Discover @ngirimana Open Source projects

NGIRIMANA Schadrack (@ngirimana)

ngirimana

Stars
124
Global Rank 171,794 (Top 7 %)
Followers 31
Following 48
Registered about 6 years ago
Most used languages

JavaScript
50.6 %

TypeScript
8.0 %

Jupyter Notebook
6.9 %

Python
5.7 %

Java
5.7 %

CSS
4.6 %

HTML
3.4 %

Dart
3.4 %

SWIG 2.3 %

C++
2.3 %

C
2.3 %

Kotlin
1.1 %

Visual Basic .NET 1.1 %

PHP
1.1 %

Makefile 1.1 %
Location 🇷🇼 Rwanda
Country Total Rank 30
Country Ranking

SWIG 1

Visual Basic .NET 2

Jupyter Notebook
5

Makefile 5

Dart
11

Kotlin
11

JavaScript
12

C
19

Java
19

C++
24

TypeScript
27

Python
28

HTML
43

CSS
52

PHP
76

Free-Mentors

Free Mentors is a social initiative where accomplished professionals become role models to young people to provide free mentorship sessions.

socketio

socket io in nodejs

socketio-chat_app_flutter-

chatting app in flutter

SEVEN-SEGMENT.X

Blockchain-react-native

NFT market place

flutter_webrtc

webtrc in flutter

UIforRwandaAdministrationLevels

Dispaly Provinces,Districts,Sectors,ells and Villages

ngirimana

blockchain

Block chain App in python

meal-react-native

murikahomeltd

Murika-frontend

traffic-light-using-assembly

parcel-tracking-system

RSA

RSA algorithm is asymmetric cryptography algorithm. Asymmetric actually means that it works on two different keys i.e. Public Key and Private Key. As the name describes that the Public Key is given to everyone and Private key is kept private.

Mobile-application-development

notaria

challenge

Display users with their corresponding post

todo

socket-test-in-java

diffie-helman

Diffie-Hellman is a way of generating a shared secret between two people in such a way that the secret can't be seen by observing the communication. That's an important distinction: You're not sharing information during the key exchange, you're creating a key together.

MyDiary-Adc

MyDiary is an online journal where users can pen down their thoughts and feelings.

recipe-app-api

react-native-authentication

React native authentication

Nexter

slack-clone

crypto-coine-real-time-data

blog

bidirectional-visitorCounter

expense-tracker-in-reactjs

tictac-in-python

portfolio

This is my portfolio web to showcase my skills and experience

Blog_NextJS

expense-tracker-react-native

shopNow

forum

hotelUI

html,css temlate

murika

from all to all

shop-app

SearchIT

Instructions for submission Create an account on [Codepen.io](https://codepen.io/) and attempt the following question. You are required to make use of only HTML, CSS and JavaScript, and NO FRAMEWORKS. Please submit via this form before 4 pm on Sunday, August 18th, 2019.

mydiarydb

Location-and-messaging

quiz

quiz app in flutter

burger-app

Promptopia

car-showcase

summarizer

ISSE_12_23

wingi-challenge

counter-and-notification

nestjs-task-management

traffic-light

diffie-helman-algorithm-

Diffie–Hellman key exchange establishes a shared secret between two parties that can be used for secret communication for exchanging data over a public network. The conceptual diagram at the top of the page illustrates the general idea of the key exchange by using colors instead of very large numbers.

airbnb-clone

expenseTracker

expense tracker

project-management-graphql

The project mana gement made with react , node.js and graphql

react_album-challenge

zoom-clone

chartCord

Real time chart using socket

NextJs-Landing-Page

qr-code-scanner

ML_KNN

Welcome to the KNN Project! This will be a simple project very similar to the lecture, except you'll be given another data set. Go ahead and just follow the directions below.

Jupyter Notebook

vbCrud

Crud operations in VB

Visual Basic .NET

Pandas_exercise

Pandas exericise

Jupyter Notebook

automatic-door-opening-simulator

data-analyis-project

Jupyter Notebook

entrance-control

Student-computer-record

fikia

Fikia team project

uber-eat-react--native

travel-advisor

job-post-blog

natours

the nature adventures

Food-delivery-react-native

product_crud_operation

class assignment API to be fetched in Android Applicaation

alan-AI-new-app

products-crude-with-spash-screen

myapp

first react native app

ig-clone-React-native

This is the instagram apllication clone developed using React native ,formik,yup and firebase

task-manager-in-nestjs

drag-and-drop

Drag and drop project management typescript

trillo

html template for reservations for car rental flight,hotel and tours

mydiary-api

card-game

mern_chat_socket_io

Dockerizing-Django-with-Postgres-Redis-and-Celery

Web-3

Krypt - Web 3.0 Blockchain Application

ML_LinearRegression

You just got some contract work with an Ecommerce company based in New York City that sells clothing online but they also have in-store style and clothing advice sessions. Customers come in to the store, have sessions/meetings with a personal stylist, then they can go home and order either on a mobile app or website for the clothes they want. The company is trying to decide whether to focus their efforts on their mobile app experience or their website. They've hired you on contract to help them figure it out! Let's get started! Just follow the steps below to analyze the customer data (it's fake, don't worry I didn't give you real credit card numbers or emails).

Jupyter Notebook

ticketing-microservice

blog_api_springboot

gymn

Utilizing two unique APIs, design the best and most cutting-edge fitness exercises app!.Golds Gym is the best React Fitness App you can currently find on YouTube and throughout the entire internet. It has the functionality to select exercise categories and specific muscle groups, browse more than a thousand exercises with useful examples, pagination, exercise details, pull related videos from YouTube, display similar exercises, and much more.

Guess-Number

An integer between 0 and 100 is chosen in this straightforward game. You enter your predictions, and the system notifies you of your success or failure and whether you should try a higher or lower number.

ledger

ML_logistcs-regression

In this project we will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user. This data set contains the following features: * 'Daily Time Spent on Site': consumer time on site in minutes * 'Age': cutomer age in years * 'Area Income': Avg. Income of geographical area of consumer * 'Daily Internet Usage': Avg. minutes a day consumer is on the internet * 'Ad Topic Line': Headline of the advertisement * 'City': City of consumer * 'Male': Whether or not consumer was male * 'Country': Country of consumer * 'Timestamp': Time at which consumer clicked on Ad or closed window * 'Clicked on Ad': 0 or 1 indicated clicking on Ad

Jupyter Notebook

decision-trees-and-random-forest-

For this project we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back. We will try to create a model that will help predict this. Lending club had a [very interesting year in 2016](https://en.wikipedia.org/wiki/Lending_Club#2016), so let's check out some of their data and keep the context in mind. This data is from before they even went public. We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from [here](https://www.lendingclub.com/info/download-data.action) or just use the csv already provided. It's recommended you use the csv provided as it has been cleaned of NA values. Here are what the columns represent: * credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise. * purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other"). * int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates. * installment: The monthly installments owed by the borrower if the loan is funded. * log.annual.inc: The natural log of the self-reported annual income of the borrower. * dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income). * fico: The FICO credit score of the borrower. * days.with.cr.line: The number of days the borrower has had a credit line. * revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle). * revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available). * inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months. * delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years. * pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Jupyter Notebook

socketiochat