Asmaa mohamed 's Projects
All Algorithms implemented in Python
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
Python Data Science Handbook: full text in Jupyter Notebooks
Maven Tooling and Xchart Integration
A game theoretic approach to explain the output of any machine learning model.
Every thing you need to know about using Sklearn in Machine Learning
A complete guide on how to start learning machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field.
This repository implements several swarm optimization algorithms and visualizes them. Implemented algorithms: Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Search (CS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA)
An Open Source Machine Learning Framework for Everyone
Project Description Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this project, you will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. You'll look for insights in the data to devise strategies to drive growth and retention. The data for this project was scraped from the Google Play website. While there are many popular datasets for Apple App Store, there aren't many for Google Play apps, which is partially due to the increased difficulty in scraping the latter as compared to the former. The data files are as follows: apps.csv: contains all the details of the apps on Google Play. These are the features that describe an app. user_reviews.csv: contains 100 reviews for each app, most helpful first. The text in each review has been pre-processed, passed through a sentiment analyzer engine and tagged with its sentiment score.Project Tasks 1. Google Play Store apps and reviews 2. Data cleaning 3. Correcting data types 4. Exploring app categories 5. Distribution of app ratings 6. Size and price of an app 7. Relation between app category and app price 8. Filter out "junk" apps 9. Popularity of paid apps vs free apps 10. Sentiment analysis of user reviews
A New Interactive Approach to Learning Data Analysis
Project Description Open source projects contain entire development histories, such as who made changes, the changes themselves, and code reviews. In this project, you'll be challenged to read in, clean up, and visualize the real-world project repository of Scala that spans data from a version control system (Git) as well as a project hosting site (GitHub). With almost 30,000 commits and a history spanning over ten years, Scala is a mature language. You will find out who has had the most influence on its development and who are the experts. The dataset includes the project history of Scala retrieved from Git and GitHub as a set of CSV files. Project Tasks 1. Scala's real-world project repository data 2. Preparing and cleaning the data 3. Merging the DataFrames 4. Is the project still actively maintained? 5. Is there camaraderie in the project? 6. What files were changed in the last ten pull requests? 7. Who made the most pull requests to a given file? 8. Who made the last ten pull requests on a given file? 9. The pull requests of two special developers 10. Visualizing the contributions of each developer
Python script written to explore US bikeshare data
Official implementation of "Using Variational Multi-view Learning for Classification of Grocery Items" in Tensorflow v1.
Udacity Data Analyst Nanodegree Project 7 - Wrangle and Analyze WeRateDogs Twitter account.
Perform data analysis based on the dataset of Wuzzuf.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent