Datascience and data analytics projects and scripts

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Bachelor thesis

MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels in Stacking Ensemble Learning

Stacking, or stacked generalization, is a technique in ensemble learning where multiple base models, or “weak learners,” are trained and combined to form a metamodel with improved predictive power. The ISOVIS research group at LNU has created StackGenVis, a visual analytics system that helps users optimize performance metrics, manage input data, including selecting features, and choose top-performing algorithms. The current version of StackGenVis uses a single Linear Regression metamodel. This work aims to investigate the impact of alternative metamodels on the predictive performance of StackGenVis using provided data and charts for comparison.

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Numerai Ensembling

Script used and testing in Numer.ai ML competition, check additional details at https://docs.numer.ai/tournament/learn

Notebook includes following steps:

• feature selection using univariate Selection and Fit model using each importance as a threshold
• Model testing and parameter tuning for Lasso Regression, Ridge Regression, XGBoost and ElasticNet models
• Model ensembling using stacked generalization

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Hobby projects

USD/RUB exchange rate and Oil Brent price correlation

• Analysis perfomed to discover the correlation (if any) between USD/RUB exchange rate and Oil Brent price.
• Intended as home project for DS course. Script includes Exploratory analysis steps, data wrangling stepsand simple machine learning models check.

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Supervised Learning techniques

notebooks created for better material understanding

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• MNIST K-NN Classification using numpy
• k-NN Classification using scikit-learn
• k-NN Regression using numpy
• Multivariate regression using gradient descent
• Polynomial regression
• Multivariate Logistic Regression
• Nonlinear logistic regression
• Regularization techniques
• Support Vector Machines with Gaussian and ANOVA(own implementation) kernels
• Support Vector Machines - One versus all MNIST
• An effect of ensembling
• Simple Neural Network - Fashion MNIST

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Unsupervised Learning techniques

notebooks created for better material understanding

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• Clustering and Dimensionality Reduction

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