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Database Administration
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Python
R Programming Language
Web Scraping
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Felhasználó avatár
$50 USD / óra
BELGIUM zászlója
beerzel, belgium
$50 USD / óra
Itt jelenleg ennyi az idő: 6:47 du.
Ekkor csatlakozott: június 14, 2018
0 Ajánlások

Axel V.

@vdvaxel

 5,0 (15 értékelés)
4,7
4,7
100%
100%
$50 USD / óra
BELGIUM zászlója
beerzel, belgium
$50 USD / óra
100%
Teljesített megbízások
100%
Költségvetésen belül
100%
Határidőn belül
14%
Ismételt megbízási arány

Expert in Python, Web Scraping, AI/ML

Hi there! My name is Axel, I'm from Belgium and I am enthusiastic about anything to do with data, analytics, big data, artificial intelligence, machine learning and writing code (preferably in Python). My past projects on Freelancer.com usually had to do with - web scraping/automation - machine learning/data science/artificial intelligence/deep learning - sentiment analysis/natural language processing/text mining - optical character recognition (OCR) - big data topics/Apache Spark - SQL - general Python scripts to automate things - progressive web apps (Angular, Ionic, HTML5, CSS) If you need help in any of these areas, I will be more than happy to help!
Freelancer Python Developers Belgium
Az összes Python Developers mutatása itt: Belgium

Keresse Axel V. felhasználót a munkájával kapcsolatban

Jelentkezzen be, hogy chaten keresztül megbeszélhessék a részleteket.

