My Power BI Portfolio

Here you have some of my PowerBI portfolio presented in PowerBI. Sounds cool, doesn’t it?

May 1, 2021 Read
Bike Share Demand Prediction in San Francisco

Introduction I am going to predict the number of borrowed bikes in a given day and station by using 4 available data set below. station.csv - Contains data that represents a station where users can pick up or return bikes. status.csv - Data about the number of bikes and docks available for a given station and minute. trips.csv - Data about individual bike trips. weather.csv - Data about the weather on a specific day for specific station id.

January 5, 2021 Read
Insights to Crimes in Czechia by using Tableau

Here is the story that I prepared by using Czech Statistical Office’s crime data. I think there is no need to talk much, you may have a look the story of the crime data below.

December 28, 2020 Read
Insights to Food Prices in Czech Republic by using Tableau

It’s beeen only three weeks to move to Prague, however, I’ve become very familiar with the prices in the supermarket. In this study, I’ll give insights of the food prices in Czech Republic by using one of the most efficient softwares for analytics, Tableau. Here is the summary of the study. Obtaining the price data from czech statistic office. Examining the change in the prices for selected foods through out the years 2014-2019 Creating a dashboard for you interact with the graphs and charts.

December 26, 2020 Read
Data Analysis of House Sales Prices and Venues in Prague with Python

Introduction Prague is one of the most popular cities in Europe with many outstanding views and historic, exciting structures. My journey to Prague has started with the hiring of my wife by a global company there. In this study, I give a brief insight about the population, house prices and types by comparing municipal parts of the city for anyone to buy a property in Prague or interested in the real estate industry.

November 17, 2020 Read
Forecasting of Gasoline MED-FOB Price Based on Historical Data with Python

Petroleum refineries are large, capital-intensive, continuous-flow manufacturing facilities. They transform crude oils into finished, refined products by separating crude oils into different fractions and then processing these fractions into finished products, through a sequence of physical and chemical conversions. Production planning and process scheduling is one of the most important parts of refineries for maximizing the profit of production. Although there are many inputs to optimize this problem, price of blend crude oil (raw material), product price to raw material price ratio, and crack margin ( product price minus raw material price) are critically important since very small change in these variable can lead to very different scenarios.

July 8, 2020 Read