mosh094 Goto Github PK

-dsn-pre-bootcamp-hackathon-the-excellent-store-challenge

The project is aimed at predicting and find out the profit returns on each product at a particular store or branch of “The Excellent Store”

-zimnat-insurance-recommendation-challenge

The project challenge: for around 10,000 customers in the test set, you are given all but one of the products they own, and are asked to make predictions around which products are most likely to be the missing product. This same model can then be applied to any customer to identify insurance products that might be useful to them given their current profile.

70_days_of_data_science

This repository presents codes from my 70 days of data science challenge with Data Science Nigeria

70_days_of_datascience

This repository presents codes from my 70 days of data science challenge with Data Science Nigeria

amazon-personalize-samples

Notebooks and examples on how to onboard and use various features of Amazon Personalize

andrew-ng-machine-learning-programming-solutions-

open course on coursera.org Stanford University

app4

data-engineering-zoomcamp

Free Data Engineering course!

data-science-projects

A Collection of Data Science/ML Projects

draft-company-cron

dsn-ai-bootcamp-qualification-hackatho-kowope-mart

The project is aimed at building a Machine learning to predict customers who are likely to default or not

educka

edureka-project

Project for the completion of the Edureka/Purdue University's Devops Post Graduate Program

expresso-churn-prediction

Expresso is an African telecommunications company that provides customers with airtime and mobile data bundles. The objective of this challenge is to develop a machine learning model to predict the likelihood of each Expresso customer “churning,” i.e. becoming inactive and not making any transactions for 90 days. This solution will help Expresso to better serve their customers by understanding which customers are at risk of leaving.

expresso-churn-prediction_app

Expresso is an African telecommunications company that provides customers with airtime and mobile data bundles. The objective of this challenge is to develop a machine learning model to predict the likelihood of each Expresso customer “churning,” i.e. becoming inactive and not making any transactions for 90 days. This solution will help Expresso to better serve their customers by understanding which customers are at risk of leaving.

gads2020-roadmap

A roadmap for learners in the Google Africa Developer Scholarship on Pluralsight

hamoye_intenship

This repository contains details of my activities during the Hamoye intenship program

homoye_2021

Stage 2

labs

logistc_regression_for_the_detection_of_malicious_urls

Logistic Regression Model for the Detection of Malicious URLs

malicious_url_prediction_app

A Streamlit app deployed on Heroku

mlbookcamp-code

The code from the Machine Learning Bookcamp book and a free course based on the book

mlops-zoomcamp

Free MLOps course from DataTalks.Club

multilabel_classification_project

purdue_edureka

salary-prediction

Linear Model to predict salary based on experience

samplejavaapp

terraform-crash-course

version-ctrl-with-git

yomi