abofficial444 Goto Github PK

2019-data-science-bowl

484th/3497th Top 14%; Competitors are challenged to predict scores on in-game assessments and create an algorithm that will lead to better-designed games and improved learning outcomes.

3rd-place-solution-busara-challenge

abofficial444

ai16tech

ai4d-predict-the-global-spread-of-covid-19

8th place... The objective of this challenge is to build an epidemiological model that predicts the spread of COVID-19 throughout the world. The target variable is the cumulative number of deaths caused by COVID-19 in each country by each date. We have selected the cumulative number of fatalities rather than the number of reported infections as the target variable because the real number of infections is unknown and will perhaps never be known. The number of reported cases is understood to be underestimated and largely biased by the availability of tests, which varies from location to location and country to country. We encourage participants to engage with the literature available on approaches and considerations when modelling the spread of diseases. For this competition, we have used the publicly-available data from the Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), which is updated on a daily-basis.

akeed-restaurant-recommendation-challenge

7th Place, The objective of this competition is to build a recommendation engine to predict what restaurants customers are most likely to order from given the customer location, restaurant information, and the customer order history. This solution will allow Akeed, an app-based food delivery service in Oman, to customise restaurant recommendations for each of their customers and ensure a more positive overall user experience. About Akeed (akeedapp.com):

akeed-restaurant-recommendation-challenge_getting_train-test

A quick notebook to get the train and test set.

ar-cutpaste

Cut and paste your surroundings using AR

basic-needs-basic-rights-kenya-tech4mental-health

Around 1 in 4 people will experience a mental health problem this year. Low-income countries have an estimated treatment gap of 85% (as compared with high-income countries with a gap of 35% to 50%). While Kenya has a mental illness prevalence rate that is comparable to that of high-income countries, there are still less than 500 healthcare professionals serving the country. In Kenya, there are growing concerns about mental health among young people, particularly university students that face a challenging and unique conflation of stressors that put them at risk of challenges like depression and substance abuse. From the use of app-based solutions for screening to electronically delivered therapies, the use of technologies including machine learning and AI will potentially transform the delivery of mental health services in the coming years. The objective of this challenge is to develop a machine learning model that classifies statements and questions expressed by university students in Kenya when speaking about the mental health challenges they struggle with. The four categories are depression, suicide, alchoholism, and drug abuse. This solution will be used for a prototype of a mental health chatbot designed specifically for university students. This initiative is a first step in leveraging technology to make mental health services more accessible and more user-friendly for young people in Kenya and around the world

calcslopedegrees

A Python script to calculate slope from a DEM.

car-price-predictor

cataman

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

causal_inference_python_code

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

character-control-using-body-posture

codelagoslogin

Allows username and password easy login access and denial of incorrect entry

competitive-data-science

Materials for "How to Win a Data Science Competition: Learn from Top Kagglers" course

dashboard---xente-fraud

A complete projects that points to the descriptive, diagnostic, prescriptive and predictive analysis; with reference to intelligence dashboards using Microsoft powerBI

data-analysis

Data Science Using Python

data-engineer-roadmap

Learning from multiple companies in Silicon Valley. Netflix, Facebook, Google, Startups

data-science-2019-axa-mansard-insurance

A Supervised learning problem to predict the probability of a insuring a building

data-science-ng-

simple machine learning task

data-science-staff-promotion-kaggle

120th/530th; To predict who is likely to be promoted

datasist

Datasist abstract numerous techniques and functions used repeatedly in Data Science and Machine Learning.

dbt-project-path

deepchecks

Test Suites for Validating ML Models & Data. Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort.

empirical-study-of-ensembles-research

A Research work on Ensemble techniques

ensemble-methods-notebooks

A collection of companion Jupyter notebooks for Ensemble Methods for Machine Learning (Manning, 2020)

eplpredict

excellent-store

“The Excellent Store”, is a leading indigenous chain of stores with headquarters in Gbagi, Oyo, Nigeria. At the core of their business is a strong sense of excellence and entrepreneurial value. And this is evident in all their 1,500 products, available to all segments of the population at customer-friendly prices, across 10 stores in different cities of Nigeria. The CEO of the company, Chief A. A. Babatunji, plans to expand the chain of stores to more Nigerian cities in 2021. However, as the COVID19 restrictions have affected the retail business, he sees the need to better understand which products return higher profits at specific stores so as to inform the expansion plan. You have been engaged as the new Retail Data Analyst to build a predictive model and find out the profit returns on each product at a particular store. The scenario he sees is where a brand of juice sold for N250 in one of his store branches may also be sold at N230 at another store within Chief Babatunji's chain of stores. He needs to therefore understand what type of product, market clusters and store type (location, age, size) will give more profit returns as he plans to expand to more cities in the country.