There are no reviews yet. Be the first to send feedback to the community and the maintainers!
KOWOPE
3rd Place. Kowope Mart is a Nigerian-based retail company with a vision to provide quality goods, education and automobile services to its customers at affordable price and reduce if not eradicate charges on card payments and increase customer satisfaction with credit rewards that can be used within the Mall. To achieve this, the company has partnered with DSBank on co-branded credit card with additional functionality such that customers can request for loan, pay for goods even with zero-balance and then pay back within an agreed period of time. This innovative strategy has increased sales for the company. However, there has been recent cases of credit defaults and Kowope Mart will like to have a system that profiles customers who are worthy of the card with minimum if not zero risk of defaulting. You have been employed as a Data Scientist to leverage Machine learning to predict customers who are likely to default or not. This is Qualification Competition for the Data Science Nigeria AI Bootcamp.Product-Recommendation--ALS--FlaskApp
Machine Learning Product recommendation as an API & as a WEB appZimnat-Insurance-Recommendation-Challenge
Sample data preparation on insurance recommendation challengeStandard-Bank-Tech-Impact-Challenge
3rd Place, Predict the likelihood of credit default of ecommerce clientsAkeed-Restaurant-Recommendation-Challenge_getting_train-test
A quick notebook to get the train and test set.Lerato
A Retrieval based bot that responds to basic question about Zindi Africa- using cosine similarity between words entered by the user and the words in the corpus. We 'll define a function response which searches the user’s utterance for one or more known keywords and returns one of several possible responses. If it doesn’t find the input matching any of the keywords, it returns a response:” I am so sorry! I dont understand your words"Urban-Air-Pollution-Challenge-by-ZindiWeekendz
You may have seen recent news articles stating that air quality has improved due to COVID-19. This is true for some locations, but as always the truth is a little more complicated. In parts of many African cities, air quality seems to be getting worse as more people stay at home. For this challenge we’ll be digging deeper into the data, finding ways to track air quality and how it is changing, even in places without ground-based sensors. This information will be especially useful in the face of the current crisis, since poor air quality makes a respiratory disease like COVID-19 more dangerous. We’ve collected weather data and daily observations from the Sentinel 5P satellite tracking various pollutants in the atmosphere. Your goal is to use this information to predict PM2.5 particulate matter concentration (a common measure of air quality that normally requires ground-based sensors to measure) every day for each city. The data covers the last three months, spanning hundreds of cities across the globe.PRJ1-Flower_Breed-Analytics
To build a model that will help predict the flowers breed correctly for production purposes. this solution will help ABC to reduce cost, time and resources.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):AI16TECH
Food-Assessment-Quality
Problem Description The food inspection department conducts regular inspection on food quality for various restaurants in the city. It’s a very well documented procedure and over time some good amount of data has been generated out of these inspections. The inspection department would like to predict where they should focus most in terms of their next inspection schedule, so that they can most optimize their time at hand to catch the worst offenders. Can the past inspection or any data that they have collected predict which facility will pass or fail. In this hackathon, MachineHack provides you with a subset of this dataset with information on food quality checks conducted on thousands of facilities that serve food across multiple cities. Your objective as a Data Scientist is to predict whether a facility will pass or fail the inspection based on a number of factors.Fraud-Detection-Model
Day 1 to 5 notebook of building a simple fraud detection modelUber-Movement-SANRAL-Cape-Town-Challenge
Unofficial 6th place solution; The aim of this challenge is to forecast if an incident will occur for each hour of each day per 500m road segment along the major roadways in Cape Town for 1 January 2019 to 31 March 2019Recommedation-Engine
Content based - using cosine similarities2019-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.ProHack
Aliens problem solved using Machine Learning and Operation Research challengeData-science-NG-
simple machine learning taskmortgage-loan-approval
predicting loan approval from government data hosted by microsoftFinancial-Inclusion-in-Tunisia-AI-TUNISIA-HACK-2019-
prequalification to biggest AI hackathon challenge in tunis 2019codelagoslogin
Allows username and password easy login access and denial of incorrect entryjava_developers
Series of core java programming projectMachine-Learning-models
AI trained modelMeet-Up-Documentations
USAID-s-Intelligent-Forecasting-Challenge-Model-Future-Contraceptive-Use
2nd/120th... Greater access to contraceptives enables couples and individuals to determine whether, when, and how often to have children. Contraceptive access is vital to safe motherhood, healthy families, and prosperous communities. In low- and middle-income countries (LMIC) around the world, health systems are often unable to accurately predict the quantity of contraceptives necessary for each health service delivery site, in part due to insufficient data, limited staff capacity, and inadequate systems. When too few supplies are ordered, service delivery sites may run out, limiting access to contraceptives and family planning. When too much product is ordered, it leads to unused contraceptives that are wasted if they are left to expire. Accurate forecasting of contraceptive consumption can save lives, money, and time by ensuring health service delivery sites have what they need when they need it and by reducing waste in the supply chain. USAID works with local health care authorities and partners to support voluntary family planning and reproductive health programs in nearly 40 countries across the globe, which includes ensuring that contraceptives are available and accessible to people who need them. With this competition, USAID seeks to identify and test more accurate methods of predicting future contraceptive use at health service delivery sites.Data-Science-2019-AXA-MANSARD-INSURANCE
A Supervised learning problem to predict the probability of a insuring a buildingFood-Delivery-Time
A Regression problem to predict food delivery time from a location to another in india, [Hosted on MachineHack platform]Love Open Source and this site? Check out how you can help us