Startup Success Prediction¶

Problem¶

Startups are businesses in their early stage of development which rely heavily on funding from venture capitalists and investors. Investors seek to find accurate ways to predict startup success in order to determine in whom to invest their money in. Assessing the success of startups, particularly from the viewpoint of venture capitalists, can prove to be an arduous and costly task. Even though venture capitalists and other investors possess valuable expertise, it can be challenging to thoroughly evaluate startup success beyond mere intuition and rudimentary factors.

For further context and understanding, this article can be read.

Solution¶

This project aims to address the challenge of assessing startup success by predicting whether a currently operating startup is likely to succeed or fail. Success is defined as a startup's founders receiving a significant amount of money through an Initial Public Offering or a Merger and Acquisition, while failure is defined as a startup closing or shutting down. The project goal is to leverage patterns in industry trends, investment insights, and individual company information to predict a startup's success using existing data. It's important to note that this project is not intended to be the sole deciding factor for investors or venture capitalists but rather a tool to support or challenge their investment decisions.

Solution - Machine Learning Method¶

I plan to use a binary classification machine learning model to predict startup success or failure based on characteristics like funding amount and industry sector. I will preprocess the data, train and test with classification algorithms, and evaluate their performance using metrics like accuracy and precision. The best-performing model can then predict success or failure for new startups.

Impact¶

A machine learning model that predicts startup success and failure can offer insights to investors, benefit startup founders, and impact the broader startup ecosystem. By analyzing industry trends and company data, it can help investors identify high-potential startups and founders understand success factors. It can also contribute to a more efficient and sustainable ecosystem, identify emerging industries and technologies, and offer investment opportunities beyond the startup space.

A concern is that the model may not predict future events, such as economic downturns or disruptive technologies, that can impact a startup's success. It's important to use the model as a tool alongside human expertise and judgment, rather than relying solely on it for decision-making.

Dataset¶

Details¶

We will use a Kaggle Dataset of Startup Success to observe the following features for each startup:

age_first_funding_year (quantitative): age of startup in years when it received its first funding round
age_last_funding_year (quantitative): age of startup in years when it received its last funding round
relationships (quantitative): number of known relationships the startup has with individuals, organizations, or other startups
funding_rounds (quantitative): total number of funding rounds the startup has received
funding_total_usd (quantitative): total amount of funding the startup has received in US dollars
milestones (quantitative): total number of milestones achieved by the startup
age_first_milestone_year (quantitative): age of the startup in years when it achieved its first milestone
age_last_milestone_year (quantitative): age of the startup in years when it achieved its last milestone
state (categorical): state or region that startup is located
industry_type (categorical): industry/sector startup belongs
has_VC (categorical): if startup has received venture capital funding
has_angel (categorical): if startup has received angel investor funding
has_roundA (categorical): if startup has received round A funding
has_roundB (categorical): if startup has received round B funding
has_roundC (categorical): if startup has received round C funding
has_roundD (categorical): if startup has received round D funding
avg_participants (quantitative): average number of participants in each funding round
is_top500 (categorical): if startup is ranked among the top 500 by website traffic
status(acquired/closed) (categorical): indicating whether the startup has been acquired by another organization (succeeded) or closed

The data presented above provides sufficient information to make significant progress on this problem. It includes crucial factors such as the startup's industry, the amount and types of funding it has received, and a clear indicator of success - whether it was acquired by another organization or closed down.

Below are the first few rows via markdown...

