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UmojaHack-Africa-2023-Carbon-Dioxide-Prediction-Challenge
gsdglangchain_dj_gpt_-TOOLS-
Created a langchain dj gpt agent thanks to lablabai workshopsGetting-deep-into-Pytorch
My Overal Journey to MetaFAULT-IMPACT-ANALYSIS--HUAWEI-PROJECT
Can you predict an NE’s average data rate change when a fault occurs?3monthsofpythoncoding
## Preparing for my first python interviewPREDICTING-AIR-QUALITY
## 1st placed solution improvedNOISE-DATA-CLASSIFICATION
Zindi competitionDigital-Green-Crop-Yield-Estimate-Challenge
Can you determine the crop yield for farms in India?Financial-Inclusion
A Learning NotebookMakerere-Passion-Fruit-Disease-Detection-Challenge
17th placed solutionUMOJAHACK22
INSURANCE CLAIM INTERMEDIATE COMPETITIONSWAHILI-WORDS-VOICE-RECOGNITION
Swahili Audio Classification Can you classify Swahili audio into words?ADBOT
Can you predict the future success of a digital advert?NIGERIA-INSURANCE
AI4D-LAB-HACKATHON-CHALLENGE
Official 3rd place solutionSwahili_News_Nlp
###KENYAN-SIGN-LANGUAGE-CLASSIFICATION
Classifying various Kenyan Sign languages into various categoriesCLEAN_METADATA_EXTRACTION
Gazzette ParsingWadhwani-AI-Bollworm-Classification-Challenge
Can you improve a pest control app by classifying if an image contains a bollworm moth or not?Predicting-Air-Quality-in-Uganda
## Unofficial seventh placed solutionKoding_With_Kolesh
Helpful Machine Learning resources for the Zindi communityLoan-Default-Prediction-Challenge
SuperLender is a local digital lending company, which prides itself in its effective use of credit risk models to deliver profitable and high-impact loan alternative. Its assessment approach is based on two main risk drivers of loan default prediction:. 1) willingness to pay and 2) ability to pay. Since not all customers pay back, the company invests in experienced data scientist to build robust models to effectively predict the odds of repayment. These two fundamental drivers need to be determined at the point of each application to allow the credit grantor to make a calculated decision based on repayment odds, which in turn determines if an applicant should get a loan, and if so - what the size, price and tenure of the offer will be. There are two types of risk models in general: New business risk, which would be used to assess the risk of application(s) associated with the first loan that he/she applies. The second is a repeat or behaviour risk model, in which case the customer has been a client and applies for a repeat loan. In the latter case - we will have additional performance on how he/she repaid their prior loans, which we can incorporate into our risk model. It is your job to predict if a loan was good or bad, i.e. accurately predict binary outcome variable, where Good is 1 and Bad is 0.Love Open Source and this site? Check out how you can help us