Predicting Bitcoin prices using a multi-dimensional LSTM and sentiment analysis.
Crop Prediction from Linear Regression to Gaussian Processes, with Model Interpretability
Data Analysis and machine learning with Boston Housing Market Data
Housing in Boston - let's model it and put it on the map!
This project predicts changes in NOx levels that occur when developing low income towns simulated using bootstrapped data from higher income towns while keeping geographical constraints fixed.
Predicts crop yield for county given temperature and EVI values in the current year
Used LSTM neural network for regression, to achieve 68% accuracy score at predicting 2019 crop yield, from historical data
Predicting crop yield in the state of Illinois
Forecast Volatility of Cryptocurrency Prices
High accuracy predictions with easy-to-gather data.
Crop yields are not exempt of suffering from the manifold damaging effects climate change. Extreme weather is more common now than ages ago, reason for which it is required to model such behaviour.
Regression and Classification on the Dataset to produce some unique insights
Estimate crop yield in Illionois
Need to predict yields? We got you covered
Using longitude and latitude data with the Boston 1978 housing data set to predict various other values using a set of nested neural networks.
Cryptocurency price forecasting using random forests and LSTM NN.Using a simple random forest we are able to select the best features that will be taken into account for an advanced model.
We made a highly efficient triangular arbitrage strategy to generate profits and analysed it against the crypto-currency datasets
Corn crop yields show monotonically increasing trend with disaster-related dips and evident geographical clustering. We develop an Attention-based DNN that can effectively predict negative events.
This is not a real project, but enjoy the cat livestream...
Predicting price changes based on order book data and spot data
utilize the LSTM model to predict corp yield
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