This project aims to analyze the determinants of school performance, specifically focusing on the final exam scores of primary schools in Warsaw.

I decided to explore Airbnb database in New York. Using clustering methods I chose the number of clusters. Clusters were selected in both scenarios: using only locations atributes: longitude and latitude and apart from them: price and type of the rented airbnb. They made it possible to divide the accomodation in New York. In the resulting clusters, I conducted, among other statistics, an analysis of the price in the districts of New York. The main purpose is to examine the price of airbnb in clusters. We can expect that price of booking depend on its location. For example in the Manhattan district the price is supposed to be higher than in Queens or Brooklyn.

The main aim of this report is to analyze relationships between ingredients in various recipes. Association Rule is a data method for identifying all associations and correlations between attribute values. Association rules are used in data science to discover correlations and co-occurrences between data sets. They are best suited for explaining patterns in data from seemingly unrelated information repositories, such as relational and transactional databases. The use of association rules is sometimes referred to as “association rule usage.”

I decided to explore "House Sales in King County" database. Database consists of a lot of variables which difference each house of sale. We can expect that the most variable is price, location and the square footage. Let's see which factors make the most difference between selling homes in King Country using dimension reduction methods. As the result this project helps to get better understanding of the data and dependencies between variables.

The main aim of this report is to analyze relationships between ingredients in various recipes. Association Rule is a data method for identifying all associations and correlations between attribute values. Association rules are used in data science to discover correlations and co-occurrences between data sets.

Using clustering methods I chose the number of clusters. Clusters were selected in both scenarios: using only locations atributes: longitude and latitude and apart from them: price and type of the rented airbnb. They made it possible to divide the accomodations in New York. In the resulting clusters, I conducted, among other statistics, an analysis of the price in the districts of New York. The main purpose is to examine the price of airbnb in clusters.