koleshjr/NIGERIA-INSURANCE

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koleshjr/NIGERIA-INSURANCE

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UmojaHack-Africa-2023-Carbon-Dioxide-Prediction-Challenge

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langchain_dj_gpt_-TOOLS-

Created a langchain dj gpt agent thanks to lablabai workshops

Getting-deep-into-Pytorch

My Overal Journey to Meta

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FAULT-IMPACT-ANALYSIS--HUAWEI-PROJECT

Can you predict an NE’s average data rate change when a fault occurs?

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3monthsofpythoncoding

## Preparing for my first python interview

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PREDICTING-AIR-QUALITY

## 1st placed solution improved

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NOISE-DATA-CLASSIFICATION

Zindi competition

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Digital-Green-Crop-Yield-Estimate-Challenge

Can you determine the crop yield for farms in India?

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Financial-Inclusion

A Learning Notebook

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Makerere-Passion-Fruit-Disease-Detection-Challenge

17th placed solution

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UMOJAHACK22

INSURANCE CLAIM INTERMEDIATE COMPETITION

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SWAHILI-WORDS-VOICE-RECOGNITION

Swahili Audio Classification Can you classify Swahili audio into words?

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Makerere-Fall-Army-worm-disease-detection

6th placed solution

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ADBOT

Can you predict the future success of a digital advert?

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AI4D-LAB-HACKATHON-CHALLENGE

Official 3rd place solution

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Swahili_News_Nlp

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KENYAN-SIGN-LANGUAGE-CLASSIFICATION

Classifying various Kenyan Sign languages into various categories

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CLEAN_METADATA_EXTRACTION

Gazzette Parsing

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Wadhwani-AI-Bollworm-Classification-Challenge

Can you improve a pest control app by classifying if an image contains a bollworm moth or not?

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Predicting-Air-Quality-in-Uganda

## Unofficial seventh placed solution

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Koding_With_Kolesh

Helpful Machine Learning resources for the Zindi community

Loan-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.

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