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ppo

Proximal Policy Optimization implementation with TensorFlow

ppscan

ppSCAN: Parallelizing Pruning-based Graph Structural Clustering (ICPP'18) - by Yulin Che, Shixuan Sun and Prof. Qiong Luo

practical-machine-learning

practical-pytorch

PyTorch tutorials demonstrating modern techniques with readable code

practical6

Practical 6: LSTM language models

practical_python_and_opencv

project studying opencv library

practical_rl

A course in reinforcement learning in the wild

practicalmachinelearningproject

Coursera Practical Machine Learning Rep.

practice

practice_assignment

Practice assignment for the R programming class on Coursera

practicemerge

pram

Probabilistic Relational Agent-based Models

praw

PRAW, an acronym for "Python Reddit API Wrapper", is a python package that allows for simple access to reddit's API.

pre-processingapproachesimbalanceregression

Pre-processing Approaches for Imbalanced Distributions in Regression

preckacz

Implementation of the preconditioned Kaczmarz method https://arxiv.org/abs/1903.01806

predict-financial-recession

The major goal of this project is to predict financial re- cession given the frequencies of the top 500 word stems in the reports of financial companies. After applying various learning models, we can see that the prediction of financial recession by the bag of words has an accuracy of more than 90%. Hence, there is indeed a correlation between the two. Moreover, we have compared different learning models (ensemble methods with Decision Tree, SVM, and KNN) with various parameters to find the best model with a relatively high average accuracy and low variance of accuracy by cross-validation on the training data set. In addition, we have also tried several pre-processing methods (tf-idf, feature selection, and centroid-based clustering) to improve the accuracy of the learning models. In the end, the best model is Gradient Boosting with Decision Tree using the pre-processed tf-idf data set.

predicted_probabilities_bank_marketing

Tackling imbalanced data with predicted probabilities. Using the Portuguese bank marketing dataset as a case study, as published in Towards Data Science on Medium.com

predicting-email-grammatical-flow-

Trained and optimized a Classification Machine Learning model to predict the grammatical flow of email using state of the art techniques : 1. Word2Vec 2. tf-idf 3. bag-of-words. The models used include Logistic Regression and Support Vector Mechanics with 250-300 features.

predicting-employee-attrition-soton-ecs

A repo for Human Resources Analytics by using data mining

predicting-health-insurance-cost

Predicting health insurance cost from Morality data using Machine Learning techniques

predicting-inductive-biases

https://openreview.net/forum?id=mNtmhaDkAr

predicting-oil-price-with-time-series-

The goal of our analysis was to use different time series methods to predict the oil price for the last 6 months of the data, September 2017 through February 2018, and determine the best prediction model for this data.

predicting-wellbeing

predicting_stock_prices

This is the coding challenge for "Predicting Stock Prices" by @Sirajology on Youtube

predictionio

PredictionIO, a machine learning server for developers and ML engineers. Built on Apache Spark, HBase and Spray.

predictive-analytics-with-tensorflow

Predictive Analytics with TensorFlow, published by Packt

predictive-maintenance

The Python and R code involves 3-4 techniques for predicting the classification of failures and non failures in the data. The following steps have been performed on the dataset: Data cleaning and/or one hot encoding for factor variables. Partitioning data into training and validation. Performing a logistic regression and predicting using the validation dataset. Lift and decile wise charts are constructed for the results obtained from the logistic regression performed. A classification tree has been built on the training dataset, the tree is pruned using the minimum cp value. A confusion matric for the tree has also been provided with an accuracy of 98.2%. A neural network with 1 hidden layer has been fit on the data.

predictive-maintenance-1

Predictive Maintenance

predictive-maintenance-2

Our PS-1 Project

predictive-modeling