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$30 USD / hora
Bandera de EGYPT
elsanta, egypt
$30 USD / hora
Aquí son las 6:37 a. m.
Se unió el septiembre 17, 2020
0 recomendaciones

Muhammad E.

@ikfrawee

verified.svg
 5,0 (14 comentarios)
6,2
6,2
100%
100%
$30 USD / hora
Bandera de EGYPT
elsanta, egypt
$30 USD / hora
100 %
Trabajos finalizados
100 %
Dentro del presupuesto
100 %
A tiempo
8 %
Tasa de recontratación

Software Engineer | Python | AWS | Django

Hello, I am a highly experienced software engineer with a focus on Python development, data analysis, and machine learning. With nearly 3 years of professional experience, I am committed to delivering high-quality work and maintaining open communication with my clients to ensure their satisfaction. I am dedicated to providing exceptional results and am always willing to make revisions as needed to achieve 100% client satisfaction. I am well-versed in the following: • Python, JavaScript, Node.JS • Web frameworks: Flask, Django, FastAPI • Cloud computing, Serverless Archtecutre (AWS). • Machine/Deep Learning (AWS Certified ML Specialist) • RESTful APIs using serverless (AWS) or frameworks (Flask, FastAPI). • Pandas, Matplotlib, NumPy, Plotly, and Seaborn • Selenium, Beautiful Soup, Scrapy, requests, and other API libraries • Jupyter notebooks and Google Colab • Tableau Spreadsheets and other similar tools • Other libraries: Pygame and basic GUI using Tkinter. I pride myself on providing professional services that meet all deadlines and exceed expectations. I pay close attention to detail to ensure that each project adds value to your business. Utilizing my extensive toolset and libraries, I strive to deliver the highest quality outcomes for your projects. I am available on Freelancer to answer any questions you may have or provide a portfolio of my work. Please do not hesitate to reach out if you have any inquiries or need assistance. I will respond promptly to your message. Please let me know how I can assist you with your project and what your requirements are.
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Portafolio

