‍Identifying The Social And Political Correlates of COVID-19 Vaccination Around The World

‣Description:

• I performed a data analysis on Covid-19 vaccination progress using machine learning and statistics to understand the relations between the different political and social characteristics of a country and its vaccination rate.

• I grouped the reasons for the uneven distribution of vaccination around the world into political and social reasons. The political reasons represent the government's inadequacy and the social reasons represent vaccine hesitancy.

• I used data extracted from Our World in Data.

• Final project for the Scientific Computing course, which received full mark.

‣Built With: •Google Colaboratory   • Python 3   •Scikit Learn   •Pandas   •NumPy   •Matplotlib   •Plotly  •IPython

‣Link to Code: Click HERE.          ‣Link to Slides: Click HERE.

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Grapevine Leaves Classification Using CNNs

‣Description:

• Implemented multiple deep machine learning models for grapevine leaf classification using a transfer learning approach based on pre-trained convolutional neural networks, including MobileNetV2, ResNet50, EfficientNetB3, and InceptionNetV3.

• Implemented an autoencoder to evaluate its impact on accuracy and used 10-fold cross-validation for evaluation.

• Final project for the Data Mining course, which received full mark.

‣Built With: •Google Colaboratory   •Python 3   •Scikit Learn   •TensorFlow  •Pandas   •NumPy   •Matplotlib   •Seaborn

‣Link to Code: Click HERE.

‍Statistical Methods With Python

‣Description:

• In this Google Colaboratory I have gathered the definitions and implemented python syntaxes for many famous concepts in statistical methods such as One and Two Sample t-tests, ANOVA Test, Regression, Chi-Square Goodness of Fit Test, and some Non-parametric Tests like Wilcoxon Test.

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‣Built With: •Google Colaboratory   • Python 3   •Scikit Learn   •Pandas   •NumPy   •Matplotlib   •Plotly  •IPython

‣Link to Code: Click HERE.

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House Pricing Prediction Using Regression Methods

‣Description:

• I implemented an optimized prediction algorithm using machine learning regression techniques for house prices by testing Linear Regression, Decision Tree Regression, Random Forest Regression, Support Vector Machine (SVM), and Extreme Gradient Boost Regression (XGBoost) algorithms

• I calculated the model's error using RMSE(Root Mean Square Error), then picked Decision Tree Regressor with an error of 9650 for predicting prices from the test dataset and implemented the feature importance method.

• Final project for the Statistical Methods course, which received full mark.

‍‣Built With: •Google Colaboratory   • Python 3   •Scikit Learn   •Pandas   •NumPy   •Matplotlib   •Plotly  •IPython

‣Link to Code: Click HERE.

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‍Classroom Object Oriented Language(COOL) Compiler

‣Description:

• I implemented a compiler for the Classroom Object Oriented Language(COOL).

• In the first part of the project, I programmed a stack machine using the COOL language.

• In phase one, I implemented a syntax analyzer (i.e. lexer) that catches some syntax errors.

• In phase two, I defined a grammar for the language(i.e. parser) to catch the rest of the syntax errors.

• In phase three, I implemented the type checker(i.e. semantic analyzer) which uses logic rules to perform the type inferences.

• Final project for the Compiler course. (The same final project as Stanford University.)

‍‣Built With: •C++   •Flex   •Bison

‣Link to Code: Click HERE.

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College Management System with Relational Databases

‣Description:

• I designed and developed a college management system using object-oriented programming in Python, with a sophisticated and modular user interface, and a relational database back-end, implemented using the PyQt5 and the SQLite3, respectively.

• Three types of users namely the Dean of faculty, professors, and students can sign up and do various things.

• Final project for the Advanced Programming course. Achieved the highest score among 60 students.‍

‣Built With: •PyQt5   •SQLite3

‣Link to Code: Click HERE.

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‍A Study on Properties of Finite Groups: A Python Implementation

‣Description:

• Designed and created an easy to use interactive program in Google Colaboratory using Jupyter Notebook and the NumPy module in Python.

• The program helps learners to derive useful information about finite groups, including determining the group order, getting the inverse of an element, and generating the centre of the group.

• Final project for the Algebra I course which received full mark and was praised by the teaching assistant.

‣Built With: •Google Colaboratory   •Python 3   •NumPy

‣Link to Code: Click HERE.

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‍Moris Mano Basic Computer In Logisim

‣Description:

• I designed a functional CPU containing ALU and other major parts in Logisim.
• My basic computer consists of the following hardware components:

            1. A memory unit with 4096 words of 16             bits each,

            2. Nine registers: AR, PC, DR, AC, IR, TR,             OUTR, INPR, and SC,

            3. Seven flip-flops: I, S, E, R, lEN, FGI, and FGO,

            4. Two decoders: a 3 x 8 operation decoder and a 4 x 16 timing decoder,

            5. A 16-bit common bus, Control logic gates,            

            6. Adder and logic circuit connected to the input of AC.T

• Final project for the Computer Organization course which r full mark.

‣Built With: •Logisim

‣Link to Code: Click HERE.

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‍Assembler and Disassembler Implemented in Python and Assembly Languages

‣Description:

• I implemented an assembler and a disassembler for the assembly64 language.

• The assembler converts asm64 commands to hexadecimal equivalents. The disassembler converts the binary strings into asm64 commands.

• I've implemented them first using the python language and then using the assembly language itself.

• Final project for the Machine Language and Assembly course. Both projects got the highest score in class among 80 students and passed all the cases.

‣Built With: •Python3   •Assembly64

‣Link to Code: Click HERE.

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Btree Maze Solver

‣Description:

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• Programmed a maze solving application in C++ with an implementation of Dijkstra’s shortest path algorithm using btree for efficiency improvements.

• Final project for the Data Structure and Algorithm Design course.

‣Built With: •C++

‣Link to Code: Click HERE.

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A Game of Puzzle

‣Description:

• Created a 2D puzzle game with multiple difficulty levels using PyGame module in Python.

• Final project for the Fundamentals of Programming course which received full mark.

‣Built With: •Python3   •Pygame

‣Link to Code: Click HERE.