Portfólió

4354589
1) Scrape CMS website (Centers for Medicare & Medicaid Services) 2) Download ZIP files from this website automatically and extract all files 3) Process the extracted CSV files with Pandas dataframes (dropping some columns, etc.) 4) Create summary tables (change in variables compared to 1 year ago, 2 years and 3 years) using Pandas dataframes as well and writing the result to a CSV file All of these scripts have been written in Python. Both the Python files as well as executable files generated from the Python files were delivered to the employer.
Scraping and Processing Government Data
4346787
This project involved creating a machine learning model from any dataset that can be found online, using the R programming language and using R Sweave in particular to write a detailed report with every step explained and the findings. I used a customer churn dataset from a telecom company to product who will and who will not churn (i.e. leaving the company as a customer). For the model I used a simple white box decision tree because of its understandability and because it still performs really well (90+% accuracy) compared to a black box model such as SVM or neural networks. Evaluation of the models was done using 10-fold cross validation and by measuring accuracy and drawing the ROC curve/measuring AUC. It also includes visualizations to visualize the dataset that is being used. The images shown on the left are an excerpt of the report that was written and the code behind it. This is obviously not the entire code delivered for this project.
Machine Learning in R
This project involved creating a machine learning model from any dataset that can be found online, using the R programming language and using R Sweave in particular to write a detailed report with every step explained and the findings. I used a customer churn dataset from a telecom company to product who will and who will not churn (i.e. leaving the company as a customer). For the model I used a simple white box decision tree because of its understandability and because it still performs really well (90+% accuracy) compared to a black box model such as SVM or neural networks. Evaluation of the models was done using 10-fold cross validation and by measuring accuracy and drawing the ROC curve/measuring AUC. It also includes visualizations to visualize the dataset that is being used. The images shown on the left are an excerpt of the report that was written and the code behind it. This is obviously not the entire code delivered for this project.
Machine Learning in R
This project involved creating a machine learning model from any dataset that can be found online, using the R programming language and using R Sweave in particular to write a detailed report with every step explained and the findings. I used a customer churn dataset from a telecom company to product who will and who will not churn (i.e. leaving the company as a customer). For the model I used a simple white box decision tree because of its understandability and because it still performs really well (90+% accuracy) compared to a black box model such as SVM or neural networks. Evaluation of the models was done using 10-fold cross validation and by measuring accuracy and drawing the ROC curve/measuring AUC. It also includes visualizations to visualize the dataset that is being used. The images shown on the left are an excerpt of the report that was written and the code behind it. This is obviously not the entire code delivered for this project.
Machine Learning in R
4346759
This project involved Jython, which is an implementation of the Python programming language designed to run on the Java platform, and JES, which is the Jython Environment for Students. The objective was relatively straightforward: to create a "world" with a "turtle" that you can move left, right, up and down and draw shapes as the turtle leaves a line when it moves.
Drawing in Jython
4346745
A relatively simple tool to scrape ALL URLs from a given website. Note: the code shown on the picture on the left is just a placeholder and is not the entire code delivered for this project.
Link Scraper
4346737
This project involved scripting various operations with RDDs (Resilient Distributed Datasets) in Apache Spark and using the MapReduce framework (see pictures).
Apache Spark Assignment
This project involved scripting various operations with RDDs (Resilient Distributed Datasets) in Apache Spark and using the MapReduce framework (see pictures).
Apache Spark Assignment
This project involved scripting various operations with RDDs (Resilient Distributed Datasets) in Apache Spark and using the MapReduce framework (see pictures).
Apache Spark Assignment
4346718
This Python script uses various machine learning algorithms (such as support vector machines, random forests, neural networks, etc.) to automatically classify dog breeds. The model will first train itself using the dataset that is provided to it (various dog breeds and pictures of that specific dog breed) after which you can give a new image to the model and it will tell you the breed of the dog by writing it in the image (as can be see on the picture to the left).
Dog Image Classifier
4354589
1) Scrape CMS website (Centers for Medicare & Medicaid Services) 2) Download ZIP files from this website automatically and extract all files 3) Process the extracted CSV files with Pandas dataframes (dropping some columns, etc.) 4) Create summary tables (change in variables compared to 1 year ago, 2 years and 3 years) using Pandas dataframes as well and writing the result to a CSV file All of these scripts have been written in Python. Both the Python files as well as executable files generated from the Python files were delivered to the employer.
Scraping and Processing Government Data
4346787
This project involved creating a machine learning model from any dataset that can be found online, using the R programming language and using R Sweave in particular to write a detailed report with every step explained and the findings. I used a customer churn dataset from a telecom company to product who will and who will not churn (i.e. leaving the company as a customer). For the model I used a simple white box decision tree because of its understandability and because it still performs really well (90+% accuracy) compared to a black box model such as SVM or neural networks. Evaluation of the models was done using 10-fold cross validation and by measuring accuracy and drawing the ROC curve/measuring AUC. It also includes visualizations to visualize the dataset that is being used. The images shown on the left are an excerpt of the report that was written and the code behind it. This is obviously not the entire code delivered for this project.
Machine Learning in R
This project involved creating a machine learning model from any dataset that can be found online, using the R programming language and using R Sweave in particular to write a detailed report with every step explained and the findings. I used a customer churn dataset from a telecom company to product who will and who will not churn (i.e. leaving the company as a customer). For the model I used a simple white box decision tree because of its understandability and because it still performs really well (90+% accuracy) compared to a black box model such as SVM or neural networks. Evaluation of the models was done using 10-fold cross validation and by measuring accuracy and drawing the ROC curve/measuring AUC. It also includes visualizations to visualize the dataset that is being used. The images shown on the left are an excerpt of the report that was written and the code behind it. This is obviously not the entire code delivered for this project.
Machine Learning in R
This project involved creating a machine learning model from any dataset that can be found online, using the R programming language and using R Sweave in particular to write a detailed report with every step explained and the findings. I used a customer churn dataset from a telecom company to product who will and who will not churn (i.e. leaving the company as a customer). For the model I used a simple white box decision tree because of its understandability and because it still performs really well (90+% accuracy) compared to a black box model such as SVM or neural networks. Evaluation of the models was done using 10-fold cross validation and by measuring accuracy and drawing the ROC curve/measuring AUC. It also includes visualizations to visualize the dataset that is being used. The images shown on the left are an excerpt of the report that was written and the code behind it. This is obviously not the entire code delivered for this project.
Machine Learning in R
4346759
This project involved Jython, which is an implementation of the Python programming language designed to run on the Java platform, and JES, which is the Jython Environment for Students. The objective was relatively straightforward: to create a "world" with a "turtle" that you can move left, right, up and down and draw shapes as the turtle leaves a line when it moves.
Drawing in Jython
4346745
A relatively simple tool to scrape ALL URLs from a given website. Note: the code shown on the picture on the left is just a placeholder and is not the entire code delivered for this project.
Link Scraper
4346737
This project involved scripting various operations with RDDs (Resilient Distributed Datasets) in Apache Spark and using the MapReduce framework (see pictures).
Apache Spark Assignment
This project involved scripting various operations with RDDs (Resilient Distributed Datasets) in Apache Spark and using the MapReduce framework (see pictures).
Apache Spark Assignment
This project involved scripting various operations with RDDs (Resilient Distributed Datasets) in Apache Spark and using the MapReduce framework (see pictures).
Apache Spark Assignment
4346718
This Python script uses various machine learning algorithms (such as support vector machines, random forests, neural networks, etc.) to automatically classify dog breeds. The model will first train itself using the dataset that is provided to it (various dog breeds and pictures of that specific dog breed) after which you can give a new image to the model and it will tell you the breed of the dog by writing it in the image (as can be see on the picture to the left).
Dog Image Classifier

