kevin-titi,Kevin T.,github

Hello! My name is Kevin. 👋🏼

Thank you for looking into my Github! My name is Kevin. I am a data analyst seeking to explore the opportunities data can provide for business action. Joining the MS in Business Analytics program at USC Marshall, I am looking forward to sharing what I have learned with you.

Take a look at my data analytics work!

💳 (Python) Fraud Analytics: Credit Card Transaction Fraud Detection
🎮 (Python) Game Analytics: Day 14 Player Retention/Churn Prediction
🎮 (Python) Game Analytics: A/B Testing the Impact of a Game Difficulty Variation
🗃️ (SQL) Northwind Traders Database Exploration
📈 (Tableau) Superstore - Executive Dashboard (I cannot put one from case competition as the data is protected by NDA, so I will instead use this sample data as a project demonstration.)

My business-related work

🏆 My team has won 1st place in Wharton People Analytics Conference Case Competition 2023. I will put my work here.
🎮 I have done Game Teardown: MARVEL SNAP, analyzing player progression, in-game economy, and monetization.

I would love to connect with new people!

👔 LinkedIn: in/kevin-titi
✉️ Email: [email protected]
📖 My favorite books are from Adam Grant!

Kevin T.'s Projects

early-access_game_premium

Which day of the week should we release early access of the game? How long should the early access be? Should we even allow for early access in the first place? We are seeking to answer these questions for AAA games that offers premium early access.

fraud_detection_credit_card_transaction

Credit card transaction fraud detection model that utilized credit card transaction data from 2010, incorporating data from both credit card companies and merchants.

game_analytics_ab_test_difficulty_effect

A simple A/B Testing in Python with 95% confidence level to assess the impact of varied difficulty settings, demonstrating significantly higher character experience with game balance adjustment.

game_analytics_d14_retention_prediction

A predictive model for player retention/churn on day-14 after game installation based on features such as in-game metrics, user behavior, and engagement patterns to identify players at risk of churning, accurately predicting 65% of all retention within the top 6% of total population.

kevin-titi

This is a repository for my profile's README.

msba-case

MSBA Case Competition

sql-northwind_traders_database

Several queries demonstrate how SQL can be used to extract and join different data sources. This project is based on a database from Northwind Traders, a company that imports and exports food globally.

wharton_people_analytics_case_competition_2023

Our team analyzed real data to help TFA optimize its corps matching process for placement acceptance and successful corps experiences. Finalists were selected in a multi-round process based on the degree to which they demonstrated.

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