creation of an interface and algorithm for forecasting demand for 14 days for goods of own production
Effort here is to identify important features in relation to our target variable using multiple Correlation methods based on data type. Feature selection is important for ML models to avoid 'curse of dimensionality' but for this dataset we will be using it build our intution that benefits our later EDA effort
A set of custom python modules for friendly workflow on pandas
Exploratory Data Analysis of MIMIC-II Dataset and extracted machine learning features
Оценка риска попадания в ДТП по маршруту движения каршеринга
Yandex Practicum Data Science project
ENG/RU Exploratory Data Analysis "Moscow's Taste: Venue Analysis and Recommendations for Investors" in the Jupyter notebook. The analysis uses libraries such as pandas, seaborn, matplotlib, and phik.
Star temperature prediction
Нейросетевая модель для предсказания температуры звёзд с RMSE 4263 на основе PyTorch. Использованы Python, Scikit-learn, Pandas, корреляция Phik.
Модель прогнозирования выживаемости стартапов с F1 0.997 на основе DecisionTree, оптимизированного через Optuna. Использованы Python, Scikit-learn, Optuna.
Creating a ML-model to forecast the presence of a heart disease
Прогнозирование покупательской активности 83BB клиентов
Building a model for predicting ad clicks
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