Data Sources

Here you’ll find everything about different data sources for data science and machine learning.

Data sources for machine learning can include a wide variety of sources and formats, such as:

  1. Structured data: data that is organized into tables or fields, such as databases, spreadsheets, or CSV files
  2. Unstructured data: data that does not have a pre-defined structure, such as text documents, images, or audio files
  3. Public data: data that is publicly available, such as data from government agencies, research institutions, or non-profit organizations
  4. Private data: data that is owned and controlled by a specific organization or individual, such as customer data, transactional data, or sensor data
  5. External data: data that is external to the organization or system that is using it, such as data from third-party vendors or partners

Using Pandas DataReader to Access Online Data Sources in Python

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Pandas DataReader is a library that allows data scientists to easily read data from a variety of sources into a Pandas DataFrame. This is especially useful for accessing data that resides outside of their local development environment and needs to be accessed via APIs. The Pandas DataReader provides functions for loading data from various online … Read more

Color-Coded Cryptocurrency Price Charts in Python

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Are you intrigued by the fascinating world of cryptocurrency and looking to visually decipher its price trends? Welcome aboard! In this comprehensive tutorial, we will explore creating color-coded line charts using Python and Matplotlib, a powerful tool for effective analysis of changes along a third dimension. The past few years have witnessed a meteoric rise … Read more

Streaming Tweets and Images via the Twitter API in Python

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Twitter is a rich source of data that can be used to understand current and future trends. Because tweets often include hashtags, they can be easily linked to specific contexts such as political discussions or financial instruments. This makes Twitter a valuable tool for collecting and analyzing data. In this article, we’ll demonstrate how to … Read more

Image Classification with Convolutional Neural Networks – Classifying Cats and Dogs in Python

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This tutorial shows how to use Convolutional Neural Networks (CNNs) with Python for image classification. CNNs belong to the field of deep learning, a subarea of machine learning, and have become a cornerstone to many exciting innovations. There are endless applications, from self-driving cars over biometric security to automated tagging in social media. And the … Read more

Customer Churn Prediction – Understanding Models with Feature Permutation Importance using Python

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Customer retention is a prime objective for service companies, and understanding the patterns that lead to customer churn can be the key to maintaining long-lasting client relationships. Businesses incur significant costs when customers discontinue their services, hence it’s vital to identify potential churn risks and take preemptive actions to retain these customers. Machine Learning models … Read more

Classifying Purchase Intention of Online Shoppers with Python

Customer Purchase Intention Prediction Python Machine Learning

Online shopping has become a part of our daily lives, and online stores are continually seeking to improve their sales. One way to achieve this is by using machine learning to predict customers’ purchase intentions. This innovative process can help businesses understand their customers’ behavior and tailor their marketing strategies accordingly. In this article, we … Read more

Geographic Heat Maps with GeoPandas: Visualizing COVID-19 Data in Python

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The spreading of COVID-19 has led to an increased interest in displaying region and country-specific information on geographic heat maps. Geographic heat maps use color shadings to visualize data that includes a spatial component and refers, for example, to countries, cities, towns, mountains, etc. The color shades are defined in a color palette and determined … Read more

Accessing Remote Data Sources via REST APIs in Python

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REST APIs provide straightforward access to remote data sources. Data scientists should learn about REST APIs because APIs (Application Programming Interfaces) are an important way for data scientists to access data from other sources. By using REST APIs, data scientists can access data from a wide range of sources, including databases, web services, and other … Read more