Luis Q.

Key Skills

Software

Don't disclose

Top Subject Matter

Multidimensional poverty and education performance (policy analysis)

Video game sales and market segmentation

Sports analytics and betting strategy automation

Top Data Types

Video

Top Task Types

Classification

Freelancer Overview

Data Science Student — IPM & ICFES SCORE RELATIONSHIP MODEL 2024 (classification modeling and evaluation). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose. Education includes Data Science Student, Universidad de La Sabana (2024). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Personal Project — SPORTS BETTING BOT 2026 (data-driven decision pipeline)

Don't disclose

Developed a sports betting bot that automates strategies using statistical analysis and historical sports data. Built data processing pipelines to prepare inputs for decision-making logic. Implemented evaluation of betting opportunities to guide automated actions. • Processed historical sports data for model-ready features • Implemented statistical evaluation and strategy decision rules • Automated bet opportunity identification and filtering • Validated decision logic against historical outcomes

2026

Data Science Student — VIDEO GAME SALES PREDICTION 2024 (clustering-based analysis)

Don't discloseClassification

Applied K-Means clustering to segment video games using critic and user scores. Converted categorical variables into numerical representations to enable clustering-based modeling. Assessed correlations across clusters to produce a market segmentation oriented predictive view. • Preprocessed score features and encoded categorical variables • Performed unsupervised clustering with K-Means and elbow method • Analyzed inter-cluster relationships with correlation checks • Produced interpretable segmentation outputs for analysis

2024

Data Science Student — IPM & ICFES SCORE RELATIONSHIP MODEL 2024 (classification modeling and evaluation)

Don't disclose

Trained a classification model to categorize Colombian departments using multidimensional poverty index (IPM) and academic performance (ICFES). Performed model evaluation to support evidence-based policy insights and identify regional disparities. Worked in a data science academic setting to build an interpretable predictive pipeline for analysis. • Implemented supervised learning classification for regional categorization • Engineered features from IPM and ICFES indicators for modeling • Evaluated predictive performance and error patterns for insights • Interpreted results to inform policy-focused conclusions

2024

Education

Work History