My projects, you can find Data Science, Machine Learning, Deep Learning, Econometrics and Statistics stuff here. Notebooks, papers (reports) and presentations.

Hope you enjoy!

January 16, 2021

German real estate modelling with quantile regression

This project is about hedonic price model for different price distribution quantiles (German real estate) estimation, you can expect: short data analysis (dependent variable distribution and its moments) data transforms (logarithm, ordinal, nominal transforms) standard hedonic price model estimation (OLS) statistical tests (Jarque-Bera, White) outliers detection (Cook’s distance, standarized and studentized residuals) quantiles hedonic price models estimation (with bootstraped covariance matrix - 999 replications) extremely interesting Beta coefficients estimates vs price quantiles plots with interpretation and inference quality paper-style report in Polish GitHub repository

January 9, 2021

Can PCA extract important informations from non-significant features? Neural Network case

This project is about boosting Neural Networks with PCA (other ML algorithms as benchmarks), you can expect: data preparation (renaming labels, balance check, standarization) Random Forest based data imputation algorithm development n-Fold Cross Validation study Machine Learning algorithms (Random Forest, XGBoost with hiperparameters optimization) 6 feature selection methods to spot non-significant features (RF importance, Mutual Information, Spearman correlation between features and with target, General to Specific econometrics procedure, Lasso logistic regression) Neural Networks development (architecture, optimizers, activations, regularization, dropout, batch norm, hyperparameters) Principal Component Analysis of the dataset PCA integration with Nets in CV hypothesis verification using the Wilcoxon test for equality of medians models comparison Python notebook in English (a ton of Python code) quality paper-style report in English project presentation in English GitHub repository

December 22, 2020

Rainfall modelling with OLS and Kernel Regression

This project is about Rainfall modelling and assumptions testing, you can expect: working on real data from Polish Institute of Meteorology and Weather Management data cleaning and analysis econometric modelling (OLS, Kernel Regression) OLS assumptions testing (RESET (several alternatives), Breusch-Pagan, Breusch-Godfrey, Jarque-Bera, Rescaled Moments, VIFs) model selection (information criteria) performance measurement (MSE, RMSE, MAE, MAPE, R squared and adjusted R squared, F test, scatterplots) data transformations (Principal Component Analysis, Box Cox power transform) “forecasting” (see the report why quotation marks used ;)) discussion about endogeneity Python notebook (and many HTML files with models for many weather stations) quality paper-style report in Polish GitHub repository