I am an Industrial Engineering student at the Bogazici University.I also do a long term internship at Pfizer.My responsibilities are to make sales analysis and reporting.I’am pursuing a data scientist career.My linkedin profile can be reached via this link.
useR! 2020: Prediction with text using tidy data principles (J. Silge, E. Hvitfeldt), tutorial
This tutorial is geared toward an R user with intermediate familiarity with R, RStudio, the basics of regression and classification modeling, and tidyverse packages such as dplyr and ggplot2. This person is comfortable with the main functions from dplyr and ggplot2 and is now ready to learn how to analyze and model text using tidy data principles. This R user has some experience with statistical modeling (such as using lm() and glm()) for prediction and classification and wants to learn how to build models with text.
A general and technical analysis of Amazon (AMZN)’s stock and a price simulation using random walk and monte carlo method. Visualizations done with plotly and ggplot.
Python is great, but when it comes to forecasting, I personally think R still has the upper hand. The Forecast package is the most complete forecasting package available on R or Python, and it’s worth knowing about it. Here is what we will see in this article: Naive methods, Exponential Smoothing (State-space models and DSHW), BATS and TBATS, ARIMA/SARIMA models, How to set up a one-step-ahead forecast.
NYC Taxi Fare Prediction with Gradient Boosting Algorithm
Gradient Boosting (GBM) is a learning technique which combines the outputs of many simple predictors to build a powerful predictor with improved performance over the base learner tree. The new tree is an enhancement version of the previous tree, therefore this technique is known as boosting, where new tree boosts the performance of the original version (Biau, et al., 2019).