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RAZIŠČI
Data Science
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Deep Learning
Machine Learning (ML)
Python
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$15 USD/uro
Zastava MOROCCO
souk sebt, lefquih ben saleh, morocco
$15 USD/uro
Trenutno je 3:21 pop. tukaj
Datum pridružitve februar 28, 2021
1 Priporočilo

Hamza R.

@hamzabenoumaia

 5,0 (23 ocen)
5,3
5,3
94%
94%
$15 USD/uro
Zastava MOROCCO
souk sebt, lefquih ben saleh, morocco
$15 USD/uro
84 %
Opravljeno delo
99 %
V okviru proračuna
99 %
Pravočasno
10 %
Stopnja ponovnega najema

Data Scientist

Hi, I am Hamza I can help you with your data science work. I can communicate in English, Arabic and French. I have experience working with various data related tasks such as : - Data Processing - Data visualization and Business intelligence - Machine learning and deep learning - Image processing - Computer Vision - NLP - Control systems - Clustering and segmentation - Recommender systems - time-series forecasting and analysis Please reach out to me if you are interested in my services.
Freelancer Python Developers Morocco
Glej vse Python Developers v Morocco

Stopite v stik s/z Hamza R. glede vašega dela

Prijavite se, da se o podrobnostih pogovorite v klepetu.

