raedjamw

AWS-Docker-Flask-Machine-Learning-App

BS_Project

Flask App to Predict Bitcoin Prices using Deep Learning LSTM RNN

building-machine-learning-pipelines

Code repository for the O'Reilly publication "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson

ebookseller

Node.js, Express Ebook sale using Stripe API

Fake-News-Classifier

This is a Fake News Classifier Using the following methods-Random Forest Classifier,Multinational Naive Bayes, Decision Trees

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.

fraud-detection

Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

Fraud-Detection-Classification-Project

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.

raedjamw/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.

raedjamw/AWS-Docker-Flask-Machine-Learning-App

raedjamw/BS_Project

Flask App to Predict Bitcoin Prices using Deep Learning LSTM RNN

raedjamw/building-machine-learning-pipelines

Code repository for the O'Reilly publication "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson

raedjamw/Fake-News-Classifier

This is a Fake News Classifier Using the following methods-Random Forest Classifier,Multinational Naive Bayes, Decision Trees

raedjamw/fashion-generation-mnist_GAN

Generating novel fashion designs with the mnist dataset using GANs

raedjamw/fashion_mnist_gan

This repository lets you generate new not seen clothes by using a gan with the fashion mnist dataset.

raedjamw/Fraud-Detection-Classification-Project

raedjamw/image_classification

using mobilenet on tensorflow.js to classify images

raedjamw/Imputation-Techniques

Fast, efficient code to pull non-null categorical data out, encode it and impute nulls with KNN Impute from fancyimpute library

raedjamw/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

raedjamw/ml-engineering-challenge

raedjamw/Open-Source-Spotlight

Introductory notebooks used in my videos, covering great open-source Python packages.

raedjamw/Pneumonia-Detection-Classification-Transfer-Learning

Pneumonia Detection Classification-Transfer Learning

raedjamw/pneumonia_detection

Using transfer learning and fine tuning to predict pneunomia. Base model is the inception V3

raedjamw/post-tuto-deployment

Build and deploy a machine learning app from scratch 🚀

raedjamw/pyBKB_v3

Python scripts that help me be a successfull meteorologist. (Python 3)

raedjamw/python-machine-learning-book-3rd-edition

The "Python Machine Learning (3rd edition)" book code repository

raedjamw/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:

raedjamw/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

raedjamw/REST_API_for_fraud-detection

A logistic regression model that detects the fraud in online transactions that can be accesed with a REST API

raedjamw/Road-Accident-Prediction

raedjamw/SAGAN-Fashion

Generate spontenous fashion ideas with Self-Attention Generative Adversarial Networks

raedjamw/social-media-tutorials

Image/Code dump of Twitter/Youtube tutorials

raedjamw/song_recommendation

raedjamw/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).

raedjamw/Stock-MArket-Forecasting

raedjamw/stock-prediction

Stock prediction web app using flask, pandas, scikit-learn and am charts

raedjamw/stock-prediction-comparing-multiple-regression-models

A stock prediction project comparing different regression models

raedjamw/Unsupervized-ML-Chameleon-Trucking-Project

Using Unsupervised Machine Learning(KNN) to determine Trucking Companies Across the US