and Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2022.119"],"render_values":[{"text":"10.15368/theses.2022.119","href":"https://doi.org/10.15368/theses.2022.119","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/2652","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Foaad Khosmood","Computer Science","College of Engineering"]},{"key":"dc:creator","label":"Author","values":["Gavrilenko, Emily"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2022-12-09T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["AI","Machine Learning","Artificial Intelligence","Startup","Computer Science","Artificial Intelligence and Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/2652","10.15368/theses.2022.119"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction.
To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential and likelihood of success. This thesis explores whether online data about a company, particularly general company data, previous funding events, published news articles, internet presence, and social media activity can be used to identify fast-growing startups. Data collected from Crunchbase, the Google Search API, and Twitter was used to predict whether a company will raise a round of funding within a fixed time horizon.
A total of ten machine learning models were evaluated and the CatBoost ensemble method achieved the best performance with precision, recall, and F1 scores of 0.663, 0.827, and 0.736 respectively for predicting funding within 3 years. The same ensem- ble method achieved F1 scores of 0.528, 0.683, 0.736, 0.763, and 0.777 at predicting funding 1-5 years into the future. The final objective was to predict whether a startup that had already raised an angel or seed round would raise another investment within a one-year horizon. The CatBoost model with a 0.75 cutoff achieved precision and F0.1 scores of 0.790 and 0.774, beating the results of previous work in this field.
"]},{"key":"dc:title","label":"Title","values":["Predicting Startup Success Using Publicly Available Data"]}]}],"canonical_facts":{"dc:contributor":["Foaad Khosmood","Computer Science","College of Engineering"],"dc:creator":["Gavrilenko, Emily"],"dc:date.available":["2022-12-09T08:00:00Z"],"dc:description.abstract":["Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction.
To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential and likelihood of success. This thesis explores whether online data about a company, particularly general company data, previous funding events, published news articles, internet presence, and social media activity can be used to identify fast-growing startups. Data collected from Crunchbase, the Google Search API, and Twitter was used to predict whether a company will raise a round of funding within a fixed time horizon.
A total of ten machine learning models were evaluated and the CatBoost ensemble method achieved the best performance with precision, recall, and F1 scores of 0.663, 0.827, and 0.736 respectively for predicting funding within 3 years. The same ensem- ble method achieved F1 scores of 0.528, 0.683, 0.736, 0.763, and 0.777 at predicting funding 1-5 years into the future. The final objective was to predict whether a startup that had already raised an angel or seed round would raise another investment within a one-year horizon. The CatBoost model with a 0.75 cutoff achieved precision and F0.1 scores of 0.790 and 0.774, beating the results of previous work in this field.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/2652","10.15368/theses.2022.119"],"dc:subject":["AI","Machine Learning","Artificial Intelligence","Startup","Computer Science","Artificial Intelligence and Robotics"],"dc:title":["Predicting Startup Success Using Publicly Available Data"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:32:13Z"}