Freelancer logo How It Works Browse Jobs
Log In Sign Up
Post a Project
Profile cover photo
You're now following
Error following user.
This user does not allow users to follow them.
You are already following this user.
Your membership plan only allows 0 follows. Upgrade here.
Successfully unfollowed
Error unfollowing user.
You have successfully recommended
Error recommending user.
Something went wrong. Please refresh the page and try again.
Email successfully verified.
User Avatar
$12 USD / hour
Flag of MOROCCO
agadir, morocco
$12 USD / hour
It's currently 2:24 PM here
Joined March 21, 2020
0 Recommendations

Mouaad E.

@MouaadErrami

 5.0 (2 reviews)
2.8
2.8
100%
100%
$12 USD / hour
Flag of MOROCCO
agadir, morocco
$12 USD / hour
100%
Jobs Completed
100%
On Budget
100%
On Time
N/A
Repeat Hire Rate

FREELANCE ON  DATA SCIENCE  , DEVELOPMENT

Thanks for visiting our profile. My number one and most important value is the guarantee of customer satisfaction! I will make sure to fulfill all the requirements of your project and are open to discuss further any of your conditions. I am skilled in Computer Science (specialized in Machine learning), and have realized a great number of projects pertaining to Data Mining, Big Data, and Deep Learning. As such, I have amassed a good amount of knowledge on R and Python. I also have decent knowledge in statistics and Mathematics. You are free to consult my earlier works in order to assess for yourself. My work is not limited to ML, as I am also proficient in Web Development using PHP and Software Architecture. Some of my projects have required the use of technologies such as Node.js/Express as well as more traditional technologies like PHP/Laravel, Python/Django, and MySQL. Among thousands of specialists, what differs me from others is my problem solving methods. A good result is guaranteed as soon and I accept the job. I'm looking forward to hearing from you.
Freelancer Python Developers Morocco
See all Python Developers in Morocco

Contact Mouaad E. about your job

Log in to discuss any details over chat.

Portfolio

8748111
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
8748068
This application consists of determining the current position of the user, locating the emergency services that are close to him, as well as establishing information specific to the chosen service.
application for locating and finding emergency services
This application consists of determining the current position of the user, locating the emergency services that are close to him, as well as establishing information specific to the chosen service.
application for locating and finding emergency services
This application consists of determining the current position of the user, locating the emergency services that are close to him, as well as establishing information specific to the chosen service.
application for locating and finding emergency services
8748049
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
8717013
it is a kind of platform that acts as an interface between applicants and the school. Students, professors and staff will have access to our platform. Each new visitor will be asked to authenticate for faculty and staff and for applicants to complete their registration form. So when the teacher authenticates, he will have access to his thesis database. He can add, modify, delete theses. Also when the admin authenticates, he will have access to all the databases of data. he can publish theses and results. add or delete a teacher
Management of doctoral theses
it is a kind of platform that acts as an interface between applicants and the school. Students, professors and staff will have access to our platform. Each new visitor will be asked to authenticate for faculty and staff and for applicants to complete their registration form. So when the teacher authenticates, he will have access to his thesis database. He can add, modify, delete theses. Also when the admin authenticates, he will have access to all the databases of data. he can publish theses and results. add or delete a teacher
Management of doctoral theses
8716990
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
8716982
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
8748111
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
The Group has adopted a new strategy for digitalizing its industrial processes. This is achieved by integrating the connected sensors into the equipment to collect the data and to allow real-time monitoring of the process; this ensures effective remote control that controls fertilizer quality on site. Thus, the aim of our project is to optimize the operating parameters of the fertilizer production process in order to master the quality of NPK fertilizers and to identify the real root causes impact on quality. This helps consolidate the initial data set and identify important variables. However, after using a few techniques to select variables, it turns out that they are expensive or ineffective. As a result, new variables were introduced that surpassed all conventional methods used. Finally, a predictive model for the quality (Titles and grain size 2-4mm) of NPK fertilizers was constructed. Subsequently, optimization heuristics were applied to find the
Predictive model for controlling the quality of NPK
8748068
This application consists of determining the current position of the user, locating the emergency services that are close to him, as well as establishing information specific to the chosen service.
application for locating and finding emergency services
This application consists of determining the current position of the user, locating the emergency services that are close to him, as well as establishing information specific to the chosen service.
application for locating and finding emergency services
This application consists of determining the current position of the user, locating the emergency services that are close to him, as well as establishing information specific to the chosen service.
application for locating and finding emergency services
8748049
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
In order to better improve its management costs, we were entrusted with the task of creating a solution capable of simulating the management costs of a multi-technique park .we discuss the different needs of the users, such as the functional and technical ones, before breaking down the different steps which allowed us to conceptualize the project. As we study the complete process needed to understand how management costs are decided, we manage to consolidate the initial data as well as the important variables. The next step was all about choosing different machine learning algorithms in order to evaluate them one by one and choosing the most compatible one in our case.
IMPLEMENTATION OF A MULTI-TECHNICAL MAINTENANCE COST SIMULAT
8717013
it is a kind of platform that acts as an interface between applicants and the school. Students, professors and staff will have access to our platform. Each new visitor will be asked to authenticate for faculty and staff and for applicants to complete their registration form. So when the teacher authenticates, he will have access to his thesis database. He can add, modify, delete theses. Also when the admin authenticates, he will have access to all the databases of data. he can publish theses and results. add or delete a teacher
Management of doctoral theses
it is a kind of platform that acts as an interface between applicants and the school. Students, professors and staff will have access to our platform. Each new visitor will be asked to authenticate for faculty and staff and for applicants to complete their registration form. So when the teacher authenticates, he will have access to his thesis database. He can add, modify, delete theses. Also when the admin authenticates, he will have access to all the databases of data. he can publish theses and results. add or delete a teacher
Management of doctoral theses
8716990
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
To fulfill this goal, Dashboards and reports have been elaborated to allow the statisticians of the Data Department to manage and manipulate all data related to Self- Activities. The realization of the project was only possible through the help of the Business Intelligence suite, through SQL server for the creation of the Data Warehouse, Data integration with Visual Studio, and Power BI for the Reporting.
Realization of a solution business decision-making
8716982
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning
The Group has adopted a new strategy of digitalisation of import processes. This is achieved by the digitisation of the import process to enable real-time monitoring of the process and reduce the time required for this process, directly on the site. In that way, our project is to allow the importer to know the estimated time that the imported goods will spend in the port for control measures before he will be able to take control of them. Moreover, to identify the things that have an impact on that duration. In the beginning, we started our approach by defining the context of our project. Afterwards, we analysed the different stages of conception, and then we carried out a complete study of the process to become familiar with its equipment and to understand the interactions between its different parts. This helps to consolidate the initial data set and to identify the factors that influence the entire import operation. Finally, a predictive model was built for the duration
Development of predictive models based on machine learning

