drroad Goto Github PK

-developed-models-of-predicting-claims-severity-for-car-insurance-company

Built a predictive model to predict the severity for the Allstate insurance claims using machine learning algorithms (Ridge/Lasso Regression, Random Forest, Gradient Boost, XGBoost, Neural Network)

-streamlit-multipag-app

10-steps-to-become-a-data-scientist

📢 Ready to learn! you will learn 10 skills as data scientist:📚 Machine Learning, Deep Learning, Data Cleaning, EDA, Learn Python, Learn python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.

2018-hindsight

A repository containing the source code for the paper "HINDSIGHT: An R-Based Framework Towards Long Short Term Memory (LSTM) Optimization"

2018-n5--pre-rfinance-meetup

In the spirit of the conference, this meetup will feature a series of short talks on practical applications with R!

2018aicity_teamuw

Source code from the winning team in Track 1 and Track 3 at the AI City Challenge Workshop in CVPR 2018.

2019-wuhan-coronavirus-data

2019 Wuhan Coronavirus data (2019-nCoV)

360viewer

3d-settlement-development

A comparison of Cesium and ArcGIS API for JavaScript

3d_street_view

The repo of Street View Image, Pose, and 3D Cities Dataset. Used in "Generic 3D Representation via Pose Estimation and Matching", ECCV16

3dcitybuilder

Generate 3D Models of Urban Areas.

3dcityreconstructionfromosm

Scalable and robust C++ application to generate a 3D model of the whole city starting from real data information provided by OpenStreetMap

3dfier

The open-source tool for creating 3D models

5minutefinance

Interactive Presentations for Financial Education using R/Shiny. See full list of presentations (with links) below.

7460project

Water Quality R Shiny App

a-data-mining-approach-on-the-analysis-of-road-safety-in-great-britain

RTA data collected are huge, multi-dimensional and heterogeneous. Moreover, the data may be incomplete and contain erroneous values, which makes the data analysis a daunting task. The target data for this study was collected by the Department for Transport, GB. Several data mining techniques such as handling an imbalanced dataset, factor reduction and prediction algorithms such as Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, Support Vector Machines (SVM) were carried out to perform an effective data analysis that could potentially support the transport department in devising better precautional measures to minimize the road accident occurrences in Great Britain. Moreover, the idea of chaining two different algorithms was attempted by identifying the significant attributes through Random Forest technique and feeding them as input to other ML algorithms. In addition, the key factors that influence these road collisions were identified and presented.

aaai2020-riskoracle

AAAIA2020-Accident Forecasting Experiments Setups

aashroy-turing_crypt

It's a web portal to track homeless people and identify slum areas through the use of Machine-learning and user inputs.

aat

Asynchronous, event-driven algorithmic trading in Python and C++

ab3dmot

Official Python Implementation for "A Baseline for 3D Multi-Object Tracking", In Submission

abnormalregiondetection

R package for the BARD method: Bayesian Abnormal Region Detection

abnormalreturns

This R package implements the event study methodology and abnormal returns in particular.

abrem

abremdebias

abrempivotals

abu

阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构

abu_ml

机器学习技术研究室——by阿布量化小组

accelerators

Data science and AI solution accelerator suite that provides templates for prototyping, reporting, and presenting data science analytics of specific domains

accessibility_toolbox

Accessibility Toolbox for R and ArcGIS

accident-severity-prediction

IBM Coursera Capstone Project - Predict Accident Severity Given Weather, Road and Lighting Conditions