Portfelj

9509494
In this project I did some analysis with local MySQL database, I used some queries to check the data errors and inconsistencies, loaded the data into Tableau and did some cleaning to prepare the data for analysis and building the dashboard in the picture.
Tableau Dashboard using data from MySQL database
In this project I did some analysis with local MySQL database, I used some queries to check the data errors and inconsistencies, loaded the data into Tableau and did some cleaning to prepare the data for analysis and building the dashboard in the picture.
Tableau Dashboard using data from MySQL database
In this project I did some analysis with local MySQL database, I used some queries to check the data errors and inconsistencies, loaded the data into Tableau and did some cleaning to prepare the data for analysis and building the dashboard in the picture.
Tableau Dashboard using data from MySQL database
9502091
In this project I used some python libraries to build an app that grabs real stock data and predict closing price, the user can train the model themselves on the interval of data that they wish
Stock forecasting app with Python
9494282
I can build powerfull image classification models with keras or Pytorch and do transfer learning, fine tuning or training from scratch with clear code and nice reports.
Training image classification models with keras
I can build powerfull image classification models with keras or Pytorch and do transfer learning, fine tuning or training from scratch with clear code and nice reports.
Training image classification models with keras
9485151
In this task, I built a Pytorch dataset class and logistic regression model with Pytorch to train on any football dataset, The model uses one layer and one sigmoid unit as output which is a logistic regression essentially (the client wanted logistic regression), However I decided to use Pytorch to make the model easy to upgrade to a deeper neural network. The dataset class generates for each match a vector of previous matches results for home and away team so that the model can use the teams history in predictions. I included some code snippets shots of dataset definition and model training.
Predict if both teams would score in a premier league match
In this task, I built a Pytorch dataset class and logistic regression model with Pytorch to train on any football dataset, The model uses one layer and one sigmoid unit as output which is a logistic regression essentially (the client wanted logistic regression), However I decided to use Pytorch to make the model easy to upgrade to a deeper neural network. The dataset class generates for each match a vector of previous matches results for home and away team so that the model can use the teams history in predictions. I included some code snippets shots of dataset definition and model training.
Predict if both teams would score in a premier league match
In this task, I built a Pytorch dataset class and logistic regression model with Pytorch to train on any football dataset, The model uses one layer and one sigmoid unit as output which is a logistic regression essentially (the client wanted logistic regression), However I decided to use Pytorch to make the model easy to upgrade to a deeper neural network. The dataset class generates for each match a vector of previous matches results for home and away team so that the model can use the teams history in predictions. I included some code snippets shots of dataset definition and model training.
Predict if both teams would score in a premier league match
9485118
In this project, a public dataset by IBM was used to analyse employee attrition. The main task was to analyse why employees may leave a company at some point. First, I performed an extensive analysis of several factors like job role, salary, distance from home etc. and how each one of them impact the probability that the employee might leave. Second, I picked the most indicative features and built a classification model (deep neural network) with keras and tensorflow and achieved an f1 score of 83 and accuracy of 85, then tried several approches to overcome the class imbalance issue and achieved 86 % f1 score. Below are some visuals of feature analysis and deep learning training process and code.
Predict if an employee would leave the company
In this project, a public dataset by IBM was used to analyse employee attrition. The main task was to analyse why employees may leave a company at some point. First, I performed an extensive analysis of several factors like job role, salary, distance from home etc. and how each one of them impact the probability that the employee might leave. Second, I picked the most indicative features and built a classification model (deep neural network) with keras and tensorflow and achieved an f1 score of 83 and accuracy of 85, then tried several approches to overcome the class imbalance issue and achieved 86 % f1 score. Below are some visuals of feature analysis and deep learning training process and code.
Predict if an employee would leave the company
9485089
In this project I worked with a dataset of hourly Electric power output of PV systems installed cross Belgium and connected to the nation grid, The objective was to forecast the hourly power yield in MW for the next 24h and one week horizons. I chose normalized root mean squared error as a percentage metric to measure model performance, then I run a Bayesian hyperparameter tuning loop to achieve best performance possible in the shortest amount of time. The pictures illustrate code snippets for feature engineering and metrics measurement, formulas for metrics used, hyperparameter tuning results and finally example of predictions generated by the model.
Forecasting Solar energy generation in Belgium with XGBoost
In this project I worked with a dataset of hourly Electric power output of PV systems installed cross Belgium and connected to the nation grid, The objective was to forecast the hourly power yield in MW for the next 24h and one week horizons. I chose normalized root mean squared error as a percentage metric to measure model performance, then I run a Bayesian hyperparameter tuning loop to achieve best performance possible in the shortest amount of time. The pictures illustrate code snippets for feature engineering and metrics measurement, formulas for metrics used, hyperparameter tuning results and finally example of predictions generated by the model.
Forecasting Solar energy generation in Belgium with XGBoost
9509494
In this project I did some analysis with local MySQL database, I used some queries to check the data errors and inconsistencies, loaded the data into Tableau and did some cleaning to prepare the data for analysis and building the dashboard in the picture.
Tableau Dashboard using data from MySQL database
In this project I did some analysis with local MySQL database, I used some queries to check the data errors and inconsistencies, loaded the data into Tableau and did some cleaning to prepare the data for analysis and building the dashboard in the picture.
Tableau Dashboard using data from MySQL database
In this project I did some analysis with local MySQL database, I used some queries to check the data errors and inconsistencies, loaded the data into Tableau and did some cleaning to prepare the data for analysis and building the dashboard in the picture.
Tableau Dashboard using data from MySQL database
9502091
In this project I used some python libraries to build an app that grabs real stock data and predict closing price, the user can train the model themselves on the interval of data that they wish
Stock forecasting app with Python
9494282
I can build powerfull image classification models with keras or Pytorch and do transfer learning, fine tuning or training from scratch with clear code and nice reports.
Training image classification models with keras
I can build powerfull image classification models with keras or Pytorch and do transfer learning, fine tuning or training from scratch with clear code and nice reports.
Training image classification models with keras
9485151
In this task, I built a Pytorch dataset class and logistic regression model with Pytorch to train on any football dataset, The model uses one layer and one sigmoid unit as output which is a logistic regression essentially (the client wanted logistic regression), However I decided to use Pytorch to make the model easy to upgrade to a deeper neural network. The dataset class generates for each match a vector of previous matches results for home and away team so that the model can use the teams history in predictions. I included some code snippets shots of dataset definition and model training.
Predict if both teams would score in a premier league match
In this task, I built a Pytorch dataset class and logistic regression model with Pytorch to train on any football dataset, The model uses one layer and one sigmoid unit as output which is a logistic regression essentially (the client wanted logistic regression), However I decided to use Pytorch to make the model easy to upgrade to a deeper neural network. The dataset class generates for each match a vector of previous matches results for home and away team so that the model can use the teams history in predictions. I included some code snippets shots of dataset definition and model training.
Predict if both teams would score in a premier league match
In this task, I built a Pytorch dataset class and logistic regression model with Pytorch to train on any football dataset, The model uses one layer and one sigmoid unit as output which is a logistic regression essentially (the client wanted logistic regression), However I decided to use Pytorch to make the model easy to upgrade to a deeper neural network. The dataset class generates for each match a vector of previous matches results for home and away team so that the model can use the teams history in predictions. I included some code snippets shots of dataset definition and model training.
Predict if both teams would score in a premier league match
9485118
In this project, a public dataset by IBM was used to analyse employee attrition. The main task was to analyse why employees may leave a company at some point. First, I performed an extensive analysis of several factors like job role, salary, distance from home etc. and how each one of them impact the probability that the employee might leave. Second, I picked the most indicative features and built a classification model (deep neural network) with keras and tensorflow and achieved an f1 score of 83 and accuracy of 85, then tried several approches to overcome the class imbalance issue and achieved 86 % f1 score. Below are some visuals of feature analysis and deep learning training process and code.
Predict if an employee would leave the company
In this project, a public dataset by IBM was used to analyse employee attrition. The main task was to analyse why employees may leave a company at some point. First, I performed an extensive analysis of several factors like job role, salary, distance from home etc. and how each one of them impact the probability that the employee might leave. Second, I picked the most indicative features and built a classification model (deep neural network) with keras and tensorflow and achieved an f1 score of 83 and accuracy of 85, then tried several approches to overcome the class imbalance issue and achieved 86 % f1 score. Below are some visuals of feature analysis and deep learning training process and code.
Predict if an employee would leave the company
9485089
In this project I worked with a dataset of hourly Electric power output of PV systems installed cross Belgium and connected to the nation grid, The objective was to forecast the hourly power yield in MW for the next 24h and one week horizons. I chose normalized root mean squared error as a percentage metric to measure model performance, then I run a Bayesian hyperparameter tuning loop to achieve best performance possible in the shortest amount of time. The pictures illustrate code snippets for feature engineering and metrics measurement, formulas for metrics used, hyperparameter tuning results and finally example of predictions generated by the model.
Forecasting Solar energy generation in Belgium with XGBoost
In this project I worked with a dataset of hourly Electric power output of PV systems installed cross Belgium and connected to the nation grid, The objective was to forecast the hourly power yield in MW for the next 24h and one week horizons. I chose normalized root mean squared error as a percentage metric to measure model performance, then I run a Bayesian hyperparameter tuning loop to achieve best performance possible in the shortest amount of time. The pictures illustrate code snippets for feature engineering and metrics measurement, formulas for metrics used, hyperparameter tuning results and finally example of predictions generated by the model.
Forecasting Solar energy generation in Belgium with XGBoost

