Rae Wallace's repositories
social-media-tutorials
Image/Code dump of Twitter/Youtube tutorials
building-machine-learning-pipelines
Code repository for the O'Reilly publication "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson
Open-Source-Spotlight
Introductory notebooks used in my videos, covering great open-source Python packages.
BS_Project
Flask App to Predict Bitcoin Prices using Deep Learning LSTM RNN
Unsupervized-ML-Chameleon-Trucking-Project
Using Unsupervised Machine Learning(KNN) to determine Trucking Companies Across the US
Real_Estate_House_Price_Prediction
Using Multiple Advanced Regression Techniques to Predicting the Value of Homes.It is based on 79 different factors. This project covers a wide range of important concepts in a data science lifecycle such as:
Fake-News-Classifier
This is a Fake News Classifier Using the following methods-Random Forest Classifier,Multinational Naive Bayes, Decision Trees
Reddit-news-sentiment-analysis-for-stock-prediction
Reddit news sentiment analysis for stock prediction Random Forest ,Naive Bayes, Decision Trees, TF-IDK,Bag of Words
live_stock_scrape
The purpose of this code is to scrape real time stocks prices and plot them dynamical:This First piece of code is just the scraping
python-machine-learning-book-3rd-edition
The "Python Machine Learning (3rd edition)" book code repository
Pension-Fund-webscraping
Using #beautifulsoup packages to scrape a table of Pension Fund data on multiple urls, converting them to pandas dataframes and then to one excel doc.
pyBKB_v3
Python scripts that help me be a successfull meteorologist. (Python 3)
Stanford-Project-Predicting-stock-prices-using-a-LSTM-Network
Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
post-tuto-deployment
Build and deploy a machine learning app from scratch 🚀
Imputation-Techniques
Fast, efficient code to pull non-null categorical data out, encode it and impute nulls with KNN Impute from fancyimpute library
fashion-generation-mnist_GAN
Generating novel fashion designs with the mnist dataset using GANs
fashion_mnist_gan
This repository lets you generate new not seen clothes by using a gan with the fashion mnist dataset.
SAGAN-Fashion
Generate spontenous fashion ideas with Self-Attention Generative Adversarial Networks
Pneumonia-Detection-Classification-Transfer-Learning
Pneumonia Detection Classification-Transfer Learning
REST_API_for_fraud-detection
A logistic regression model that detects the fraud in online transactions that can be accesed with a REST API
pneumonia_detection
Using transfer learning and fine tuning to predict pneunomia. Base model is the inception V3
image_classification
using mobilenet on tensorflow.js to classify images
stock-prediction-comparing-multiple-regression-models
A stock prediction project comparing different regression models
stock-prediction
Stock prediction web app using flask, pandas, scikit-learn and am charts