8550577
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
7868253
I‘ve successfully completed Data Analysis and Deep Learning Analysis Nanodegrees from Udacity. Data Analysis Professional and Advanced: Throughout this journey, I have developed outstanding skills in Python and its libraries (Numpy, Pandas, Matplotlib…) alongside understanding the steps of the data analysis process and how to gather and wrangle data using python libraries to get it clean and ready for further analysis and visualization. Also, apply inferential statistics and probability to real-world scenarios, create and analyze the results of hypothesis testing, create and analyze A/B test results, and build regression models in Python. Deep Learning: I have developed outstanding skills in machine learning and deep learning. Got introduced to neural networks, Learned about CNNs for image recognition, RNNs for sequence generation, and GANs for image generation. Also, Model deployment through AWS SageMaker.
Certificates
I‘ve successfully completed Data Analysis and Deep Learning Analysis Nanodegrees from Udacity. Data Analysis Professional and Advanced: Throughout this journey, I have developed outstanding skills in Python and its libraries (Numpy, Pandas, Matplotlib…) alongside understanding the steps of the data analysis process and how to gather and wrangle data using python libraries to get it clean and ready for further analysis and visualization. Also, apply inferential statistics and probability to real-world scenarios, create and analyze the results of hypothesis testing, create and analyze A/B test results, and build regression models in Python. Deep Learning: I have developed outstanding skills in machine learning and deep learning. Got introduced to neural networks, Learned about CNNs for image recognition, RNNs for sequence generation, and GANs for image generation. Also, Model deployment through AWS SageMaker.
Certificates
I‘ve successfully completed Data Analysis and Deep Learning Analysis Nanodegrees from Udacity. Data Analysis Professional and Advanced: Throughout this journey, I have developed outstanding skills in Python and its libraries (Numpy, Pandas, Matplotlib…) alongside understanding the steps of the data analysis process and how to gather and wrangle data using python libraries to get it clean and ready for further analysis and visualization. Also, apply inferential statistics and probability to real-world scenarios, create and analyze the results of hypothesis testing, create and analyze A/B test results, and build regression models in Python. Deep Learning: I have developed outstanding skills in machine learning and deep learning. Got introduced to neural networks, Learned about CNNs for image recognition, RNNs for sequence generation, and GANs for image generation. Also, Model deployment through AWS SageMaker.
Certificates
7235036
This project was part of the data wrangling section of the Udacity Data Analyst Nanodegree program. And it primarily focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook. This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater than 10. This aspect was not cleaned as it is part of the humor and popularity of WeRateDogs. For this project, we only wanted original ratings (no retweets or replies) that have images. Not all of the original tweets in the dataset are dog ratings and some are retweets. Fully assessing and cleaning the entire dataset would require exceptional effort so only a subset of its issues (eight quality issues and two tidiness issues at minimum) needed to be assessed and cleaned.
Data Wrangling - WeRateDogs
This project was part of the data wrangling section of the Udacity Data Analyst Nanodegree program. And it primarily focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook. This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater than 10. This aspect was not cleaned as it is part of the humor and popularity of WeRateDogs. For this project, we only wanted original ratings (no retweets or replies) that have images. Not all of the original tweets in the dataset are dog ratings and some are retweets. Fully assessing and cleaning the entire dataset would require exceptional effort so only a subset of its issues (eight quality issues and two tidiness issues at minimum) needed to be assessed and cleaned.
Data Wrangling - WeRateDogs
This project was part of the data wrangling section of the Udacity Data Analyst Nanodegree program. And it primarily focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook. This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater than 10. This aspect was not cleaned as it is part of the humor and popularity of WeRateDogs. For this project, we only wanted original ratings (no retweets or replies) that have images. Not all of the original tweets in the dataset are dog ratings and some are retweets. Fully assessing and cleaning the entire dataset would require exceptional effort so only a subset of its issues (eight quality issues and two tidiness issues at minimum) needed to be assessed and cleaned.
Data Wrangling - WeRateDogs
This project was part of the data wrangling section of the Udacity Data Analyst Nanodegree program. And it primarily focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook. This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater than 10. This aspect was not cleaned as it is part of the humor and popularity of WeRateDogs. For this project, we only wanted original ratings (no retweets or replies) that have images. Not all of the original tweets in the dataset are dog ratings and some are retweets. Fully assessing and cleaning the entire dataset would require exceptional effort so only a subset of its issues (eight quality issues and two tidiness issues at minimum) needed to be assessed and cleaned.
Data Wrangling - WeRateDogs
7235011
Using Python, Pandas & Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc. I was trying to answer these questions: 1. What was the best month for sales? How much was earned that month? 2. What city sold the most product? 3. What time should we display advertisements to maximize the likelihood of customers buying products? 4. What products are most often sold together? 5. What product sold the most? Why do you think it sold the most?
Sales Anaysis
