Diego Eduardo N. Profile

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5,0

(4 comentarios)

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$20 USD / hora

palermo,

argentina

$20 USD / hora

Aquí son las 5:33 p. m.

Se unió el octubre 31, 2015

0 Recomendaciones

Diego Eduardo N.

@diegoedunapo

5,0

(4 comentarios)

3,9

100%

$20 USD / hora

palermo,

argentina

$20 USD / hora

100 %

Trabajos finalizados

100 %

Dentro del presupuesto

100 %

A tiempo

17 %

Tasa de recontratación

Data scientist and Certified Public Accountant

Data Scientist, Machine Learning Engineer and Data Analytics with +6 years of experience in developing solutions for Banks and Insurance companies where data is the core of the final product. Experienced in handling microservices and REST APIs using Python with a focus on process improvement and automation using Machine Learning, NLP & AI. In addition, I am specialized in various Big Data tools, Business Intelligence and strong experience in reporting and indicators for different industries ( SPSS / Spark / SQL / AWS / Google Cloud / PowerBI). I am passionate about Artificial Intelligence and data science focused on improving processes and customer experience.

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Portafolio

In this project several manual tasks were automated, organizing the Machine Learning processes.
The typical flow, realization steps were applied:
1)Upload the training data from BigQuery, upload and store the training files, which have already been transformed into Cloud Storage, pack the training code into a train.py file. This call will be pushed as a Docker image into the Container Log, triggering the trainee in the Artificial Intelligence Platform training.
2)The Artificial Intelligence Platform also stores the training devices as the training model in the Cloud Storage.
3)Deployment of the training model using the AI Platform prediction, so that the model can be utilice. The prediction of the AI platform does that, retrieving the saved model from the Cloud Storage by deploying it as an API.

Implement a machine learning application in Google Cloud. -

Using public data a prediction model was built to predict the price of a rental taking into account the following independent variables: Environments, M2 and Neighborhood.
The LGBMRegressor algorithm was trained with 4 neighborhoods of the 50. Then i built the Api with Flask, saved it in GitHub and used Heroku for the deploy.

Repositorio:
https://github.com/NapoliD/Flask_api_alquiler

https://alquilar.herokuapp.com/

How much to charge your rent?

Gradient boosting is currently one of the most popular techniques for efficient modeling of tabular datasets of all sizes. XGboost is a very fast, scalable implementation of gradient boosting, with models using XGBoost regularly winning online data science competitions and being used at scale across different industries.
I implemented this algorithm in several projects in R or Python, and obtaining very good results.

Extreme Gradient Boosting with XGBoost

Machine learning is being used to optimize customer journeys which maximize their satisfaction and lifetime value.
Use different techniques to predict the rotation of customers and interpret their drivers, measure and forecast the value of the customer's useful life and, finally, create customer segments based on their product purchasing patterns.
Churn prediction
Customer Lifetime Value (CLV) prediction
Customer segmentation

Machine learning for marketing in python

The objective of this project was to identify potential clients to offer lines of credit:

Modeling credit risk for both personal loans and businesses is of great importance to banks. The probability that a debtor will default is a key component in reaching a credit risk measure.

Applied methodology:
Logistic regression and decision trees.

Analysis steps:
1) Data preprocessing
2) Logistic regression.
3) decision trees
4) Evaluation of a credit risk model.

Credit Risk Modeling in R

Implement a machine learning application in Google Cloud. -

How much to charge your rent?

Extreme Gradient Boosting with XGBoost

Machine learning for marketing in python

Credit Risk Modeling in R

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5,0

$350,00 USD

Machine Learning module to detect early anomalies in sensor data

Nuestro primer proyecto de Inteligencia Artificial y fue una excelente experiencia de trabajo con Diego, un ingeniero my profesional con mucho conocimiento , el trabajo realizado superó nuestras expectativas, siempre estuvo en comunicación , asesorándonos y sugiriendo posibles soluciones a nuestro reto. Gracias.

Python

Software Architecture

Machine Learning (ML)

Data Mining

Hadoop

Carlos O.

@calicheosorio76

•

hace 3 años

5,0

$60,00 USD

AWS Sagemaker + SparkML pipeline to be built

Delivered on time? Delivered within budget? Rate Diego Eduardo N. Quality of the work 5.0 Communication 5.0 Expertise 5.0 Professionalism 5.0 How likely are you going to hire Diego Eduardo N. again? 5.0

Machine Learning (ML)

Amazon Web Services

PySpark