Unnamed: 0	state_code	latitude	longitude	zip_code	id	city	Unnamed: 6	name	labels	founded_at	closed_at	first_funding_at	last_funding_at	age_first_funding_year	age_last_funding_year	age_first_milestone_year	age_last_milestone_year	relationships	funding_rounds	funding_total_usd	milestones	state_code.1	is_CA	category_code	is_software	is_web	is_enterprise	is_gamesvideo	is_othercategory	object_id	has_VC	has_angel	has_roundA	has_roundB	has_roundC	has_roundD	avg_participants	is_top500	status
1005	CA	42.35888	-71.05682	92101	c:6669	San Diego		Bandsintown	1	1/1/2007		4/1/2009	1/1/2010	2.2493	3.0027	4.6685	6.7041	3	3	375000	3	CA	1	music	0	0	0	0	1	c:6669	0	1	0	0	0	0	1.0	0	acquired
204	CA	37.238916	-121.973718	95032	c:16283	Los Gatos		TriCipher	1	1/1/2000		2/14/2005	12/28/2009	5.126	9.9973	7.0055	7.0055	9	4	40100000	1	CA	1	enterprise	0	0	1	0	0	c:16283	1	0	0	1	1	1	4.75	1	acquired
1001	CA	32.901049	-117.192656	92121	c:65620	San Diego	San Diego CA 92121	Plixi	1	3/18/2009		3/30/2010	3/30/2010	1.0329	1.0329	1.4575	2.2055	5	1	2600000	2	CA	1	web	0	1	0	0	0	c:65620	0	0	1	0	0	0	4.0	1	acquired
738	CA	37.320309	-122.05004	95014	c:42668	Cupertino	Cupertino CA 95014	Solidcore Systems	1	1/1/2002		2/17/2005	4/25/2007	3.1315	5.3151	6.0027	6.0027	5	3	40000000	1	CA	1	software	1	0	0	0	0	c:42668	0	0	0	1	1	1	3.3333	1	acquired
1002	CA	37.779281	-122.419236	94105	c:65806	San Francisco	San Francisco CA 94105	Inhale Digital	0	8/1/2010	10/1/2012	8/1/2010	4/1/2012	0.0	1.6685	0.0384	0.0384	2	2	1300000	1	CA	1	games_video	0	0	0	1	0	c:65806	1	1	0	0	0	0	1.0	1	closed

In [1]:

# below is the loaded data, download data from the website to view dataframe
import pandas as pd

data = pd.read_csv("startup data.csv")
df = pd.DataFrame(data)
df

Out[1]:

	Unnamed: 0	state_code	latitude	longitude	zip_code	id	city	Unnamed: 6	name	labels	...	object_id	has_VC	has_angel	has_roundA	has_roundB	has_roundC	has_roundD	avg_participants	is_top500	status
0	1005	CA	42.358880	-71.056820	92101	c:6669	San Diego	NaN	Bandsintown	1	...	c:6669	0	1	0	0	0	0	1.0000	0	acquired
1	204	CA	37.238916	-121.973718	95032	c:16283	Los Gatos	NaN	TriCipher	1	...	c:16283	1	0	0	1	1	1	4.7500	1	acquired
2	1001	CA	32.901049	-117.192656	92121	c:65620	San Diego	San Diego CA 92121	Plixi	1	...	c:65620	0	0	1	0	0	0	4.0000	1	acquired
3	738	CA	37.320309	-122.050040	95014	c:42668	Cupertino	Cupertino CA 95014	Solidcore Systems	1	...	c:42668	0	0	0	1	1	1	3.3333	1	acquired
4	1002	CA	37.779281	-122.419236	94105	c:65806	San Francisco	San Francisco CA 94105	Inhale Digital	0	...	c:65806	1	1	0	0	0	0	1.0000	1	closed
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
918	352	CA	37.740594	-122.376471	94107	c:21343	San Francisco	NaN	CoTweet	1	...	c:21343	0	0	1	0	0	0	6.0000	1	acquired
919	721	MA	42.504817	-71.195611	1803	c:41747	Burlington	Burlington MA 1803	Reef Point Systems	0	...	c:41747	1	0	0	1	0	0	2.6667	1	closed
920	557	CA	37.408261	-122.015920	94089	c:31549	Sunnyvale	NaN	Paracor Medical	0	...	c:31549	0	0	0	0	0	1	8.0000	1	closed
921	589	CA	37.556732	-122.288378	94404	c:33198	San Francisco	NaN	Causata	1	...	c:33198	0	0	1	1	0	0	1.0000	1	acquired
922	462	CA	37.386778	-121.966277	95054	c:26702	Santa Clara	Santa Clara CA 95054	Asempra Technologies	1	...	c:26702	0	0	0	1	0	0	3.0000	1	acquired

923 rows × 49 columns

Citation¶

KC, M. (2020, September 16). Startup success prediction. Kaggle. Retrieved February 26, 2023, from https://www.kaggle.com/datasets/manishkc06/startup-success-prediction

Additional Sources¶

Baldridge, R. (2023, January 4). What is a startup? the ultimate guide. Forbes. Retrieved February 26, 2023, from https://www.forbes.com/advisor/business/what-is-a-startup/