Pinned Repositories
Communicate-Data-Findings-Project
Prosper Loan Dataset Analysis
dog_rating
The dataset that I will be wrangling, analyzing, and visualizing is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. These ratings almost always have a denominator of 10. The numerators, though? Almost always greater than 10. 11/10, 12/10, 13/10, etc. Why? Because "they're good dogs Brent." WeRateDogs has over 4 million followers and has received international media coverage. WeRateDogs downloaded their Twitter archive and sent it to Udacity via email exclusively for I to use in this project. This archive contains basic tweet data (tweet ID, timestamp, text, etc.) for all 5000+ of their tweets as they stood on August 1, 2017. More on this soon. Image Predictions File One more cool thing: Udacity ran every image in the WeRateDogs Twitter archive through a neural network that can classify breeds of dogs*. The results: a table full of image predictions (the top three only) alongside each tweet ID, image URL, and the image number that corresponded to the most confident prediction (numbered 1 to 4 since tweets can have up to four images) and all of them on this URL "https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv". Each tweet's retweet count and favorite ("like") count at minimum, and any additional data I find interesting. Using the tweet IDs in the WeRateDogs Twitter archive, query the Twitter API for each tweet's JSON data using Python's Tweepy library and store each tweet's entire set of JSON data in a file called 'tweet_json.txt' file.
draw_with_turtle
I draw some graphs with turtle library
europeansoccerdatabase
This soccer database comes from Kaggle and is well suited for data analysis and machine learning. It contains data for soccer matches, players, and teams from several European countries from 2008 to 2016. This dataset is quite extensive. I'll explore some questions about teams and players so write SQL code to export data from data that I'll use it to explore the questions. and I noticed the problems on data in the first columns the data duplicated in many rows and get at different time.
markdown-here
Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.
matsim-code-examples
A repository containing code examples around MATSim
matsim-example-project
A small example of how to use MATSim as a library.
TSPheuristic
Implemented heuristics algorithms to solve Travel Salesman Problem
WaterNetworkDistribution
I am make a report for existing project "Projet sur les r´eseaux de distribution d’eau"(Water Network Distribution Project) made Pierre Carpentier, ENSTA Paris. To apply numerical optimization methods during "Numerical methods in optimization"course at Matster degree with my friend
whitewine
The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent).
mohmednasr's Repositories
mohmednasr/Communicate-Data-Findings-Project
Prosper Loan Dataset Analysis
mohmednasr/dog_rating
The dataset that I will be wrangling, analyzing, and visualizing is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. These ratings almost always have a denominator of 10. The numerators, though? Almost always greater than 10. 11/10, 12/10, 13/10, etc. Why? Because "they're good dogs Brent." WeRateDogs has over 4 million followers and has received international media coverage. WeRateDogs downloaded their Twitter archive and sent it to Udacity via email exclusively for I to use in this project. This archive contains basic tweet data (tweet ID, timestamp, text, etc.) for all 5000+ of their tweets as they stood on August 1, 2017. More on this soon. Image Predictions File One more cool thing: Udacity ran every image in the WeRateDogs Twitter archive through a neural network that can classify breeds of dogs*. The results: a table full of image predictions (the top three only) alongside each tweet ID, image URL, and the image number that corresponded to the most confident prediction (numbered 1 to 4 since tweets can have up to four images) and all of them on this URL "https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv". Each tweet's retweet count and favorite ("like") count at minimum, and any additional data I find interesting. Using the tweet IDs in the WeRateDogs Twitter archive, query the Twitter API for each tweet's JSON data using Python's Tweepy library and store each tweet's entire set of JSON data in a file called 'tweet_json.txt' file.
mohmednasr/draw_with_turtle
I draw some graphs with turtle library
mohmednasr/europeansoccerdatabase
This soccer database comes from Kaggle and is well suited for data analysis and machine learning. It contains data for soccer matches, players, and teams from several European countries from 2008 to 2016. This dataset is quite extensive. I'll explore some questions about teams and players so write SQL code to export data from data that I'll use it to explore the questions. and I noticed the problems on data in the first columns the data duplicated in many rows and get at different time.
mohmednasr/markdown-here
Google Chrome, Firefox, and Thunderbird extension that lets you write email in Markdown and render it before sending.
mohmednasr/matsim-code-examples
A repository containing code examples around MATSim
mohmednasr/matsim-example-project
A small example of how to use MATSim as a library.
mohmednasr/TSPheuristic
Implemented heuristics algorithms to solve Travel Salesman Problem
mohmednasr/WaterNetworkDistribution
I am make a report for existing project "Projet sur les r´eseaux de distribution d’eau"(Water Network Distribution Project) made Pierre Carpentier, ENSTA Paris. To apply numerical optimization methods during "Numerical methods in optimization"course at Matster degree with my friend
mohmednasr/whitewine
The inputs include objective tests (e.g. PH values) and the output is based on sensory data (median of at least 3 evaluations made by wine experts). Each expert graded the wine quality between 0 (very bad) and 10 (very excellent).
mohmednasr/matsim-libs
Multi-Agent Transport Simulation
mohmednasr/matsim-maas
This project contains a collection of examples to run (Autonomous) Mobility as a Service in MATSim.
mohmednasr/matsim-serengeti-park-hodenhagen
mohmednasr/pt2matsim
Package to create a multi-modal MATSim network and schedule from public transit data (GTFS or HAFAS) and an OSM map of the area.