Pinned Repositories
-lie-detector-with-Hiden-markov-models-
реализовать детектор лжи, который по подрагиванию рук человека, определяет, говорит он правду или нет. Допустим, когда человек лжет, руки трясутся чуть больше.
AI-hackaton
Ensemble-of-models
Exchange-Rates-fitting
Improved-XGboost-model
Text segmentation (classification)
LDA-analysis.
Latent Drichlet Allocation for text modeling
Model
Neural-Network-Predicting-number-from-Image
Here I'm using Neural Network algorithms to make model, which can predict number from picture.
Obesity-in-America-data-visualization
Just another repisitory about data visualization
rproject
Проекты связанные с анализом данных
Dikosh's Repositories
Dikosh/AI-hackaton
Dikosh/LDA-analysis.
Latent Drichlet Allocation for text modeling
Dikosh/t-sne-algorithm
Dikosh/text-analysisR
Dikosh/Text-mining
Dikosh/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow
Dikosh/ab-test
A/B testing is a method for comparing the effectiveness of several different variations of a web page. For example, an online clothing retailer that specializes in mens’ streetwear may want to examine whether a black or pink background results in more purchases from visitors to the site. Lets say that our online store is just a single web page, and we run this experiment by randomly showing one variation (pink background) of the page to half the visitors and the control background to the other half. After running the experiment for one week, we find that the pink background resulted in 40% purchase rate with 500 visitors while the black background resulted in a 30% purchase rate with 550 visitors. So which background is more effective at generating purchases from visitors to the online store. One way to examine this problem is by calculating confidence intervals of the conversion rates for each variation of the site. In the following R code, I construct a function which calculates the confidence intervals for the purchase rate of each site at a 80% significance level. In this example, the purchase rate for the pink background is significantly higher than the purchase rate for the black background
Dikosh/asd
NewYORK
Dikosh/asd1
NewYORK
Dikosh/Data-Analysis
Data Science Using Python
Dikosh/datasciencecoursera
Dikosh/datasharing
The Leek group guide to data sharing
Dikosh/ddd-data-set
Dikosh/DinislamOraz
sandbox
Dikosh/Dispersionni-analysis
Dikosh/dom
Dikosh/domilion
Dikosh/equipmetry
Dikosh/LSTM-music
LSTM MUSIC
Dikosh/Machine_learning_course
Dikosh/mathcalc
test repo
Dikosh/onewayAnova
Dikosh/pareto-chart
The Pareto Chart brings immediate focus to which reasons are part of the “vital few” and thus should receive attention first. By dropping a vertical line from where the horizontal line a t 80% intersects the cumulative percentage line, t his chart shows that traffic, oversleeping, and alarm failure are the most critical reasons that people in our survey are late for work. The next step in our problem solving activity might be to use the 5 W hys or Ishikawa/Fishbone Diagram techniques to determine the root causes of those reasons.
Dikosh/Recommender-systems
Here I will make post some posts about recommender systems types with description and code :)
Dikosh/Regression
Dikosh/Regression1
Dikosh/streamlit-example
Example Streamlit app that you can fork to test out share.streamlit.io
Dikosh/week1
Dikosh/week1_1
asd
Dikosh/xgboost-1-