Pinned Repositories
MLP-for-regression-model
A project directed to develop an Artificial Neural Network or Multi-Layer Perceptron for a regression model, able to predict the miles per gallon indicator of an auto, using Pytorch.
amazon-data-analysis
A project aiming to (1) explore the original Amazon dataset, (2) clean and preprocess its numeric and textual data to generate a cleaned set, and (3) analyze this cleaned version, using Pandas.
dataquest-python-fundamentals
This is the final project of the Dataquest's Python Fundamentals course. The objective is to analyze the google playstore database with pure Python, avoiding the use of libraries such as Pandas.
100-python-exercises-challenge
It contains my solutions to the 100+ python exercises challenge, proposed initially by zhiwehu as a practice of python 2, and then collected and modified to python 3 solutions by darkprinx.
alien-invasion-pygame
A project intending to create a simple 2D videogame, based on the "Python Crash Course" book by Eric Matthes, using Pygame.
convolutional-autoencoder
A project that (1) applies a convolutional autoencoder to extract a reduced number of critical features of signals and (2) tests three machine learning classification methods, using Keras and Sklearn.
learning-log-web-app-django
A project aiming to create a simple web app, based on the "Python Crash Course" book by Eric Matthes, using Django and HTML.
python-script-to-executable
The following project converts a Pygame script into an executable app with cx_Freeze module so that anyone who does not have Python or Pygame installed, can run and play it.
titanic-data-analysis
A project developed by Mineros DataLab aiming to (1) explore the original Titanic database, (2) clean and preprocess it to generate a cleaned set, and (3) analyze this cleaned database, using Pandas.
titanic-kaggle-ML-competition
The code I developed for the legendary Titanic Machine Learning competition on Kaggle, where out of 50,000 participants, I ranked in the top 5%. Sklearn, Pandas, Seaborn, Tensorflow and more...
wlemusl's Repositories
wlemusl/learning-log-web-app-django
A project aiming to create a simple web app, based on the "Python Crash Course" book by Eric Matthes, using Django and HTML.
wlemusl/titanic-kaggle-ML-competition
The code I developed for the legendary Titanic Machine Learning competition on Kaggle, where out of 50,000 participants, I ranked in the top 5%. Sklearn, Pandas, Seaborn, Tensorflow and more...
wlemusl/100-python-exercises-challenge
It contains my solutions to the 100+ python exercises challenge, proposed initially by zhiwehu as a practice of python 2, and then collected and modified to python 3 solutions by darkprinx.
wlemusl/MLP-for-regression-model
A project directed to develop an Artificial Neural Network or Multi-Layer Perceptron for a regression model, able to predict the miles per gallon indicator of an auto, using Pytorch.
wlemusl/python-script-to-executable
The following project converts a Pygame script into an executable app with cx_Freeze module so that anyone who does not have Python or Pygame installed, can run and play it.
wlemusl/alien-invasion-pygame
A project intending to create a simple 2D videogame, based on the "Python Crash Course" book by Eric Matthes, using Pygame.
wlemusl/convolutional-autoencoder
A project that (1) applies a convolutional autoencoder to extract a reduced number of critical features of signals and (2) tests three machine learning classification methods, using Keras and Sklearn.
wlemusl/dataquest-python-fundamentals
This is the final project of the Dataquest's Python Fundamentals course. The objective is to analyze the google playstore database with pure Python, avoiding the use of libraries such as Pandas.
wlemusl/titanic-data-analysis
A project developed by Mineros DataLab aiming to (1) explore the original Titanic database, (2) clean and preprocess it to generate a cleaned set, and (3) analyze this cleaned database, using Pandas.
wlemusl/amazon-data-analysis
A project aiming to (1) explore the original Amazon dataset, (2) clean and preprocess its numeric and textual data to generate a cleaned set, and (3) analyze this cleaned version, using Pandas.