Using Python, Pandas & Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc. I was trying to answer these questions: 1. What was the best month for sales? How much was earned that month? 2. What city sold the most product? 3. What time should we display advertisements to maximize the likelihood of customers buying products? 4. What products are most often sold together? 5. What product sold the most? Why do you think it sold the most?
Sales Anaysis
Using Python, Pandas & Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc. I was trying to answer these questions: 1. What was the best month for sales? How much was earned that month? 2. What city sold the most product? 3. What time should we display advertisements to maximize the likelihood of customers buying products? 4. What products are most often sold together? 5. What product sold the most? Why do you think it sold the most?
Sales Anaysis
Using Python, Pandas & Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc. I was trying to answer these questions: 1. What was the best month for sales? How much was earned that month? 2. What city sold the most product? 3. What time should we display advertisements to maximize the likelihood of customers buying products? 4. What products are most often sold together? 5. What product sold the most? Why do you think it sold the most?
Sales Anaysis
8526522
A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these. For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. My goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.
A/B testing
A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these. For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. My goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.
A/B testing
A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these. For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. My goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.
A/B testing
9427034
AWS Certified ML Specialty: Earners of this certification have an in-depth understanding of AWS machine learning (ML) services. They demonstrated ability to build, train, tune, and deploy ML models using the AWS Cloud. Badge owners can derive insight from AWS ML services using either pretrained models or custom models built from open-source frameworks. Verify: https://www.credly.com/badges/29fc5b80-8619-48ff-a842-552136b8358d/public_url
AWS Certified ML Specialty
AWS Certified ML Specialty: Earners of this certification have an in-depth understanding of AWS machine learning (ML) services. They demonstrated ability to build, train, tune, and deploy ML models using the AWS Cloud. Badge owners can derive insight from AWS ML services using either pretrained models or custom models built from open-source frameworks. Verify: https://www.credly.com/badges/29fc5b80-8619-48ff-a842-552136b8358d/public_url
AWS Certified ML Specialty
8550577
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
Airlines Delays Analysis: This project has two parts that demonstrate the importance and value of data visualization techniques in the data analysis process. In the first part, I used Python visualization libraries to systematically explore a selected dataset, starting from plots of single variables and building up to plots of multiple variables. In the second part, I produced a short presentation that illustrates interesting properties, trends, and relationships that you discovered in the selected dataset.
Data Visualization
7868253
I‘ve successfully completed Data Analysis and Deep Learning Analysis Nanodegrees from Udacity. Data Analysis Professional and Advanced: Throughout this journey, I have developed outstanding skills in Python and its libraries (Numpy, Pandas, Matplotlib…) alongside understanding the steps of the data analysis process and how to gather and wrangle data using python libraries to get it clean and ready for further analysis and visualization. Also, apply inferential statistics and probability to real-world scenarios, create and analyze the results of hypothesis testing, create and analyze A/B test results, and build regression models in Python. Deep Learning: I have developed outstanding skills in machine learning and deep learning. Got introduced to neural networks, Learned about CNNs for image recognition, RNNs for sequence generation, and GANs for image generation. Also, Model deployment through AWS SageMaker.
Certificates
I‘ve successfully completed Data Analysis and Deep Learning Analysis Nanodegrees from Udacity. Data Analysis Professional and Advanced: Throughout this journey, I have developed outstanding skills in Python and its libraries (Numpy, Pandas, Matplotlib…) alongside understanding the steps of the data analysis process and how to gather and wrangle data using python libraries to get it clean and ready for further analysis and visualization. Also, apply inferential statistics and probability to real-world scenarios, create and analyze the results of hypothesis testing, create and analyze A/B test results, and build regression models in Python. Deep Learning: I have developed outstanding skills in machine learning and deep learning. Got introduced to neural networks, Learned about CNNs for image recognition, RNNs for sequence generation, and GANs for image generation. Also, Model deployment through AWS SageMaker.
Certificates
I‘ve successfully completed Data Analysis and Deep Learning Analysis Nanodegrees from Udacity. Data Analysis Professional and Advanced: Throughout this journey, I have developed outstanding skills in Python and its libraries (Numpy, Pandas, Matplotlib…) alongside understanding the steps of the data analysis process and how to gather and wrangle data using python libraries to get it clean and ready for further analysis and visualization. Also, apply inferential statistics and probability to real-world scenarios, create and analyze the results of hypothesis testing, create and analyze A/B test results, and build regression models in Python. Deep Learning: I have developed outstanding skills in machine learning and deep learning. Got introduced to neural networks, Learned about CNNs for image recognition, RNNs for sequence generation, and GANs for image generation. Also, Model deployment through AWS SageMaker.
Certificates
7235036
