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$6 USD / hora
Bandera de EGYPT
sidi gaber, egypt
$6 USD / hora
Aquí son las 12:44 p. m.
Se unió el febrero 7, 2023
0 Recomendaciones

Mohamed F.

@Flaifel

 5,0 (1 comentario)
0,0
0,0
100%
100%
$6 USD / hora
Bandera de EGYPT
sidi gaber, egypt
$6 USD / hora
100 %
Trabajos finalizados
100 %
Dentro del presupuesto
100 %
A tiempo
N/A
Tasa de recontratación

Data Scientist | Data Analyst | Data Entry

Mohamed Flaifel is a Google-certified data scientist and analyst with 2 nano degrees from Udacity. Top Skills: ✅ Data Science ✅ Machine Learning Models ✅ Statistical Analysis ✅ Regression Analysis ✅ A/B Testing ✅ Python, Pandas, and Numpy for Data Analytics. ✅ Matplotlib, Seaborn for Visualization. ✅ Web scraping. ✅ MS Excel, MS Word, MS PowerPoint ✅ Data Cleaning, Wrangling, and EDA ✅ Virtual Assistance I always strive to provide the best service possible by being available to work immediately on required tasks. If you choose me for a job, professionalism, and satisfaction are guaranteed
Freelancer Data Entry Clerks Egypt
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Contacta Mohamed F. sobre tu trabajo

