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adaboost.m1

blast4go

A fast and light-weighted bioinformatics pipeline for high-quality protein function prediction and functional analysis of genomic datasets

california-housing-prices-prediction

Optimized hyperparameters in a Random Forest model (RFM) to achieve a certain error tolerance (MSE criterion) using Python

copula-dyn-pred

Code for "A Gaussian copula approach for dynamic prediction of survival with a longitudinal biomarker"

cu-ssp

Implementation of High Quality Protein Q8 Secondary Structure Prediction by Diverse Neural Network Architectures

cu-tsp

Implementation of Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

deep-convolution-stock-technical-analysis

Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.

deep-learning-financial-news-stock-movement-prediction

Deep Time Series Code (Stock Sentiment Prediction)

deepstock

Technical experimentations to beat the stock market using deep learning :chart_with_upwards_trend:

ensemble-anfis

Machine Learning price prediction of Indexes. Comparing ANFIS vs ARIMA and Hybrid SOM/ANFIS vs Ensemble ANFIS.

gckrig

Spatial interpolation of geo-spatial count data (prediction) using Gaussian copula

house-price-prediction

Model Evaluation and Validation Project: Predicting Boston Housing Prices Install This project requires Python and the following Python libraries installed: NumPy Pandas matplotlib scikit-learn You will also need to have software installed to run and execute a Jupyter Notebook If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Code Template code is provided in the boston_housing.ipynb notebook file. You will also be required to use the included visuals.py Python file and the housing.csv dataset file to complete your work. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project. Note that the code included in visuals.py is meant to be used out-of-the-box and not intended for students to manipulate. If you are interested in how the visualizations are created in the notebook, please feel free to explore this Python file. Run In a terminal or command window, navigate to the top-level project directory boston_housing/ (that contains this README) and run one of the following commands: ipython notebook boston_housing.ipynb or jupyter notebook boston_housing.ipynb This will open the Jupyter Notebook software and project file in your browser. Data The modified Boston housing dataset consists of 489 data points, with each datapoint having 3 features. This dataset is a modified version of the Boston Housing dataset found on the UCI Machine Learning Repository. Features RM: average number of rooms per dwelling LSTAT: percentage of population considered lower status PTRATIO: pupil-teacher ratio by town Target Variable 4. MEDV: median value of owner-occupied homes

improve-zillow-s-prediction-residual

Create a model to improve the residual error of the Zillow's prediction model

kerasgan

A couple of simple GANs in Keras

machine-learning-octave

🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained

machinelearninginaction

marketgan

Implementing a Generative Adversarial Network on the Stock Market

new-york-stock-exchange-predictions-rnn-lstm

BEST SCORE ON KAGGLE SO FAR. Mean Square Error after repeated tuning 0.00032. Used stacked GRU + LSTM layers with optimized architecture, learning rate and batch size for best model performance. The graphs are self explanatory once you click and go inside !!!

nyc-taxi-trip-prediction

NYC taxi trip prediction using Advanced ensemble models. I used XGB , averaging and stacking ensemble models.

precipitation-prediction-convlstm-keras

Convolutional LSTM neural network to extrapolate radar images, and predict rainfall - CIKM 2017 contest

precipitationprediction

Existing precipitation prediction models have high error rates. The goal of this research is to reduce the error rates of the existing prediction models. An ensemble approach has been proposed to develop a New Aggregated Model to predict precipitation based on the dataset of some existing prediction models. This is a part of my master's thesis project.

predicting-stock-market-crashes

predictionerror_analysis

project3_kaggle_russian_housing

Placed Top 1% out of 2267 teams in Kaggle Sberbank housing price prediction challenge at the time of submission using feature engineering, advanced machine learning model ensembles, demonstrating low error rates in predictive analytics.

prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

prosit

Prosit offers high quality MS2 predicted spectra for any organism and protease as well as iRT prediction. When using Prosit is helpful for your research, please cite "Gessulat, Schmidt et al. 2019" DOI 10.1038/s41592-019-0426-7

randomforest-stockpredictor

Utilizing the ensemble method of random forests to predict stock prices.

sentiment-analysis-in-event-driven-stock-price-movement-prediction

Use NLP to predict stock price movement associated with news

stock-market-prediction-using-neural-networks-and-genetic-algorithm

Matlab Module for Stock Market Prediction using Simple NN

stock-market-price-prediction

Analysis of various deep learning based models for financial time series data using convolutions, recurrent neural networks (lstm), dilated convolutions and residual learning