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Accessing datasets in a structured form through an API can often simplify the life of a data analyst - especially if the same data series are used repeatedly. Unfortunately, many public data sources such as the Federal Statistical Office (BFS) do not provide data access through an API ( STAT-TAB makes life a bit easier, but is not fully automated). While opendata.swiss offers a great way to explore available public...
For the past few years, tasks involving text and speech processing have become really hot-trendy. Among the various researches belonging to the fields of Natural Language Processing and Machine Learning, sentiment analysis ranks really high. Sentiment analysis allows identifying and getting subjective information from the source data using data analysis and visualization, ML models for classification, text mining and analysis. This helps...
Introduction Nowadays PostgreSQL is probably one of the most powerful relational databases among the open-source solutions. Its functional capacities are no worse than Oracle’s and definitely way ahead of the MySQL. So if you are working on apps using Python, someday you will face the need of working with databases. Luckily, Python has quite a wide amount of packages that provide an easy way of connecting and using databases. In...
The way other people think about one or another product or service has a big impact on our everyday process of making decisions. Earlier, people relied on the opinion of their friends, relatives, or products and services reposts, but the era of the Internet has made significant changes. Today opinions are collected from different people around the world via reviewing e-commerce sites as well as blogs and social nets. To transform gathered...
Among the variety of open source relational databases, PostgreSQL is probably one of the most popular due to its functional capacities. That is why it is frequently used among all the areas of work where databases are involved. In this article, we will go through connection and usage of PostgreSQL in R. R is an open source language for statistical and graphics data analysis providing scientists, statisticians, and academics powerful tools...
What is Exploratory Data Analysis Exploratory data analysis (EDA) is a powerful tool for a comprehensive study of the available information providing answers to basic data analysis questions. What distinguishes it from traditional analysis based on testing a priori hypothesis is that EDA makes it possible to detect — by using various methods — all potential systematic correlations in the...
Introduction Exploratory data analysis (EDA) is an approach to data analysis to summarize the main characteristics of data. It can be performed using various methods, among which data visualization takes a great place. The idea of EDA is to recognize what information can data give us beyond the formal modeling or hypothesis testing task. In other words, if initially we don’t have at all or there are not enough priori ideas about...
In the modern world, the information flow which befalls on a person is daunting. This led to a rather abrupt change in the basic principles of data perception. Therefore visualization is becoming the main tool for presenting information. With the help of visualization, information is presented to the audience in a more accessible, clear, visual form. Properly chosen method of visualization can make it possible to structure large data arrays,...
The more carefully you process the data and go into details, the more valuable information you can get for your benefit. Data visualization is an efficient and handy tool for gaining insights from data. Moreover, you can make the data far more understandable, colorful and pleasant with the help of visualization tools. As data is changing every second, it is an urgent task to investigate it carefully and get the insights as fast as...
   Random Forest is a powerful ensemble learning method that can be applied to various prediction tasks, in particular classification and regression. The method uses an ensemble of decision trees as a basis and therefore has all advantages of decision trees, such as high accuracy, easy usage, and no necessity of scaling data. Moreover, it also has a very important additional benefit, namely perseverance to overfitting (unlike...
In this Jupyter Notebook we will retrieve data from the European Central Bank (ECB). The ECB publishes through the European Open Data Portal, which we discussed in the previous tutorial . Before diving into the code, please take a quick look at the following websites, to get a feel for what we will be dealing with. EU portal: https://data.europa.eu/euodp/en/data/publisher/ecb ECB SDMX 2.1 RESTful web...
   A common and very challenging problem in machine learning is overfitting, and it comes in many different appearances. It is one of the major aspects of training the model. Overfitting occurs when the model is capturing too much noise in the training data set which leads to bad predication accuracy when applying the model to new data. One of the ways to avoid overfitting is regularization technique. In this tutorial, we...