Értékelések

Változtatások elmentve
1 - 5 / 12 értékelés látható
Vélemények szűrése ez alapján:
5,0
$200,00 USD
Scrape website and process data with python and pandas. Agile experience a plus.
Very responsive and professional. Takes the time to understand the project on the front end. [14 August, 2018] Very professional and quick!
Python
Web Scraping
S
zászlója Todd D.
@SMAtodd
•
6 évvel ezelőtt
5,0
$98,00 USD
Project for Axel V. -- 3
excellent work
Python
Web Scraping
Machine Learning (ML)
Database Administration
+1 további készség
O
zászlója Oscar C.
@oscontreras84
•
6 évvel ezelőtt
5,0
$115,00 USD
Project for Axel V. -- 2
excellent work!
Python
Web Scraping
Machine Learning (ML)
Database Administration
+1 további készség
O
zászlója Oscar C.
@oscontreras84
•
6 évvel ezelőtt
5,0
$111,11 USD
i need a python developer
excellent work
Python
O
zászlója Oscar C.
@oscontreras84
•
6 évvel ezelőtt
5,0
$210,00 USD
Sentiment Analysis Tool in Arabic
Smart Person
PHP
JavaScript
Python
Software Architecture
Machine Learning (ML)
M
Closed User
@MasterKayal
•
6 évvel ezelőtt

Tapasztalat

Digital Business Integration Analyst

Global IT consulting firm
szept. 2017 - Jelenleg
Global IT consulting company with more than 450,000 employees worldwide. I am part of the Digital practice and more specifically the Analytics team. In this team, I mainly focus on Big Data.

Tanulmányok

Magna Cum Laude in Information Systems Engineering

Katholieke Universiteit Leuven, Belgium 2012 – 2017
(5 év)

Végzettségek

CCA175 Certified Spark and Hadoop Developer

Cloudera
2018
Cloudera certified Apache Spark and Hadoop developer (Python/Scala)

Keresse Axel V. felhasználót a munkájával kapcsolatban

Jelentkezzen be, hogy chaten keresztül megbeszélhessék a részleteket.

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Képesítések

us_eng_1.png US English 1 82%

Kiemelt készségek

Python 15
Web Scraping 10
Database Administration 4
Programming 4
R Programming Language 2

Hasonló szabadúszók keresése

Python Developers in Belgium
Python Developers
Web Scraping Specialists
Database Administrators

Hasonló bemutatók böngészése

Python
Web Scraping
Database Administration
Programming
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