Popular GitHub Repositories for Predicting Housing Prices

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code ?.

Predict stock market prices using RNN model with multilayer LSTM cells + optional multi-stock embeddings.

Using python and scikit-learn to make stock predictions

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Train and Deploy an ML REST API to predict crypto prices, in 10 steps

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

Introducing neural networks to predict stock prices

Implementation of MEGABYTE, Predicting Million-byte Sequences with Multiscale Transformers, in Pytorch

Exploiting Bitcoin prices patterns with Deep Learning.

Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

Simple baselines and RNNs for predicting human motion in tensorflow. Presented at CVPR 17.

A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"

A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

PyTorch implementation of "Super-Realtime Facial Landmark Detection and Shape Fitting by Deep Regression of Shape Model Parameters" predicting facial landmarks with up to 400 FPS

Predicting price trends in cryptomarkets using an lstm-RNN for the use of a trading bot

Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker

Predicting depression from acoustic features of speech using a Convolutional Neural Network.

Flask API for training and predicting using scikit learn models

Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).

Acquiring data from Alpha Vantage and predicting stock prices with PyTorch's LSTM