This project was part of the data wrangling section of the Udacity Data Analyst Nanodegree program. And it primarily focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook. This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater than 10. This aspect was not cleaned as it is part of the humor and popularity of WeRateDogs. For this project, we only wanted original ratings (no retweets or replies) that have images. Not all of the original tweets in the dataset are dog ratings and some are retweets. Fully assessing and cleaning the entire dataset would require exceptional effort so only a subset of its issues (eight quality issues and two tidiness issues at minimum) needed to be assessed and cleaned.
Data Wrangling - WeRateDogs
This project was part of the data wrangling section of the Udacity Data Analyst Nanodegree program. And it primarily focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook. This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater than 10. This aspect was not cleaned as it is part of the humor and popularity of WeRateDogs. For this project, we only wanted original ratings (no retweets or replies) that have images. Not all of the original tweets in the dataset are dog ratings and some are retweets. Fully assessing and cleaning the entire dataset would require exceptional effort so only a subset of its issues (eight quality issues and two tidiness issues at minimum) needed to be assessed and cleaned.
Data Wrangling - WeRateDogs
This project was part of the data wrangling section of the Udacity Data Analyst Nanodegree program. And it primarily focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook. This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater than 10. This aspect was not cleaned as it is part of the humor and popularity of WeRateDogs. For this project, we only wanted original ratings (no retweets or replies) that have images. Not all of the original tweets in the dataset are dog ratings and some are retweets. Fully assessing and cleaning the entire dataset would require exceptional effort so only a subset of its issues (eight quality issues and two tidiness issues at minimum) needed to be assessed and cleaned.
Data Wrangling - WeRateDogs
This project was part of the data wrangling section of the Udacity Data Analyst Nanodegree program. And it primarily focused on wrangling data from the WeRateDogs Twitter account using Python, documented in a Jupyter Notebook. This Twitter account rates dogs with humorous commentary. The rating denominator is usually 10, however, the numerators are usually greater than 10. This aspect was not cleaned as it is part of the humor and popularity of WeRateDogs. For this project, we only wanted original ratings (no retweets or replies) that have images. Not all of the original tweets in the dataset are dog ratings and some are retweets. Fully assessing and cleaning the entire dataset would require exceptional effort so only a subset of its issues (eight quality issues and two tidiness issues at minimum) needed to be assessed and cleaned.
Data Wrangling - WeRateDogs
7235011
Using Python, Pandas & Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc. I was trying to answer these questions: 1. What was the best month for sales? How much was earned that month? 2. What city sold the most product? 3. What time should we display advertisements to maximize the likelihood of customers buying products? 4. What products are most often sold together? 5. What product sold the most? Why do you think it sold the most?
Sales Anaysis
Using Python, Pandas & Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc. I was trying to answer these questions: 1. What was the best month for sales? How much was earned that month? 2. What city sold the most product? 3. What time should we display advertisements to maximize the likelihood of customers buying products? 4. What products are most often sold together? 5. What product sold the most? Why do you think it sold the most?
Sales Anaysis
Using Python, Pandas & Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc. I was trying to answer these questions: 1. What was the best month for sales? How much was earned that month? 2. What city sold the most product? 3. What time should we display advertisements to maximize the likelihood of customers buying products? 4. What products are most often sold together? 5. What product sold the most? Why do you think it sold the most?
Sales Anaysis
Using Python, Pandas & Matplotlib to analyze and answer business questions about 12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by month, product type, cost, purchase address, etc. I was trying to answer these questions: 1. What was the best month for sales? How much was earned that month? 2. What city sold the most product? 3. What time should we display advertisements to maximize the likelihood of customers buying products? 4. What products are most often sold together? 5. What product sold the most? Why do you think it sold the most?
Sales Anaysis
8526522
A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these. For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. My goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.
A/B testing
A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these. For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. My goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.
A/B testing
A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these. For this project, I will be working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. My goal is to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.
A/B testing
9427034
AWS Certified ML Specialty: Earners of this certification have an in-depth understanding of AWS machine learning (ML) services. They demonstrated ability to build, train, tune, and deploy ML models using the AWS Cloud. Badge owners can derive insight from AWS ML services using either pretrained models or custom models built from open-source frameworks. Verify: https://www.credly.com/badges/29fc5b80-8619-48ff-a842-552136b8358d/public_url
AWS Certified ML Specialty
AWS Certified ML Specialty: Earners of this certification have an in-depth understanding of AWS machine learning (ML) services. They demonstrated ability to build, train, tune, and deploy ML models using the AWS Cloud. Badge owners can derive insight from AWS ML services using either pretrained models or custom models built from open-source frameworks. Verify: https://www.credly.com/badges/29fc5b80-8619-48ff-a842-552136b8358d/public_url
AWS Certified ML Specialty

