Machine Learning Team Leader
2018 - PRESENTMototech- Developed a forecasting algorithm that predicts the performance of NFL players. The results outperform state of the arte techniques by reducing the MSE from 8.541 to 5.576.
Technologies: Python, R, Sklearn, Tensorflow, KerasInvited Profesor
2018 - PRESENTDigital House- Dictated theoretical and applied lectures on the following topics: topic detection, conditionality reduction, embedding, time series analysis, embeddings, sentiment analysis, and text analysis/text mining. I used Tensorflow, Python, and keras during the lessons.
Technologies: Python, Sklearn, Tesnorflow, Bokeh, Matplotlib, Seaborn, KerasResearch intern
2017 - PRESENTUniversity of Buenos Aires- Analyzed the texts of news using natural language processing techniques. In particular, recursive deep models for semantic compositionality over sentiment treebank in order to detect the sentiment of a sentence, dimensional reduction algorithms and topic detection methods were used with the intention of characterizing the mass media bias during presidential elections.
- Focused my study on developing better machine learning techniques which efficiently uses the information of a node and its neighbours. In addition, This new semi supervised methodology will be validated using graphs of biological and social origin.
Technologies: Python, R, Matlab, Sklearn, Tesnorflow, Keras, Xgboost, Catboost, LightboostData Scientist
2016 - 2017Hexagon Consulting- Implemented different predictive models in order to describe the future financial behavior of bank clients using Python.
- Designed and implemented a recommendation system that uses text reviews in order to recommend a movie using text analysis (topic detection, sentiment analysis, and word embedding).
- Created and develop a software which statistically calculates a personal index of inflation based on a big economic and financial database.
Technologies: Python, Sklearn, Predictive models, JavaScript, D3, Seaborn, Bokeh
Federico Albanese
Data Visualization Developer in Buenos Aires, Argentina
Member since October 6, 2018
Frederico is currently pursuing a Ph.D. in Computer Science, studying and designing new machine learning techniques. During this process, he's able to continuously learn and implement state of the art algorithms and become a better data scientist with each day. In the last few years, Frederico has also worked at a financial consulting company analyzing transactional data and making model predictions.
Federico is now available for hire
Portfolio
- MototechPython, R, Sklearn, Tensorflow, Keras
- Digital HousePython, Sklearn, Tesnorflow, Bokeh, Matplotlib, Seaborn, Keras
- University of Buenos AiresPython, R, Matlab, Sklearn, Tesnorflow, Keras, Xgboost, Catboost, Lightboost
Experience
- Data Visualization 5 years
- Predictive Modeling 5 years
- Python 3 5 years
Availability
Part-time
Preferred Environment
Linux, Windows, Jupyter, Spyder, Git
The most amazing...
...prediction model I had coded outperformed the state of the art models in that area by 15%. This model was also fast and easy to interpret.
Employment
Experience
- Transparentar (Other amazing things)https://github.com/YamilaBarrera/transparentar
Created and developed software which uses statistics in order to analyze a complex network of fishing data.
Skills
Languages
Python 3, R, SQLLibraries/APIs
Sklearn, Keras, TensorFlowTools
MATLABOther
Neural Networks, Text Mining, Natural Language Processing (NLP), Embedded Software, Word2vec, Regression Models, Predictive Modeling, Data Visualization, Generative Adversarial Networks (GANs)
Education
- Ph.D. candidate in Computer Science and Machine Learning2018 - 2021Buenos Aires University - Buenos Aires, Argentina
- Licenciatura (equivalent to a bachelor + master degree) in Physics2011 - 2017Buenos Aires University - Buenos Aires, Argentina
