AbdelrahmanRagab38

Automatic-License-Number-Plate-Recognition

This project aims to recognize license number plates, the project could be useful for security, monitoring, e-challan, etc. In order to detect license number plates, we will use OpenCV to identify number plates and python pytesseract to extract characters and digits from the number plates.

BikeRent

Exam-Cell-Automation-System.

Currently Exam cell activity mostly includes a lot of manual calculations and is mostly paper based. The project aims to bring in a centralized system that will ensure the activities in the context of an examination that can be effectively managed.  This system allows students to enroll themselves into the system by registering their names or by sharing details to admin.  This is done by providing their personal and all the necessary details like Name, email, examination, semester, etc.  The provided details are then entered by admin into the system to create their hall tickets and also creates login id and password for them.  After creating the hall ticket, the system mails the link of soft copy to every student who have registered.  Students containing link in the mail can view and print the hall ticket and also can login into the system using login id and password to modify or update their details like Phone number, email-id, etc.  Admin is also responsible for generation of mark sheets for every registered student.  There will be total three to six semesters where each semester contains maximum seven subjects.  Admin can enter the marks of every student into their respective mark sheet using the system’s GUI or via Database entry.  Every student mark sheet will be created and printed separately.  Thus on a whole it serves as a complete automated software which handles the every tedious and complex process handled during the examination times by the exam cell of a college. This system comprises of 6 Modules: Description: 1. Student Register: - To register students have to just provide their Personal Details like Name, Address, Phone No, etc., and a photo, to the enroll himself into the System. 2. Admin Login: - Admin can view who has enrolled into the system, and can see all the new enrollment on his login. 3. Send Email: - After creating the hall ticket, the system mails the link of soft copy to every student who have registered. 4. Student Login: - Students can use their credentials provided by admin to login into the system 5. View and Update Details: - System allows registered students to view and modify/update the personal details like Phone number, email-id, etc. 6. Mark sheet Generation: - System allows admin to generate mark sheet of examination for every student. Advantages  This system allows only the registered students to login into the system which prevents unauthorized access. Helwan University - Faculty of Computers and Information - Computer Science Department Module: CS251 Software Engineering I – Spring “Semester 2” 2018-19 Page 30 of 44  The students can view their hall ticket by just clicking on link provided by admin via e-mail.  The students can even update their details as and when required.  Boosts enterprise accessibility.  Faster exam registration.  Easy result generation.  Improved accuracy of student data.  Better convenience for students.

Homeversit_Website

Make-a-square

Make a square with size 4X4 by using 4 or 5 pieces. The pieces can be rotated or flipped and all pieces should be used to form a square. Example sets of pieces. There may be more than one possible solution for a set of pieces, and not every arrangement will work even with a set for which a solution can be found. Examples using the above set of pieces... Rotate piece D 90 degree then flip horizontal {R 90 + F H}

Natural-Language-Processing-with-Classification-and-Vector-Spaces-nlp_specialization-course1

course 1 of coursera specialization

Online-bus-booking-system

Online Bus Booking System Modules ADMIN MODULE AGENT MODULE USER MODULE Features of Online Bus Booking System ADMIN MODULE Admin has overall control of the system. The main functions of admin are given below. Bus Management Route Management Board Point Management Drop Point Management Promo Code management Gallery Add Agent Cancellation View Booking Details Seat Layout View Rating Details Admin login username - amit password amit USER MODULE Can register or login Book bus. View and select the seat Use promo code Book the ticket by selecting route, date of journey and the return date View available buses Payment integrated Brief overview of the technology: Front end: HTML, CSS, JavaScript HTML: HTML is used to create and save web document. E.g. Notepad/Notepad++ CSS : (Cascading Style Sheets) Create attractive Layout Bootstrap : responsive design mobile freindly site JavaScript: it is a programming language, commonly use with web browsers. Back end: PHP, MySQL PHP: Hypertext Preprocessor (PHP) is a technology that allows software developers to create dynamically generated web pages, in HTML, XML, or other document types, as per client request. PHP is open source software. MySQL: MySql is a database, widely used for accessing querying, updating, and managing data in databases. Software Requirement(any one) XAMPP Server LAMP Server Installation Steps 1. Download zip file and Unzip file on your local server.4 2-7ot tahina 3-put the project in the hdocs file in xammp 3. Database Configuration Open phpmyadmin Create Database named bus. Import database bus.sql from downloaded folder(inside database) 4. Open Your browser put inside "http://localhost/Bus Booking System