Ocene

Spremembe shranjene
Prikazanih je1 - 5 od 23 ocen
 Filtriraj ocene po:
5,0
$100,00 USD
Project for Hamza R. -- 4
great as usual
Python
Machine Learning (ML)
Data Science
Pytorch
+1 več
S
Zastava Sulfer A.
@sulfermm21
•
pred 2 letoma
5,0
$180,00 USD
Project for Hamza R. -- 3
Hamza has delivered great work.
Python
Machine Learning (ML)
Data Science
Pytorch
+1 več
S
Zastava Sulfer A.
@sulfermm21
•
pred 2 letoma
4,6
$50,00 CAD
Python + machine learning/data mining + programming (arithmetic, comparison, and logical) expert required
Professional and good communicator.
Python
Algorithm
Web Scraping
Machine Learning (ML)
Data Mining
R
Zastava Amrik S.
@Rick9988
•
pred 2 letoma
5,0
$150,00 USD
Project for Hamza R. -- 2
Hamza always delivers high quality work, I'd definitely recommend him
Python
Machine Learning (ML)
Data Science
Pytorch
+1 več
S
Zastava Sulfer A.
@sulfermm21
•
pred 2 letoma
0,0
$10,00 USD
Project for Hamza R.
The project is not completed according to the dispute
Python
Machine Learning (ML)
Data Science
Pytorch
+1 več
C
Closed User
@chabirmolla
•
pred 2 letoma

Kvalifikacije

Deep learning Specialization

Deeplearning.ai
2020
Neural Networks and Deep Learning Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Structuring Machine Learning Projects Convolutional Neural Networks Sequence Models

Google Professional Data Analyst

Google
2021
Those who earn the Google Data Analytics Professional Certificate have completed eight courses, developed by Google, that include hands-on, practice-based assessments and are designed to prepare them for introductory-level roles in Data Analytics. They are competent in tools and platforms including spreadsheets, SQL, Tableau, and R. They know how to prepare, process, analyze, and share data for thoughtful action.

Stopite v stik s/z Hamza R. glede vašega dela

Prijavite se, da se o podrobnostih pogovorite v klepetu.

Verifikacije

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Povezano s Facebookom

Certifikacije

preferredfreelancer-1.png Preferred Freelancer Program SLA 1 92%

Top spretnosti

Python 16
Machine Learning (ML) 14
Data Science 8
Data Visualization 8
Deep Learning 5

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