| Project | Description |
|---|---|
| Air quality | This survey includes the dataset which contains 9358 instances of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an Air Quality Chemical Multisensor Device. The device was located on the field in a significantly polluted area, at road level, within an Italian city. Data was recorded from March 2004 to February 2005 representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses. The task is to predict the value of target variable C6H6(GT) based on the information about features. So, it is a regression task. The project contains exploratory data analysis (EDA), data preparation, implementing Linear Regression model and regularized regression models as well. In order to evaluate quality of models were used RMSE, R^2 and MAE metrics. |
| Hypothyroid disease | The objective of this research is to design a machine learning algorithm to predict hypothyroid disease. It is a classification task, so we use K-Nearest Neighbors (KNN) and Logistic Regression as base learners. Precision, recall, F1 quality metrics were used to evaluate the models. Various techniques were adopted to preprocess the data to suite the requirement of analysis. Feature selections were made to optimize the performance of machine learning algorithms. However, better feature selection techniques can be applied to further improve the accuracy. |
| Sentiment classification | This document represents a problem of sentiment classification for film rewiews on IMDb. The classification is supposed to be binary (positive and negative classes). The data was loaded, preprocessed, vectorized using TF_IDF method and then tokinized with stemming and lemmatization aproaches separtely. Thereafter SGDClassifier, LinearSVC and NaiveBayes algorithms were implemented. To evaluate the results confusion matrix and ROC curves were plotted. Hyperparameters were tuned to improve the predictive power of models. |
| Jobs advert parsing | This snippet presents a scrapping tool which uses BeatifulSoup library to parse vacancies on the web-site. The script also allows the user to filter out vacancies containig skills with which the user is not familiar with and to save all the collected data to txt file for subsequent use. |
| Snake AI | This project represents a classic snake game, supplemented by reinforcement learning. The game model was written using Pygame library. The other part responsible for reinforcement learning was implemented using Pytorch framework. While computer plays trying to maximize reward, learning curves are plotting in order to track the learning process. |