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Portafolio

11701206
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
11007517
web scraping a widely famous arabic football website (Yallakora.com) getting a csv file with match details, time, score and teams. using Python + BeautifulSoap
Web Scraping famous arabic football website (Yallakora.com)
web scraping a widely famous arabic football website (Yallakora.com) getting a csv file with match details, time, score and teams. using Python + BeautifulSoap
Web Scraping famous arabic football website (Yallakora.com)
10798845
In this project, I explored data related to bike-share systems for three major cities in the United States. Chicago, New York City, and Washington. In an interactive and friendly experience, the user chooses to filter the data by city first, then he has the freedom to specify a certain month or a certain day to add as a filter. by computing some descriptive statistics. The user gets some interesting information that is readable, understandable, and easy to follow as below: • most common month • most common day of the week • most common hour of the day • most common start station • most common end station • most common trip from start to end (i.e., most frequent combination of start station and end station) • total travel time • average travel time • counts of each user type • counts of each gender (only available for NYC and Chicago) • the ages and years of birth for the youngest, oldest, and the average user in the end, the user is prompted if he wants to take a look at the data as
interactive Python script for data analysis
In this project, I explored data related to bike-share systems for three major cities in the United States. Chicago, New York City, and Washington. In an interactive and friendly experience, the user chooses to filter the data by city first, then he has the freedom to specify a certain month or a certain day to add as a filter. by computing some descriptive statistics. The user gets some interesting information that is readable, understandable, and easy to follow as below: • most common month • most common day of the week • most common hour of the day • most common start station • most common end station • most common trip from start to end (i.e., most frequent combination of start station and end station) • total travel time • average travel time • counts of each user type • counts of each gender (only available for NYC and Chicago) • the ages and years of birth for the youngest, oldest, and the average user in the end, the user is prompted if he wants to take a look at the data as
interactive Python script for data analysis
In this project, I explored data related to bike-share systems for three major cities in the United States. Chicago, New York City, and Washington. In an interactive and friendly experience, the user chooses to filter the data by city first, then he has the freedom to specify a certain month or a certain day to add as a filter. by computing some descriptive statistics. The user gets some interesting information that is readable, understandable, and easy to follow as below: • most common month • most common day of the week • most common hour of the day • most common start station • most common end station • most common trip from start to end (i.e., most frequent combination of start station and end station) • total travel time • average travel time • counts of each user type • counts of each gender (only available for NYC and Chicago) • the ages and years of birth for the youngest, oldest, and the average user in the end, the user is prompted if he wants to take a look at the data as
interactive Python script for data analysis
10914072
Adventure Works Financial Dashboard (Power BI)
10798850
in this project, I dealt with a dataset gathered from TMDB.com which contains 10,000 rows. there were so many limitations in the data that I managed to wrangle them perfectly for the analysis like some null values and missing information. In the end, I was able to answer and visualize some interesting questions like 1) What are the trends of Budget, Revenue and Profit over time? 2) is there a corellation between budget and vote/popularity? 3) What are the top 3 genres that profit the most? 4) What are the most genres produced over time? 5) is there a specific genre that are costly than other? 6) what is the popularity and avg. voting for the top 10 actor, director and production companies?
Exploratory Data Analysis for TMDB dataset
in this project, I dealt with a dataset gathered from TMDB.com which contains 10,000 rows. there were so many limitations in the data that I managed to wrangle them perfectly for the analysis like some null values and missing information. In the end, I was able to answer and visualize some interesting questions like 1) What are the trends of Budget, Revenue and Profit over time? 2) is there a corellation between budget and vote/popularity? 3) What are the top 3 genres that profit the most? 4) What are the most genres produced over time? 5) is there a specific genre that are costly than other? 6) what is the popularity and avg. voting for the top 10 actor, director and production companies?
Exploratory Data Analysis for TMDB dataset
11701206
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
The HR department at Salifort Motors wants to take some initiatives to improve employee satisfaction levels at the company. They collected data from employees, but now they don’t know what to do with it. They have the following question: What’s likely to make the employee leave the company? Our goals in this project are to analyze the data collected by the HR department and build a model that predicts whether or not an employee will leave the company. If we can predict when employees are likely to quit, it might be possible to identify factors that contribute to their leaving. Because it is time-consuming and expensive to find, interview, and hire new employees, increasing employee retention will be beneficial to the company. we used Logistic Regression and Random Forest models to predict the data
Logistic Regression and Tree-based Modeling
11007517
web scraping a widely famous arabic football website (Yallakora.com) getting a csv file with match details, time, score and teams. using Python + BeautifulSoap
Web Scraping famous arabic football website (Yallakora.com)
web scraping a widely famous arabic football website (Yallakora.com) getting a csv file with match details, time, score and teams. using Python + BeautifulSoap
Web Scraping famous arabic football website (Yallakora.com)
10798845
In this project, I explored data related to bike-share systems for three major cities in the United States. Chicago, New York City, and Washington. In an interactive and friendly experience, the user chooses to filter the data by city first, then he has the freedom to specify a certain month or a certain day to add as a filter. by computing some descriptive statistics. The user gets some interesting information that is readable, understandable, and easy to follow as below: • most common month • most common day of the week • most common hour of the day • most common start station • most common end station • most common trip from start to end (i.e., most frequent combination of start station and end station) • total travel time • average travel time • counts of each user type • counts of each gender (only available for NYC and Chicago) • the ages and years of birth for the youngest, oldest, and the average user in the end, the user is prompted if he wants to take a look at the data as
interactive Python script for data analysis
In this project, I explored data related to bike-share systems for three major cities in the United States. Chicago, New York City, and Washington. In an interactive and friendly experience, the user chooses to filter the data by city first, then he has the freedom to specify a certain month or a certain day to add as a filter. by computing some descriptive statistics. The user gets some interesting information that is readable, understandable, and easy to follow as below: • most common month • most common day of the week • most common hour of the day • most common start station • most common end station • most common trip from start to end (i.e., most frequent combination of start station and end station) • total travel time • average travel time • counts of each user type • counts of each gender (only available for NYC and Chicago) • the ages and years of birth for the youngest, oldest, and the average user in the end, the user is prompted if he wants to take a look at the data as
interactive Python script for data analysis
In this project, I explored data related to bike-share systems for three major cities in the United States. Chicago, New York City, and Washington. In an interactive and friendly experience, the user chooses to filter the data by city first, then he has the freedom to specify a certain month or a certain day to add as a filter. by computing some descriptive statistics. The user gets some interesting information that is readable, understandable, and easy to follow as below: • most common month • most common day of the week • most common hour of the day • most common start station • most common end station • most common trip from start to end (i.e., most frequent combination of start station and end station) • total travel time • average travel time • counts of each user type • counts of each gender (only available for NYC and Chicago) • the ages and years of birth for the youngest, oldest, and the average user in the end, the user is prompted if he wants to take a look at the data as
interactive Python script for data analysis
10914072
Adventure Works Financial Dashboard (Power BI)
10798850
in this project, I dealt with a dataset gathered from TMDB.com which contains 10,000 rows. there were so many limitations in the data that I managed to wrangle them perfectly for the analysis like some null values and missing information. In the end, I was able to answer and visualize some interesting questions like 1) What are the trends of Budget, Revenue and Profit over time? 2) is there a corellation between budget and vote/popularity? 3) What are the top 3 genres that profit the most? 4) What are the most genres produced over time? 5) is there a specific genre that are costly than other? 6) what is the popularity and avg. voting for the top 10 actor, director and production companies?
Exploratory Data Analysis for TMDB dataset
in this project, I dealt with a dataset gathered from TMDB.com which contains 10,000 rows. there were so many limitations in the data that I managed to wrangle them perfectly for the analysis like some null values and missing information. In the end, I was able to answer and visualize some interesting questions like 1) What are the trends of Budget, Revenue and Profit over time? 2) is there a corellation between budget and vote/popularity? 3) What are the top 3 genres that profit the most? 4) What are the most genres produced over time? 5) is there a specific genre that are costly than other? 6) what is the popularity and avg. voting for the top 10 actor, director and production companies?
Exploratory Data Analysis for TMDB dataset

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5,0
$3,00 USD
Copy typing work on Google
he has done a great job.
Data Entry
Virtual Assistant
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Avatar del usuario
Bandera de Mahrukh C.
@mahrukhch747
•
hace 11 meses

Experiencia

Deputy Head of production

ENHD
ene 2018 - jul 2022 (4 años, 6 meses)
I had the honor to work as a deputy head of production to the largest hydrographic governmental office, responsible for analyzing geospatial data achieving great milestones together with my team.

Educación

maritime science

Arab Academy for Science, Technology and Maritime Transport, Egypt 2006 - 2010
(4 años)

Calificaciones

Data Analysis

Udacity
2023
A nano degree from one of the most trusted platforms Udacity, with 2 full successful projects done

Contacta Mohamed F. sobre tu trabajo

Inicia sesión para comentar cualquier detalle por chat.

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preferredfreelancer-1.png Preferred Freelancer Program SLA 1 88%

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Data Entry 1
Virtual Assistant 1
PDF 1
Python
Data Processing

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