Reviews

Changes saved
Showing 1 - 2 out of 2 reviews
Filter reviews by:
5.0
£40.00 GBP
Price comparing application
Good quality work delivered on time
PHP
Java
Python
Web Scraping
HTML
Y
Flag of Yuewen D.
@YW2021
•
3 years ago
5.0
$40.00 USD
Realization of a request management application of staffing
Mouad is a great person to work with. His level of knowledge is tremendous and very skilled in Spring Framework and Java. Delevered the work in time. Continusly communicated even after the work is completed. Would recommend him 100% for any future work. Thanks Mouad
AngularJS
Java Spring
Microsoft SQL Server
MVC
SQL
User Avatar
Flag of Abderrahim O.
@oujbih
•
3 years ago

Contact Mouaad E. about your job

Log in to discuss any details over chat.

Verifications

Preferred Freelancer
Identity Verified
Payment Verified
Phone Verified
Email Verified
Facebook Connected

Top Skills

Python 1
SQL 1
Web Scraping 1
Java Spring 1
JavaScript

Browse Similar Freelancers

Python Developers in Morocco
Python Developers
SQL Developers
Web Scraping Specialists

Browse Similar Showcases

Python
SQL
Web Scraping
Java Spring
Previous User Next User
Invite sent successfully!
Thanks! We’ve emailed you a link to claim your free credit.
Something went wrong while sending your email. Please try again.
Freelancer logo Philippines / English Help & Support Freelancer Categories Projects Contests Freelancers Enterprise Membership Preferred Freelancer Program Project Management Local Jobs Photo Anywhere Showcase API for Developers Get Verified Desktop App About About us How it Works Security Investor Sitemap Stories News Team Awards Press Releases Careers Terms Privacy Policy Terms and Conditions Copyright Policy Code of Conduct Fees and Charges Partners Escrow.com Loadshift Warrior Forum Apps Apple App Store logoGoogle Play logo
Copy to clipboard failed, please try again after adjusting your permissions.
Copied to clipboard.
Registered Users Total Jobs Posted
Freelancer ® is a registered Trademark of Freelancer Technology Pty Limited (ACN 142 189 759)
Copyright © 2024 Freelancer Technology Pty Limited (ACN 142 189 759)
Loading preview
Permission granted for Geolocation.
Your login session has expired and you have been logged out. Please log in again.