acidcooles

acidcooles's Stars

• danielgross/teleprompter

  Language:Python32339

• Ryujinx/Ryujinx-Games-List

  List of games & demos tested on Ryujinx

  1k320

• osrf/cloudsim-portal

  Language:JavaScript22

• mvt-project/mvt

  MVT (Mobile Verification Toolkit) helps with conducting forensics of mobile devices in order to find signs of a potential compromise.

  Language:Python10.3k964

• unknown-marketwizards/tradingview-desktop

  Language:TypeScript24579

• InstaPy/InstaPy

  📷 Instagram Bot - Tool for automated Instagram interactions

  Language:Python16.7k3.8k

• alischah/InstaPy

  📷 Instagram Bot - Like/Comment/Follow Automation Script

  Language:Python1

• graphsense/graphsense-dashboard

  A web dashboard for interactive cryptocurrency analysis.

  Language:Elm10419

• alexandrebarachant/Grasp-and-lift-EEG-challenge

  Code and documentation for the winning sollution to the Grasp-and-Lift EEG Detection challenge

  Language:Python21789

• elena-roff/time-series-prophet

  Time Series Analysis & Forecasting of Rossmann Sales with Python. EDA, TSA and seasonal decomposition, Forecasting with Prophet and XGboost modeling for regression.

  Language:Jupyter Notebook18971

• DataDog/piecewise

  ## Auto-archived due to inactivity. ## Functions for piecewise regression on time series data

  Language:Python10435

• jasonha97/Final-Thesis-Repository

  This repository contains all Python scripts and Jupyter Notebooks that were used in the final stages of my undergraduate thesis. To summarise, the thesis involved the analysis of Stage N2 portions of 8 hour EEG recordings across 15 patients in order to extract 'sleep spindles'. The 'Quadratic Parameter Sinusoid' or QPS (Palliyali et. al. 2015) was used as a way to extract 6 quadratic polynomial coefficients that served as statistical descriptors of features of the extracted spindles (and non-spindles) such as their amplitudes, envelope symmetry, frequency, phase and more. The way this was achieved was using non-linear least squares (NLLS) via the Levenberg-Marquadt Algorithm (LM) as a way to perform a best fit of the model to the raw captured spindle. The main goal of the thesis was to use these 6 parameters as learning features in a simple feed-forward neural network in order to classify whether or not an acquired raw portion of an EEG signal is a spindle or not. The conclusion to the study showed that while the QPS model was a great way to reconstruct spindles and extract valuable coefficient data, there is no guarantee the non-linear regression will work since parameter initialisation is highly dependent on whether or not it is known (for certain) if the acquired raw section of the EEG is a spindle or not.

  Language:Jupyter Notebook2

• RayanAAY-ops/Regression-stacked-SVM_RandomForest

  This is my implementation of a stacked regressor using optimized SVM and random Forest using Optuna.The actual inputs of the combined regressor is a latent representation of 220 inputs compressed into 5 ,extracted using an auto-encoder implemented under Keras

  Language:Python81

• jradavenport/random-forest-timeseries

  Messing about with doing time series forecasting using Random Forest regression

  510

• xenakas/machine_learning

  machine learning in python mostly

  Language:Jupyter Notebook1

• hyperopt/hyperopt

  Distributed Asynchronous Hyperparameter Optimization in Python

  Language:Python7.2k1.1k

• hyperopt/hyperopt-sklearn

  Hyper-parameter optimization for sklearn

  Language:Python1.6k272

• carlleston/Sales_Analytics

  This project is a playground of time series and regression models, the data was got in a coursera competition in Kaggle,that provided with daily historical sales data. My goal for this project was creating a Machine Learning model to forescast the main invoice resource in Games Market in the Russia using economics exogenous variables as GDP, Unemployment Rate, Moex and the RUB value in Dollar.

  Language:Jupyter Notebook32

• carlleston/Visualizing_dtree_pipeline

  Language:Jupyter Notebook1

• 0zean/MARS-Time-Series

  Multivariate Adaptive Regression Splines for Time Series Prediction

  Language:Python187

• elixias/regression_pipeline_template

  A regression pipeline template with backward elimination

  Language:Python1

• openai/gpt-3

  GPT-3: Language Models are Few-Shot Learners

  15.7k2.3k

• optuna/optuna

  A hyperparameter optimization framework

  Language:Python10.6k1k

• pierpaolo28/Data-Visualization

  Collection of interactive Jupiter Notebook widgets and graphs.

  Language:Jupyter Notebook157112

• Naive-Bae/real_estate_regression

  A linear regression model and data-cleaning pipeline to predict CA housing prices.

  Language:Jupyter Notebook1

• bluerose98/house_prices

  In this housing price prediction challenge, I practiced writing efficient code by building pipelines for preprocessing features (scaling numeric and encoding categorical) and modeling (linear and random forest regression).

  Language:Jupyter Notebook1

• SwoopGT/Regression-Analysis-of-Oceanographic-Data-using-Pipelines-and-Hyperparameter-Tuning-

  A comparison of metrics for Linear Regression and Multiple Linear Regression models developed on Oceanographic data is made. Also involves creation of polynomial features and streamlining the process using Pipelines. The models developed are subjected to hyperparameter tuning using GridSearch..

  Language:Jupyter Notebook2

• DennCardoso/yara_iris_model

  Development of Pipeline Model for a Simple Logistic Regression

  Language:Jupyter Notebook1

• IbrahimSahibzada/Yelp-Yelp

  Classifying Yelp reviews using NLTK, Pipelines, Logistic Regression.

  Language:Jupyter Notebook1

• kinir/catboost-with-pipelines

  Example of using catboost regressor with sklearn pipelines.

  Language:Jupyter Notebook144