Project | Description | Libraries | Skills |
---|---|---|---|
Automation of cow quality assessment | Automating the calculation of a cow's approximate milk yield and predicting the taste of its milk | Pandas Numpy Matplotlib Seaborn Scikit-learn Scipy |
Machine Learning Data Analysis Regression Classification |
Automation of personnel management | Creating a model that can predict employee satisfaction levels and a model that can predict potential employee terminations | Pandas Numpy Matplotlib Seaborn Scikit-learn Shap Phik |
Machine Learning Data Analysis Regression Classification |
Selecting a drilling location using a linear model | Creating a model to identify the most profitable and analysing possible profit and risks | Pandas Numpy Matplotlib Seaborn Scikit-learn |
Machine Learning Data Analysis Regression Bootstrap |
Determining the cost of cars | Creation of a model to determine the market value of the machine | Pandas Numpy Matplotlib Seaborn Scikit-learn Phik LightGBM |
Machine Learning Data Analysis Regression Gradient Boosting |
Forecasting taxi orders | Creating a model to predict values based on time series | Pandas Numpy Matplotlib Scikit-learn Statsmodel LightGBM |
Machine Learning Data Analysis Regression Gradient Boosting TimeSeries |
Classification of text sentiment | Creating a model to classify the toxicity of comments | Pandas Numpy Matplotlib Scikit-learn LightGBM nltk tdqm WordCloud SpaCy |
Machine Learning Data Analysis Classification Gradient Boosting Text NLP TF-IDF |
Determining the age of buyers from photographs | Creating a model to predict a person's age from images | Pandas Numpy Matplotlib keras TensorFlow |
Machine Learning Data Analysis Regression Computer Vision TensorFlow |
Determining the churn of telecom operator customers | Creating a model for predicting the termination of a contract with a telecom operator by a client | Pandas Numpy Matplotlib Seaborn Scikit-learn Phik LightGBM Catboost |
Machine Learning Data Analysis Classification Gradient Boosting |
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