• Stars
    star
    2
  • Language
    Jupyter Notebook
  • License
    Apache License 2.0
  • Created over 1 year ago
  • Updated over 1 year ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

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

More Repositories

1

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

gsdg
Jupyter Notebook
11
star
2

langchain_dj_gpt_-TOOLS-

Created a langchain dj gpt agent thanks to lablabai workshops
Python
2
star
3

Getting-deep-into-Pytorch

My Overal Journey to Meta
Jupyter Notebook
2
star
4

3monthsofpythoncoding

## Preparing for my first python interview
Jupyter Notebook
2
star
5

PREDICTING-AIR-QUALITY

## 1st placed solution improved
Jupyter Notebook
2
star
6

NOISE-DATA-CLASSIFICATION

Zindi competition
Jupyter Notebook
2
star
7

Digital-Green-Crop-Yield-Estimate-Challenge

Can you determine the crop yield for farms in India?
Jupyter Notebook
1
star
8

Financial-Inclusion

A Learning Notebook
Jupyter Notebook
1
star
9

Makerere-Passion-Fruit-Disease-Detection-Challenge

17th placed solution
Jupyter Notebook
1
star
10

UMOJAHACK22

INSURANCE CLAIM INTERMEDIATE COMPETITION
Jupyter Notebook
1
star
11

SWAHILI-WORDS-VOICE-RECOGNITION

Swahili Audio Classification Can you classify Swahili audio into words?
Jupyter Notebook
1
star
12

Makerere-Fall-Army-worm-disease-detection

6th placed solution
Jupyter Notebook
1
star
13

ADBOT

Can you predict the future success of a digital advert?
Jupyter Notebook
1
star
14

NIGERIA-INSURANCE

Jupyter Notebook
1
star
15

AI4D-LAB-HACKATHON-CHALLENGE

Official 3rd place solution
Jupyter Notebook
1
star
16

Swahili_News_Nlp

###
Jupyter Notebook
1
star
17

KENYAN-SIGN-LANGUAGE-CLASSIFICATION

Classifying various Kenyan Sign languages into various categories
Jupyter Notebook
1
star
18

CLEAN_METADATA_EXTRACTION

Gazzette Parsing
Jupyter Notebook
1
star
19

Wadhwani-AI-Bollworm-Classification-Challenge

Can you improve a pest control app by classifying if an image contains a bollworm moth or not?
Jupyter Notebook
1
star
20

Predicting-Air-Quality-in-Uganda

## Unofficial seventh placed solution
Jupyter Notebook
1
star
21

Koding_With_Kolesh

Helpful Machine Learning resources for the Zindi community
Python
1
star
22

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.
Jupyter Notebook
1
star