Pinned Repositories
API-authentication-API-using-node-and-mongoDB-
Authentication API
Apollo-11
Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.
blog
KenafricaGram
A mini app to post my pieces
portfolio
catering
practise
portfolio
RecognizeMe
Facial recognition web app for processing real time video feeds.
selenium_projects
A collection of projects that I used Selenium for.
Ken-Sarowiwa's Repositories
Ken-Sarowiwa/CompreFace
Free and open-source face recognition system from Exadel
Ken-Sarowiwa/University-Facial-Multifactor-Authentication-System
I have created a GUI Based Contactless University Access Control, Facial Authentication System. By utilizing OpenCV, Haar Cascade, and Python.
Ken-Sarowiwa/openface
Face recognition with deep neural networks.
Ken-Sarowiwa/bioinformatics
:microscope: Path to a free self-taught education in Bioinformatics!
Ken-Sarowiwa/OpenCV-REST-API
Learn to create a REST API microservice for extracting faces from images using OpenCV, OpenCV-python, Flask, Docker, and Heroku
Ken-Sarowiwa/hackingtool
ALL IN ONE Hacking Tool For Hackers
Ken-Sarowiwa/NEWS_APP
This app enables users to access news from their preferred news media house. The user also has the freedom to choose from whatever category they would wish to update themselves on the news.
Ken-Sarowiwa/food-ordering-system
Food or Item Order Management System
Ken-Sarowiwa/attendance-system
A simple, modern and scalable facial recognition based attendance system built with Python back-end & Angular front-end.
Ken-Sarowiwa/codewars
Ken-Sarowiwa/Simple-Facial-Recognition-For-WebApp-Login
Simple Facial Recognition App Using Flask and RapidAPI for Authentication
Ken-Sarowiwa/HotelReservationSystem
A web application to book a room in a hotel (room reservation).
Ken-Sarowiwa/PrisonManagementSystem
Website that displays a detailed information of prisoners in a prison cell
Ken-Sarowiwa/PredPol---Crime-Analysis
Crimes have been severely increased in past few years, the Problem Statement includes analysis of crimes with different perspectives including utmost attributes possible and predicting via the study of nature of crimes committed. The problem statement is described to initially predict the crime-type based on location and time. We worked on data about historical crimes in California. We had close to 13,000 records of crimes with data on the date and time of the crime, its location, and its type. Common types of crime include theft, criminal damage, criminal trespass, and assault. This project took on the task of predicting the type of crime that was committed given a police report in two ways one according to time that is when crime took place and another is location that is where crime took place. From a small number of overly detailed features, in time it will give the detail that at which time slot which crime is maximum and in location it will tell at which place which type of crime is maximum. They then trained various diagram based models (Graphs and Pie charts) to classify crimes by type using the generated features. Finally, they tested the performance of their models on testing data. They conclude that predicting the type of crimes committed by time and location alone is quite difficult, but that the feature engineering greatly increases predictive power. Predictions will be made to provide local authorities with an upper hand on crime and help them plan a better strategy to tackle the same.
Ken-Sarowiwa/PrisonManagementSystem-1
Ken-Sarowiwa/PMS
Prison Management System-A DBMS Project