Allsian's repositories
amazon-scrapper-1M
An amazon scrapper to scrape 1 million products {title, price, images}
Awesome_Machine_Learning_Solutions
A curated list of repositories for my book Machine Learning Solutions.
capm_shiny
Demo project of creating an interactive analytical tool for stock market using CAPM.
cheatsheets-1
RStudio Cheat Sheets
cifar-extender
Extend the CIFAR10 dataset with ImageNet data.
d3
Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:
handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
healthcare-workforce
A repository for healthcare workforce modelling.
Kaggle-Human-or-Robot
Kaggle Project. Facebook Recruiting IV: Human or Robot? | DataRoot University (2017)
libffm
A Library for Field-aware Factorization Machines
machine-learning-notebooks
Assorted exercises and proof-of-concepts to understand and study machine learning and statistical learning theory
Machine-Learning-with-Python
Python codes for common Machine Learning Algorithms
Marketing-Analytics
Complete machine learning analysis to solve marketing problems.
NLP-with-Python
Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more
paper-tips-and-tricks
Best practice and tips & tricks to write scientific papers in LaTeX, with figures generated in Python or Matlab.
pattern_classification
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
python-cheatsheet
Comprehensive Python Cheatsheet
pytorch-pretrained-BERT
đź“–The Big-&-Extending-Repository-of-Transformers: Pretrained PyTorch models for Google's BERT, OpenAI GPT & GPT-2, Google/CMU Transformer-XL.
shiny-textual-sentiment
A Shiny app that visualizes and let's you play with textual sentiment time series
stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
TagAnomaly
Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)
text-preprocessing-techniques
16 Text Preprocessing Techniques in Python for Twitter Sentiment Analysis.
Time-series-analysis-of-Inflation-rates-using-ShinyDashboard
This project will aim at studying and analyzing the inflation rates of countries globally. The dataset is a public dataset downloaded from International Monetary Fund(IMF) which consists of the inflation rates of countries from 1980 to 2017 and the projected inflation rates of the countries till 2022. Finally I will be producing a dashboard build in R to visualize and analyze the inflation rates.