Michael Farid

A React Web application to identify fake products using blockchain technology in the supply chain. Utilized using turffle framework with solidity programming language. Allows the company to upload its products and we generate a QR code that corresponds to each of them. The user scans the QR code to indentify if the product is the authentic one or fake.

A tool used to show the relations and properties of people (nodes) in a social network. This include community detection using Louvain Algorithm with some community detection techniques including conductance, modularity, NMI and community coverage. It also utilized page rank for link analysis technique using • Python & Tkinter (GUI library)

Implementation of a Deep learning model aimed at classifying a binary Gender dataset . Improve the model's layers to fit well in the dataset and get high performance. This Model Includes dataset preparation, Preprocessing , Choosing the most suitable model, Training & Testing the model and Modifying the neural network layers to improve efficiency.

This stock price prediction project utilizes historical market data, volatility indices, and fundamental indicators to forecast future stock prices. It encompasses data import, preprocessing, exploratory analysis, and the application of a linear regression model to make predictions. The project is accessible via this Colab link.

This project involves using a dataset to assess the probability ofa breast tumor being malignant or benign. It considers factorssuch as lump thickness, bare nuclei count, and mitosis. It's avaluable opportunity for aspiring machine learning practitionersto gain experience in R programming while working on a real-world problem.

This is a machine learning model written in Python that predicts the gradient descent of one variable according to another. The Dataset contains 12 columns and 300152 rows. It describes the departure time, arrival time, source city, destination city, flight number, price, ... of each flight. Data visualization include Horizontal Bar Chart, Pie Chart, Histograms, Box Plot, Scatter Plot.

analyzed a real dataset on the COVID-19 pandemic. Applying the data analytic project cycle, I explored the dataset's attributes, performed data cleaning, and visualized the data using various charts. I applied anomaly detection to identify outliers and utilized two types of predictive analytic techniques. Additionally, I performed text mining on a separate text dataset. Overall, my project provided insights into COVID-19 data through data analytics techniques.

This Shiny dashboard provides a visual summary of COVID-19 data, including global statistics, top and lowest countries in terms of deaths, continent-specific information, and histograms for death and case data. Users can interact with different tabs to explore and analyze COVID-19 statistics.