Kolesh jr (@koleshjr)
github

koleshjr

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  • Stars
    star
    33
  • Global Rank 458,219 (Top 16 %)
  • Followers 78
  • Following 123
  • Registered over 5 years ago
  • Most used languages
    Jupyter Notebook
         91.3 %
    Python
         8.7 %
  • Location πŸ‡°πŸ‡ͺ Kenya
  • Country Total Rank 719
  • Country Ranking
    Jupyter Notebook
         13
    Python
         888

Top 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

FAULT-IMPACT-ANALYSIS--HUAWEI-PROJECT

Can you predict an NE’s average data rate change when a fault occurs?
Jupyter Notebook
2
star
5

3monthsofpythoncoding

## Preparing for my first python interview
Jupyter Notebook
2
star
6

PREDICTING-AIR-QUALITY

## 1st placed solution improved
Jupyter Notebook
2
star
7

NOISE-DATA-CLASSIFICATION

Zindi competition
Jupyter Notebook
2
star
8

Digital-Green-Crop-Yield-Estimate-Challenge

Can you determine the crop yield for farms in India?
Jupyter Notebook
1
star
9

Financial-Inclusion

A Learning Notebook
Jupyter Notebook
1
star
10

Makerere-Passion-Fruit-Disease-Detection-Challenge

17th placed solution
Jupyter Notebook
1
star
11

UMOJAHACK22

INSURANCE CLAIM INTERMEDIATE COMPETITION
Jupyter Notebook
1
star
12

SWAHILI-WORDS-VOICE-RECOGNITION

Swahili Audio Classification Can you classify Swahili audio into words?
Jupyter Notebook
1
star
13

Makerere-Fall-Army-worm-disease-detection

6th placed solution
Jupyter Notebook
1
star
14

ADBOT

Can you predict the future success of a digital advert?
Jupyter Notebook
1
star
15

NIGERIA-INSURANCE

Jupyter Notebook
1
star
16

AI4D-LAB-HACKATHON-CHALLENGE

Official 3rd place solution
Jupyter Notebook
1
star
17

Swahili_News_Nlp

###
Jupyter Notebook
1
star
18

KENYAN-SIGN-LANGUAGE-CLASSIFICATION

Classifying various Kenyan Sign languages into various categories
Jupyter Notebook
1
star
19

CLEAN_METADATA_EXTRACTION

Gazzette Parsing
Jupyter Notebook
1
star
20

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
21

Predicting-Air-Quality-in-Uganda

## Unofficial seventh placed solution
Jupyter Notebook
1
star
22

Koding_With_Kolesh

Helpful Machine Learning resources for the Zindi community
Python
1
star
23

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