Comentarios

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5,0
$1.000,00 USD
Exploratory Data Analysis
They provided an entry that fulfilled the requirements of the contest
Data Analysis
Udacity
hace 3 años
5,0
$1.000,00 USD
Data Science Predictive Model
They provided an entry that fulfilled the requirements of the contest
Udacity
hace 3 años
5,0
$30,00 USD
Teach me Visual Studio code with Python Pandas
Muhammad is a nice person. He tried his best to help others and also not giving up and he is also a generous people by giving some study material reference in the future. I wish a very bright day to his career in the future
Python
Excel
Data Science
Coding
NumPy
Avatar del usuario
Bandera de Edward W.
@edwonk123
•
hace 3 años
5,0
$35,00 AUD
Review python code and make functional
Excellent experience working with Muhammad, was very friendly, kept me updated and informed and worked within the time constraint. His python work was exceptional and covered all basis. I highly recommend Muhammad and will look to use him again in the future if I require any python work!
Python
Software Architecture
Machine Learning (ML)
Neural Networks
Tensorflow
D
Bandera de David G.
@davidgarip
•
hace 3 años
5,0
$500,00 USD
Blog Post to highlight the success of the Egypt FWD International Freelancer Contest (IFC) to date
Muhammad was great to work with would recommend
Christmas
G
Closed User
@gperks
•
hace 3 años

Experiencia

Python Developer and Data Analyst

Freelance
dic 2019 - Presente
Junior python developer and data analyst with more than one year of experience. What can I do? • Make scripts for solving problems and automating task using different python's libraries. • Gather data from different resources in different formats. • Assess and clean the data. • Analyze and visualize the data.

Structural BIM Engineer

IHCG
jun 2017 - Presente
Responsibilities: • Carry out a structural design of concrete and steel structures. • Use different analysis and modeling software and tools. • Participating in BIM process and models. • Prepare and export detailed drawings. • Prepare reports; design & calculation notes. and BOQ lists.

Educación

Bachelor of Science (B.Sc)

Tanta University, Egypt 2011 - 2016
(5 años)

Calificaciones

AWS Certified Machine Learning – Specialty

Amazon Web Services
2021
Earners of this certification have an in-depth understanding of AWS machine learning (ML) services. They demonstrated the ability to build, train, tune, and deploy ML models using the AWS Cloud. Badge owners can derive insight from AWS ML services using either pre-trained models or custom models built from open-source frameworks. Verify: https://www.credly.com/badges/29fc5b80-8619-48ff-a842-552136b8358d/public_url

Deep Learning

Udacity
2021
Learning Topics: • Introduction to neural networks using NumPy and PyTorch. • Convolutional networks (CNNs) • Recurrent networks (RNNs). • Generative Adversarial Networks (GANs) • Model Deployment: using AWS SageMaker. • Certification: https://confirm.udacity.com/XRPQYPGS

Advanced Data Analysis Nanodegree Program

Udacity
2020
Learning Topics: • Practical Statistics: Apply inferential statistics and probability to real-world scenarios, create and analyze the results of hypothesis testing, create and analyze A/B test results, and build regression models in Python. • Data Visualization: Build visualizations using different design elements to explore the data and communicate insights. • Certificate: https://graduation.udacity.com/confirm/WZKMZUJD

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Principales habilidades

Python 11
Coding 3
Web Scraping 2
Software Architecture 2
Docker 1

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