Predicting-Credit-Card-Fraud-Transactions

# Problem: Predicting Credit Card Fraud ## Introduction to business scenario You work for a multinational bank. There has been a significant increase in the number of customers experiencing credit card fraud over the last few months. A major news outlet even recently published a story about the credit card fraud you and other banks are experiencing. As a response to this situation, you have been tasked to solve part of this problem by leveraging machine learning to identify fraudulent credit card transactions before they have a larger impact on your company. You have been given access to a dataset of past credit card transactions, which you can use to train a machine learning model to predict if transactions are fraudulent or not. ## About this dataset The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred over the course of two days and includes examples of both fraudulent and legitimate transactions. ### Features The dataset contains over 30 numerical features, most of which have undergone principal component analysis (PCA) transformations because of personal privacy issues with the data. The only features that have not been transformed with PCA are 'Time' and 'Amount'. The feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction amount. 'Class' is the response or target variable, and it takes a value of '1' in cases of fraud and '0' otherwise. Features: `V1, V2, ... V28`: Principal components obtained with PCA Non-PCA features: - `Time`: Seconds elapsed between each transaction and the first transaction in the dataset, $T_x - t_0$ - `Amount`: Transaction amount; this feature can be used for example-dependent cost-sensitive learning - `Class`: Target variable where `Fraud = 1` and `Not Fraud = 0` ### Dataset attributions Website: https://www.openml.org/d/1597 Twitter: https://twitter.com/dalpozz/status/645542397569593344 Authors: Andrea Dal Pozzolo, Olivier Caelen, and Gianluca Bontempi Source: Credit card fraud detection - June 25, 2015 Official citation: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson, and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015. The dataset has been collected and analyzed during a research collaboration of Worldline and the Machine Learning Group (mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML.

Sentiment-Analysis-for-IMDB-Movie-Reviews

USD-EGP-Time-Series-Forecasting

AbdelrahmanRagab38/Automatic-License-Number-Plate-Recognition

This project aims to recognize license number plates, the project could be useful for security, monitoring, e-challan, etc. In order to detect license number plates, we will use OpenCV to identify number plates and python pytesseract to extract characters and digits from the number plates.

AbdelrahmanRagab38/BikeRent

AbdelrahmanRagab38/Homeversit_Website

AbdelrahmanRagab38/Recomendtions_System_Course

AbdelrahmanRagab38/USD-EGP-Time-Series-Forecasting

AbdelrahmanRagab38/Sentiment-Analysis-for-IMDB-Movie-Reviews

AbdelrahmanRagab38/AIND-Capstone

Machine Translation with RNNs. This is the capstone project for the Udacity AI Nanodegree.

AbdelrahmanRagab38/Arabic_Dialect-Last-version

AbdelrahmanRagab38/Arabic_Preprocissing_data

AbdelrahmanRagab38/Chatbot_Project

The dataset for a chatbot is a JSON file that has disparate tags like goodbye, greetings, pharmacy_search, hospital_search, etc. Every tag has a list of patterns that a user can ask, and the chatbot will respond according to that pattern.

AbdelrahmanRagab38/CICD

AbdelrahmanRagab38/Count_down_Timer_Python_Gui

AbdelrahmanRagab38/Detect-fraudulent-activities-Enron-Email-Dataset

AbdelrahmanRagab38/Emojify-Facial-emojii-identification-

Emojify-Facial emojii identification

AbdelrahmanRagab38/Generate_tv_scripts_using_lstm_Udacity_deep_learning

AbdelrahmanRagab38/Image-Caption-Generator-with-CNN-LSTM

In this project, we will be implementing the caption generator using CNN (Convolutional Neural Networks) and LSTM (Long short term memory).

AbdelrahmanRagab38/IR_Project

AbdelrahmanRagab38/machine-learning-engineering-for-production-public

Public repo for DeepLearning.AI MLEP Specialization

AbdelrahmanRagab38/MLOps_practice

Learning and practice MLOps

AbdelrahmanRagab38/Music-Genre-Classification

Deep learning project to automatically classify different musical genres from audio files

AbdelrahmanRagab38/My-projects-for-Deep_Learning-Nanogegree-Udacity

AbdelrahmanRagab38/MyNotes_TimeSeriesForecastingInPython_TextBook

This repo Contains My Study notes & practice For the Time Series Forecasting In Python

AbdelrahmanRagab38/Project_Plagiarism_Detection

AbdelrahmanRagab38/PyQT_Learning_Practice

Learning - practice what i learned from pyQT tutorials

AbdelrahmanRagab38/python-cli-example

A small Python CLI example that you can use to build on

AbdelrahmanRagab38/toolSearchingFlutter

Tool Searching app Flutter Web

AbdelrahmanRagab38/tutorials

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

AbdelrahmanRagab38/Udacity_P3

AbdelrahmanRagab38/Udacity_third-project_visualization

AbdelrahmanRagab38/Youtube-Spam-detector-with-Flask-Api

Machine learning web application using flask api