Startups Survival Using Data Science
By: Sagar Daswani
Tags: Crowdfunding, Artificial Intelligence, Social Media, Data Science, Startup

Summary:

This article includes data scraped from angelist.io, a publicly available startup platform with a tabular view of startups with features such as size, start/joined date, location, and market. Later social media data (followers and following) was scraped from twitter for every startup company. The data is divided into two parts, one that includes social media features and others don’t - in order to compare model performances. I used the Chi-sq test to get the most important features from location and market, as it is intuitive that geographical location, as well as market competitiveness, has an effect on startups. Below is the brief outline of project flow:

Raw Data — scraped from Angelist(angel.co) and twitter

Data Cleaning — one hot encoding along with feature selection using Chi-Sq test

Logistic Regression model — choosing best accuracy on a validation set

Model comparison and evaluation.

Data Ingestion/collection:

Getting a startup data was a bit tricky, after researching I got my hands on angelist.co data. As data is in quite a good shape and freely accessible. Below is the snapshot of the web page from which I scraped data:

Inspiration and code credits for scraping Angel.co data to xiaoxiang-ma

Angellist Companies Webpage — https://angel.co/companies

The above page has quite a good amount of data in terms of startup companies, and I have scraped unique startups from all different markets based in the US.

Features scraped are Company name, Joined Date, Market, Website link, Employee Size, Stage, and Total Raised amount.

Similarly using Selenium and BeautifulSoup(web scraping package in python), I was able to extract a company’s count of followers and following. Link to Jupyter notebook!

Predictive model motivation:

I was skeptical about implementing a linear regression model based on raised amounts or converting this into a classification model to predict if a startup is good or not good. I got my hands on a study by Techcrunch regarding the fundraising impact of one round to another.

Based on the chart below we can see that the probability of fundraising in Series A is the highest when companies raised >= 2 million in their Seed/pre-series A round. In other words, we can say that a seed-stage company that raises $2 Million should have a higher chance of survival than the one that raises less than $2 million. Thus, this threshold will help us evaluate the goodness of companies.

The probability of raising money for Series B round is a lot flat, the study shows that fundraising amount has less of an impact on advancing to the next stage. Still, I am considering the spike at $12 Million, so following the same set of steps from the previous one to evaluate the goodness of startups.

Data Cleaning and Preprocessing:

Eliminating null values and repeated rows along with updating data types. Converting location and market features into one-hot encoding for machine learning models. Created years column by getting the time difference between the current time and the joined time. In order to convert ordinal data of company size to numeric, I just got the middle point of the size range.

Generated new columns/target class “raised_2mil” and “raised_12mil” as a binary outcome from a raised column in data. Filtered stage by “seed” and “seriesA” and created two data sets.

So now to break down we have 4 ml models in total:

Models WITHOUT followers: “Seed” and “SeriesA” Models WITH followers: “Seed” and “SeriesA”

Web-Scraped Dataset

Predictive Model:

Considering the nature of input features, most of them are binary generated by one-hot encoding. As there are many unique markets and locations our data has turned into high dimensionality, hence not very suitable for tree-based models. Thus moving forward with logistic regression for better performance.

Feature Selection:

In most cases binary features are very skewed towards zero, for example, data has one location as Menifee which has exactly 1 row and rests all 0. So clearly this variable is less important than others.

So in order to select the top 10 features from our data I am going to use the Chi-Squared test for feature selection. Below is the list of 10 features I was able to select that contribute to most of the cases.

Selected Features List

Model performances:

Now time to train our logistic model on a selected set of features, and a ROC curve is plotted using validation sets. Each of these ROC curves has a metric AUC(Area Under Curve). Values near to 1 show better performance in predicting outcomes.

ROC curves for Seed Model on training data

ROC curve for Series A model on training data

Model Evaluation and Discussion:

Time to evaluate our model on test data,

Seed Models results:

Seed Model Evaluation Matrix Barplot

From the above plot, we can see that there is almost a 5% increase in all the evaluation matrices. Test accuracy jumped from 70% to 75% by just including the follower column from the social media feature list.

Series A Models results:

Series A Model Evaluation Matrix Barplot

In Series A models there is a 4% increase in Test Accuracy as well as Total Negative Ratio, a slight decrease in Total Positive Ratio, but there is an overall increase.

Conclusion and Future Steps:

As we saw our model evaluations based on including social media features (i.e., Followers), there is definitely an impact of social media on startup survival or sustainability for the next funding round. Accuracy is decent, around 75%, as we know the nature of a startup is unpredictable, and still, I was able to achieve accuracy better than flipping a coin.

This makes me wonder if we include more social media features such as engagement rate, click ratios, count of profile mentions across different social media platforms such as Facebook and Instagram. If given enough resources, I can definitely look into including all those features and come up with a better model to support my hypothesis in the future. What if we can come up with a strategy on how we can help early-stage startups to improve their marketing campaign and achieve a certain threshold of a social media KPI that makes their chances of achieving target crowdfunding easily along with a strong network! (MayBe!)

I would say this hypothesis is just a starting point on how big one can make of this project. I know this project looks very streamlined based on Crunchbase’s article but the addition of social media angle gave it a meaningful insight. Still, there are so many mysteries and techniques in the crowdfunding realm that simply might not be learnable by logistic regression.