Position: Junior Data Scientist
Category: Data Science
English level: upper
Years of experience: 1

Original project:
My goal was to create model that can predict count of likes based on text and amount of followers. I have used Twitter API to take data about users and tweets( like the amount of followers, text of a tweet, count of likes, etc). Then I have preprocessed data with pandas and numpy. My next step was to create new csv with less data, because my laptop was not powerful enough. So, then I just preprocessed data (vectorized text and added to this vector count of followers). The last step was to make the model. I found the best model - it's Logistic Regression, which gives for me an 89% accuracy.
And I've taken part in the competitions on the Kaggle.
Python Developer / Data Scientist (October 2022 - August 2023).
Implement time series forecasting techniques for solar
prediction.
XGBoost(with trees) have been chosen as model for this
project, because first of all in this dataset we had almost no correlation between X and Y, so Linear Regression will not fit, resources was limited so LSTM was no use for this project. And among all tree models, XGBoost gives the best results. It gives best results due to many reasons, for example: regularization built in this model, it is ensemble method, it has good tuning due to parametrize.