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3rd_place_solution_trailblazers-nigerian-qualification-challenge

9th-place-solution_max-indabax-nigeria-hackathon-

Predict the likelihood of a lead reaching the last stage (onboarding) quicker than others. This will enable the team to channel resources and efforts toward converting such leads. The model that says the type of leads to after, so we don’t waste resources. (In terms of age, geography, marital status, and other lead qualities) Your task is to build machine learning to help the business in saving costs. Note: Maximum Submission per day Predict the likelihood of a lead reaching the last stage (onboarding) quicker than others. This will enable the team to channel resources and efforts toward converting such leads. The model that says the type of leads to after, so we don’t waste resources. (In terms of age, geography, marital status, and other lead qualities) Your task is to build machine learning to help the business in saving costs. Note: Maximum Submission per day is 20. Kindly have good cross validation to optimise your solution and submission.

ai-squad-2nd-place-winning-solution-ai4d-africa-s-anglophone-research-lab-tanzania-tourism-classific

Can you use tourism survey data and ML to classify the range of expenditures a tourist spends in Tanzania?

ai4d-africa-s-anglophone-research-lab-tanzania-tourism-classification-challenge

Can you use tourism survey data and ML to classify the range of expenditures a tourist spends in Tanzania?

ansem_chaieb_womxn-in-big-data-south-africa-female-headed-households-in-south-africa-challenge

First place solution form Ansem Chaieb.

dataset

dsn-2023-bootcamp

emmanuel-ebiendele

emmanuel_9th_place_solution_max-indabax-nigeria-hackathon-

Predict the likelihood of a lead reaching the last stage (onboarding) quicker than others. This will enable the team to channel resources and efforts toward converting such leads. The model that says the type of leads to after, so we don’t waste resources. (In terms of age, geography, marital status, and other lead qualities) Your task is to build machine learning to help the business in saving costs. Note: Maximum Submission per day Predict the likelihood of a lead reaching the last stage (onboarding) quicker than others. This will enable the team to channel resources and efforts toward converting such leads. The model that says the type of leads to after, so we don’t waste resources. (In terms of age, geography, marital status, and other lead qualities) Your task is to build machine learning to help the business in saving costs. Note: Maximum Submission per day is 20. Kindly have good cross validation to optimise your solution and submission.

heart-disease-classification-using-machine-learning

indabax-nigeria2023

An application of a classification problem - Fraud Detection Electricity and Gas Consumption Challenge

indavaxnigeria2024

Exchange Rate Forecasting Challenge Join the Exchange Rate Forecasting Challenge

nigeria-covid-19

predicting-the-sale-price-of-bulldozers-using-machine-learning

teamxdatathon23

Fraud Detection for online payment

xente-fraud-challenge-detection

Classifying whether a transaction was Fraud