The following projects are designed to develop the skills and abilities of a data analyst. When combined with other projects, they form a portfolio of various approaches to data collection, transformation, analysis, and visualization.
| Name of project | Description of project | Skills and tools |
|---|---|---|
| Anomaly detection | The goal of the project is to identify unusual patterns or outliers in the data that deviate from expected behavior using multivariate anomaly detection techniques - Cluster-based Local Outlier Factor, Histogram-based Outlier Detection, Isolation Forest and K-Nearest Neighbours. | python, pandas, matplotlib, seaborn, sklearn, pyod |
| Customer segmentation | This project aims to classify customers using the RFM (recency, frequency, monetary) technique. The main goal of the project is to create a model that predicts purchases made by a new customer in the next few years, starting from their first purchase. | python, pandas, matplotlib, seaborn, plotly, sklearn, scipy, yellowbrick |
| Market basket analysis | In this study, we use transactional data from a retail store to analyze the items that are often purchased together. We use the apriori algorithm and association rules to identify these patterns. | python, pandas, matplotlib, mlxtend |
| Time series forecast | The project is dedicated to predicting future values based on historical time series data - in this case, data from the OpenAQ API. | python, pandas, geopandas, matplotlib, seaborn, plotly, requests, statsmodels, ARIMA |