About Me
🌐 LinkedIn | GitHub | Class of Physics
Physicist with a specialization in Data Science and Data Analytics.
Apps
Task Tracker App
Streamlit
A lightweight web application to track and analyze daily habits and progress.
Check out and use it 😉 HERE (version beta)
Machine Learning Engineer
MLops Turbine Energy
MLops | MLflow | flask API | Streamlit
Machine Learning model to predict Turbine Energy Yield (TEY) based on operational data. It features an interactive Streamlit and an API that serves the best-performing model tracked in MLflow.
Recommender System
MLops | MLflow | Streamlit
Developed a recommender system using filtering techniques on Amazon’s dataset. Experiments managed with MLflow and a web app built in Streamlit for user interaction.
ETL Process
BigQuery | Airflow | Testing
Implemented an ETL pipeline to extract data from a API, transform it, and load it into BigQuery using Airflow. Incorporated Agile methodologies, testing, and version control.
Data Analytics
Global Commodities Trade
Brief analysis over Global Commodities Trade; focus on Trade Balance, Trade average per country and the commodities most commercialization from 1990 to 2016.
E-commerce Purchasing Patterns Analysis
This project analyzes real-world e-commerce data to uncover customer behavior, purchasing patterns, and product trends. Using a dataset of 2.7M events, including views, add-to-carts, and transactions, it provides insights to optimize marketing strategies and inventory management.
Dataset Highlights
- Events: 2.66M views, 69K add-to-carts, 22K transactions.
- Users: 1.4M unique visitors.
- Products: 417K unique items.
- The analysis focuses on identifying anomalies, popular products, and customer journeys to support decision-making in e-commerce operations.
Data Science Projects
Product Sales Projection
Machine Learning | Time Series | LightGBM
Developed a sales prediction model, achieving a 6% accuracy improvement, enhancing inventory planning for an agricultural company.
Forecasting Store Sales of Drinks
Machine Learning | Time Series
Predicted daily beverage sales for February 2022 using a RandomForestRegressor, improving RMSE by 11.3%.
Discovering Exoplanets
Machine Learning
Classified exoplanets using Random Forest and hyperparameter tuning, achieving an F1 score of 74.65%.
Classification of Flowers
Deep Learning | Pretrained | TPU
Built a flower image classification model using pretrained models (Xception, VGG16, DenseNet201) and TPU hardware.
Skills
- Technical Skills: MLops, Streamlit, LightGBM, Airflow, ETL, Docker,etc.
- Soft Skills: Problem Solving, Agile Methodologies (Kanban), Effective Communication
- Languages: Spanish (Native), English (B2)
Research
- Quantum Study of Solitons in a Disordered Nonlinear Medium
- Analysis of the Probability Distribution of Eigenvalues in Random Matrices
📢 ANNOUNCEMENT : Collaboration
I am available to collaborate with startups needing support in Data Science and Data Analysis projects.
If you’re interested in working together, feel free to send me a direct message or email me at cristianj3006@gmail.com.
🚀 Check out my projects and contributions!
👷 Page in Building…