Emilio Conde Data Science Portfolio

Predicting Startups Success

Note: The output is in an HTML file. For accurate visualization, please download the file.

This project examines the key differences between businesses started by one person and those started by a team of co-founders. The goal is to figure out what makes a company successful and what might cause it to close or face acquisition.

• Data was taken from Pitchbook Platform and extracted via web scraping through entrepreneur's LinkedIn profiles.
• The analysis includes differences between funding rounds, funding location, skill comparison across teams, and more.
• Model was built using logistic regression
• 90%+ accuracy across both training and testing datasets.
• Predictions were made on data from real companies

Time to move across rounds:

• Solo Founders can take up to twice as more months than co-founder teams when securing a funding round

Area Under the Curve for logistic regression model:

AUC for testing dataset	AUC for training dataset
94.1%	93.9%

Predictions on real world data:

Chance of Company A being successful	Chance of Company B being successful
87%	9%

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LinkedIn Company Demographics Scrapper

This project significantly enhanced the efficiency of data scraping from company profiles, providing tangible benefits to Endeavor Mexico.

• The code is fully prepared for use and deployment across different computers. Note for first-time Selenium users: Please ensure to install necessary libraries to prevent any errors.
• Code only needs a list with the URLs to retrieve in a .csv format.
• Added value: The scraper retrieves comprehensive employee demographic information, including locations, majors, skills, and occupations.
• Executing all the provided code will yield a clean, ready-to-analyze data frame for any required analysis.

Data Frame output glimpse:

Company Name	Industry	Location	Employees	N1_Country	Country1	N1_School	School1	N1_Major	Major1
STUCK?	Education	Al Rabie District, Riyadh	8	2	Egypt	4	University of Oxford	3	Teacher Education Multiple Levels
THIQAH	IT Services and IT Consulting	Al Sahafah Dist, Riyadh	1100	916	Saudi Arabia	218	King Saud University	169	Computer Science
MRSOOL	مرسول	Technology, Information and Internet	Riyadh, RIYADH	500	1343	Saudi Arabia	37	King Saud University	35
Dsquares	Technology, Information and Internet	Riyadh	4	170	Egypt	30	Cairo University	32	Computer Science
Soum	Internet Marketplace Platforms	Riyadh	61	43	Saudi Arabia	11	King Fahd University of Petroleum & Minerals	6	Industrial Engineering

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Sports Analytics

Explore my portfolio of sports-related projects where I blend my passion for data analysis with my love for sports.

Tracking NBA Season Leaders Over a Decade:

• I've delved into the past decade's box score data using SQLite for a deep-dive analysis.
• The goal of this project was to do data manipulation using SQL with a minimal touch-up on R.
• The resulting graph paints an exciting picture, highlighting the league's top performers across key stats like: 3PT%, PPG, Assists, and Rebounds.
• SQL Queries & Data Source

A Deep Dive Into Luka Doncic's 60-Point Game: Shot Chart Analysis:

• Luka had a record-breaking game on december 27, 2022. What was his shot selection and shot making during that game?