Yandex Praktikum: https://praktikum.yandex.ru/
Certificate : https://www.linkedin.com/in/maksimchernikov/
This repository is mainly for projects I have done under Yandex Praktikum in 2020. As part of the training, I independently completed 18 projects that were reviewed by experienced mentors. I have selected the most difficult and interesting of them so that you have the opportunity to evaluate my skills.
The Yandex Praktikum Data Science program prepares me for a career as a Data Scientist, helping me learn how to cleanse and organize data, analyze it, test hypotheses, find patterns and relationships, select features, build forecasting and classification models, choose a suitable algorithm, select hyperparameters for models, work with images, text and flat tables and etc.
Programming language: Python (ver. 3)
IDE: Jupyter Notebook
Libraries: Numpy, Pandas, Matplotlib, Seaborn, PostgreSQL, Scikit learn, LightGBM, Catboost, Keras, PyTorch, Pymystem3, Collections
Tips: If you want to run this code localy, I would recomend you to install Anaconda and use Jupyter Notebook with it's basic libraries: Numpy , Pandas , SciPy , Scikit learn , Matplotlib , Seaborn. Libraries may vary depending on the projects. By the way, Yandex prohibits uploading source datasets and full project descriptions, so you won't see them here (non-disclosure agreement).
| Project name | Description | Libraries | Status |
|---|---|---|---|
| Аnalysis of mobile tariffs | Analysis of 2 mobile tariffs in order to identify the most profitable | pandas, numpy, scipy, matplotlib, seaborn | Done |
| Game industry analysis | Analyze the gaming industry and identify patterns that determine the success of the game | pandas, numpy, scipy, matplotlib, seaborn | Done |
| Bank customer churn prediction | Develop an ML model that predicts the churn of bank customers | pandas, numpy, scikit learn, matplotlib, seaborn | Done |
| Oil production | Develop an ML model that will help determine the region where oil production will bring the greatest profit | pandas, numpy, scikit learn | Done |
| Auto price prediction | Develop an ML model with which you can find out the market value of your car | pandas, numpy, scikit learn, lightgbm, catboost, matplotlib, seaborn | Done |
| Taxi orders prediction | Develop an ML model for predicting the number of taxi orders for the next hour | pandas, numpy, scikit learn, lightgbm, statsmodels, matplotlib, seaborn | Done |
| Toxic comments classification | Develop an ML model for classifying comments into positive and negative | pandas, numpy, scikit learn, torch, tqdm, sys, os, time, transformers, nltk, matplotlib, seaborn | Done |
| Steel temperature prediction | Develop an ML model that predicts the temperature at the final stage of steel processing | pandas, numpy, scikit learn, lightgbm, matplotlib, seaborn | Done |
| Gold recovery rate prediction | Develop an ML model that predicts the recovery rate of gold from a gold ore | pandas, numpy, scikit learn, matplotlib, seaborn | Done |
All projects are described in Russian, if you want to delve deeper into them feel free to contact me and I will translate them into English for you: chernikovmaksim7@gmail.com