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$30 USD / hora
Bandeira do(a) MOROCCO
rabat, morocco
$30 USD / hora
Atualmente, está 3:56 AM aqui
Ingressou em junho 15, 2021
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Mouad M.

@mouadmguild

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$30 USD / hora
Bandeira do(a) MOROCCO
rabat, morocco
$30 USD / hora
Não se aplica
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Machine Learning Engineer

Hello! I'm Mouad, a seasoned data scientist with over three years of specialized experience. My expertise spans machine learning, deep learning, and statistical modeling. I possess a broad skill set that includes data analysis, deep learning, natural language processing, and time series forecasting. I thrive on addressing complex technical challenges in diverse domains. Are you ready to elevate your projects? Contact me today to start our conversation. Core Competencies: Machine Learning Data Analytics Statistical Modeling Data Visualization Programming in Python and R SQL Proficiency Data Cleaning and Preprocessing Data Architecture Project Management Proficiency in Agile and Scrum Excellent Communication and Problem-Solving Abilities Technical Proficiencies: Python libraries such as NumPy, Pandas, and Scikit-learn R libraries including dplyr, tidyr, and ggplot2 SQL database management with systems like MySQL, PostgreSQL, and SQLite Data visualization expertise using Tableau, Power BI, and D3.js Cloud computing on platforms like AWS, Google Cloud, and Azure Big Data technologies including Hadoop and Spark Version control with Git Tools for Data Cleaning and Preprocessing such as OpenRefine
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Portfólio

11877118
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend
11877118
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend
This project involves the development and evaluation of predictive models to forecast signups in the US. based on television advertising spending data. The models tested include a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and regression analyses. The SARIMA model accounts for both non-seasonal and seasonal components, while regression models explore the relationship between signups and spending lags. The project includes data preprocessing, model creation, and performance evaluation, with the last 100 observations set aside for testing.
Predictive Modeling of Signups Based on TV Ad Spend

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Educação

Statistics and Big Data

Morocco 2019 - 2023
(4 anos)

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Principais Habilidades

Python
Data Processing
Web Scraping
Machine Learning (ML)
Database Administration

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