CSGO-Pro-Gear-Performance-and-EDA
feature-engineering-tutorials
| CSGO-Pro-Gear-Performance-and-EDA | feature-engineering-tutorials | |
|---|---|---|
| 5 | 1 | |
| 1 | 266 | |
| - | 1.1% | |
| 0.0 | 0.0 | |
| almost 2 years ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
CSGO-Pro-Gear-Performance-and-EDA
- The outputs of my jupyter notebooks inside of Github repos only show half of what they used to. Why did this happen and how to fix? I am certain that the outputs used to show everything when viewed in Github, and I have not reuploaded the notebooks to the repo's since then.
- The outputs of my jupyter notebooks inside of Github repos only show half of what they used to. Why did this happen and how to fix? I am certain that the outputs used to show everything when viewed in Github.
- I wanted to share my first personal data science project. I'm also looking for criticism. I set out to see how well you could model CS:GO player's accuracy performance based on their gear and settings alone. Please check out my repo link in the text!
- I wanted to share my first personal data science project. I'm also looking for criticism. I set out to model CS:GO player's accuracy performance based on their gear and settings alone. Sorry if this counts as self promotion. Please check out my repo link in the text!
feature-engineering-tutorials
-
How to balance multiple time series data?
I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.
What are some alternatives?
NLP-CNN-Subreddit-Sorter-Heroku-App - End-to-end development of an application using a convolutional neural network that suggests to users/moderators which technical subreddit a post actually belongs to. Novel method to determine # of CNN filters. Custom Word2vec embeddings. The subreddits chosen are all technical and similar, and benefit users/moderators interested in data science and related fields. (Exploratory data analysis, feature engineering, custom word2vec embeddings, convolutional neural network, deployment via flask to Heroku )
jupyter-notebook-chatcompletion - Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!
football-crunching - Analysis and datasets about football (soccer)
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
RecSys_Course_AT_PoliMi - This is the official repository for the Recommender Systems course at Politecnico di Milano.
dtreeviz - A python library for decision tree visualization and model interpretation.
csgo-impact-rating - A probabilistic player rating system for Counter Strike: Global Offensive, powered by machine learning
ydata-quality - Data Quality assessment with one line of code
Epidemiology101 - Epidemic Modeling for Everyone
gastrodon - Visualize RDF data in Jupyter with Pandas
PRML - PRML algorithms implemented in Python
desbordante-core - Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.