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pjcurvature

Calculate the curvature of discrete points

pke

Python Keyphrase Extraction module

plcmf

Source Code for Pseudo-Label Guided Collective Matrix Factorization. Reference: Di Wang, Songwei Han, Quan Wang, Lihuo He, Yumin Tian and Xinbo Gao. Pseudo-Label Guided Collective Matrix Factorization for Multi-View Clustering. IEEE Transactions on Cybernetics, 2021.

pmdarima

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

pnt-ddf

pothole-detector

The service in the android app detects the potholes using accelerometer and sends it to the server and is fetched back and shown to all users on a map view

pothole-project

Detect Potholes from gyroscope and accelerometer data using mobile phone sensors

pothole_matlab

Pothole classification with accelerometer data.

potholes

Detection of Potholes with acceleration sensor data and machine learning algorithm

ppd534

USC PPD534: Data, Evidence, and Communication for the Public Good

ppp-bayestree

Code release for "Evaluation of Precise Point Positioning Convergence with an Incremental Graph Optimizer".

practical-time-series-analysis

Practical Time-Series Analysis, published by Packt

prase-python

The code of paper "Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding"

prasemap

A Probability Reasoning and Semantic Embedding-based Knowledge Graph Alignment System

predictingtaxidemandairports

Predicting Taxi Demand at Airports in NYC

prediction-using-bayesian-neural-network

Prediction of continuous signals data and Web tracking data using dynamic Bayesian neural network. Compared with other network architectures aswell.

probability

Probabilistic reasoning and statistical analysis in TensorFlow

problogprecompilation

profitmaximize

NYC Taxi Trip Profit Maximization

project_lakshadweep_itinerary

Itinerary planning with graph models on real data, including some cool stuff like full fledge scraping, captch parsing and itinerary planning...

prototree

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

pseudo_lidar_v2

(ICLR) Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving

pseudoraytracingosm

Conv nets applied to OSM maps for path loss prediction

pso

An Ontological Framework for Data-Driven Physical Security and Insider Threat Detection

pubg-dataexploration

In a PUBG game, up to 100 players start in each match (matchId). Players can be on teams (groupId) which get ranked at the end of the game (winPlacePerc) based on how many other teams are still alive when they are eliminated. In game, players can pick up different munitions, revive downed-but-not-out (knocked) teammates, drive vehicles, swim, run, shoot, and experience all of the consequences -- such as falling too far or running themselves over and eliminating themselves. We are provided with a large number of anonymized PUBG game stats, formatted so that each row contains one player's post-game stats. The data comes from matches of all types: solos, duos, squads, and custom; there is no guarantee of there being 100 players per match, nor at most 4 player per group. After performing Data Cleaning, Data Wrangling, Data Visualization, I have prepared the data set to build a model in order to predict the players' finishing placement based on their final stats, on a scale from 1 (first place) to 0 (last place).

pubg_placement_prediction

In a PUBG game, up to 100 players start in each match (matchId). Players can be on teams (groupId) which get ranked at the end of the game (winPlacePerc) based on how many other teams are still alive when they are eliminated. In game, players can pick up different munitions, revive downed-but-not-out (knocked) teammates, drive vehicles, swim, run, shoot, and experience all of the consequences -- such as falling too far or running themselves over and eliminating themselves. You are provided with a large number of anonymized PUBG game stats, formatted so that each row contains one player's post-game stats. The data comes from matches of all types: solos, duos, squads, and custom; there is no guarantee of there being 100 players per match, nor at most 4 player per group. You must create a model which predicts players' finishing placement based on their final stats, on a scale from 1 (first place) to 0 (last place).

pygcn

Graph Convolutional Networks in PyTorch

pygeotile

Python package to handle tiles and points of different projections, in particular WGS 84 (Latitude, Longitude), Spherical Mercator (Meters), Pixel Pyramid and Tiles (TMS, Google, QuadTree)

pykale

Knowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem

pykg2vec

Python library for knowledge graph embedding and representation learning.