Pinned Repositories
abraxas
Alejandro-Coronado
Algunos Documuentos
arquitecturaMCC
Proyectos de Arquitectura
awesome-public-datasets
An awesome list of high-quality open datasets in public domains (on-going). By everyone, for everyone!
BiologicalReponse-Kaggle
BSS-Mexico
clustnet
compiladorDecaf
python-instagram-marketingbot
This this an instagram bot that reads a list of hashtags and every day follow new people that ulploaded post with tha hastagh and then follow. Then the bot will perform a customer adquisition startegy to get their attention and pass a message.
tesisAlejandroCoronado
coronate-zz's Repositories
coronate-zz/python-instagram-marketingbot
This this an instagram bot that reads a list of hashtags and every day follow new people that ulploaded post with tha hastagh and then follow. Then the bot will perform a customer adquisition startegy to get their attention and pass a message.
coronate-zz/abraxas
coronate-zz/BSS-Mexico
coronate-zz/clustnet
coronate-zz/compiladorDecaf
coronate-zz/docker
docker help functios
coronate-zz/DonorsChoose_Application_Screening_analysis
Graphical Analysis of the DonorsChoose.org Application Screening Kaggle competition.
coronate-zz/first-edition
The book's repo
coronate-zz/flask
The Flask Mega-Tutorial from Miguel Bringuer
coronate-zz/GA-Customer-Revenue
The 80/20 rule has proven true for many businesses–only a small percentage of customers produce most of the revenue. As such, marketing teams are challenged to make appropriate investments in promotional strategies. GStore RStudio, the developer of free and open tools for R and enterprise-ready products for teams to scale and share work, has partnered with Google Cloud and Kaggle to demonstrate the business impact that thorough data analysis can have. In this competition, you’re challenged to analyze a Google Merchandise Store (also known as GStore, where Google swag is sold) customer dataset to predict revenue per customer. Hopefully, the outcome will be more actionable operational changes and a better use of marketing budgets for those companies who choose to use data analysis on top of GA data.
coronate-zz/genetic_BSS
Genetic algorith to solve multiagent traveling salesman problem
coronate-zz/google_landmark_recognition
coronate-zz/handson-ml
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
coronate-zz/Instagram-API-python
Unofficial instagram API, give you access to ALL instagram features (like, follow, upload photo and video and etc)! Write on python.
coronate-zz/instagram_autoai
coronate-zz/instagram_dropbox
This project allows you to sync the dropbox api with the instagram api. Giving you acces to one picture at a time (not comsuming memory resources) and uploading the picures inside your folder acording to an preconfigured schedule (see chrontab.txt). This project is really useful for photographers that don't whant to spend much time uploading photos and changing details through the instagram app. It also use a conventinal naming that gives some credibility to the pictures and provide information to the followers (explain in more detail).
coronate-zz/jigsaw
Can you help detect toxic comments ― and minimize unintended model bias? That's your challenge in this competition. The Conversation AI team, a research initiative founded by Jigsaw and Google (both part of Alphabet), builds technology to protect voices in conversation. A main area of focus is machine learning models that can identify toxicity in online conversations, where toxicity is defined as anything rude, disrespectful or otherwise likely to make someone leave a discussion. Last year, in the Toxic Comment Classification Challenge, you built multi-headed models to recognize toxicity and several subtypes of toxicity. This year's competition is a related challenge: building toxicity models that operate fairly across a diverse range of conversations. Here’s the background: When the Conversation AI team first built toxicity models, they found that the models incorrectly learned to associate the names of frequently attacked identities with toxicity. Models predicted a high likelihood of toxicity for comments containing those identities (e.g. "gay"), even when those comments were not actually toxic (such as "I am a gay woman"). This happens because training data was pulled from available sources where unfortunately, certain identities are overwhelmingly referred to in offensive ways. Training a model from data with these imbalances risks simply mirroring those biases back to users. In this competition, you're challenged to build a model that recognizes toxicity and minimizes this type of unintended bias with respect to mentions of identities. You'll be using a dataset labeled for identity mentions and optimizing a metric designed to measure unintended bias. Develop strategies to reduce unintended bias in machine learning models, and you'll help the Conversation AI team, and the entire industry, build models that work well for a wide range of conversations. Disclaimer: The dataset for this competition contains text that may be considered profane, vulgar, or offensive.
coronate-zz/kickstarter_analysis
coronate-zz/landprices_indonesia
coronate-zz/predictingmolecularproperties
coronate-zz/pydata-book
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
coronate-zz/python-kickstarter-track
In this Repository I'll be uploading the advance of my project:
coronate-zz/RVisualization_StevensInstitute
coronate-zz/santander_customer
coronate-zz/server_client_OperativeSystems
Here is a python program that creates two classes, one for a user and one for a server. The client and the server can communicate using sockets through the localhost.
coronate-zz/spark-scala-tutorial
A free tutorial for Apache Spark.
coronate-zz/tertulia
tertulia
coronate-zz/Tesis
Efecto de la violencia provocada por el Narcotráfico sobre la Industría Manufacturera - Tesis de Licenciatura
coronate-zz/tmdb_boxoffice
We're going to make you an offer you can't refuse: a Kaggle competition! In a world... where movies made an estimated $41.7 billion in 2018, the film industry is more popular than ever. But what movies make the most money at the box office? How much does a director matter? Or the budget? For some movies, it's "You had me at 'Hello.'" For others, the trailer falls short of expectations and you think "What we have here is a failure to communicate." In this competition, you're presented with metadata on over 7,000 past films from The Movie Database to try and predict their overall worldwide box office revenue. Data points provided include cast, crew, plot keywords, budget, posters, release dates, languages, production companies, and countries. You can collect other publicly available data to use in your model predictions, but in the spirit of this competition, use only data that would have been available before a movie's release. Join in, "make our day", and then "you've got to ask yourself one question: 'Do I feel lucky?'"
coronate-zz/ubuntuScripts