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Showing posts with the label data science

How to Scrape Soundcloud data using Selenium? (from scratch)

Photo by  ClĂ©ment H  on  Unsplash Hello there, if you are new to web scraping or want to learn how you can scrape data from websites using Selenium then this article is for you. In this article we are going to scrape data from SoundCloud but you can use this technique to scrape data from other websites also. Before we move further and jump into coding, let’s take a look at what is web scraping. If you already hold knowledge about scraping you can jump to the coding section. Web Scrapping (also termed Screen Scraping, Web Data Extraction, Web Harvesting etc.) is a technique employed to extract large amounts of data from websites whereby the data is extracted and saved to a local file in your computer or to a database in table (spreadsheet) format.  As mentioned in the topic of the article, we are going to use Selenium for scraping the data. In case if you don’t know what seleni

Precision and Recall | Precision and Recall in machine learning

Precision and recall are the two terms which confused me a lot in my machine learning path. These terms sound easy but they are not as easy as they sound. And the high-level definition provided in most of the blogs are way out of my understanding, actually I never find those definitions easy to understand. So, I tried to find some other way to understand these terms with real world example. Photo by Karsten Winegeart on Unsplash Ok, enough of talking. Let’s start with where to use precision and recall instead of accuracy. So, take an example where you got an imbalanced data for skin cancer and you were asked to create a model to detect skin cancer. You created a model which detects skin cancer with very high accuracy say 98%. But, is it the perfect model? Or we

Understanding mean Average Precision for Object Detection (with Python Code)

Photo by  Avel Chuklanov  on  Unsplash If you ever worked on object detection problem where you need to predict the bounding box coordinates of the objects, you may have come across the term mAP (mean average precision). mAP is a metric used for evaluating object detectors. As the name suggest it is the average of the AP. To understand mAP , first we need to understand what is precision, recall and IoU(Intersection over union). Almost everyone is familiar with first two terms, in case you don’t know these terms I am here to help you. Precision and Recall Precision: It tells us how accurate is our predictions or proportion of data points that our model says relevant are actually relevant. Formula for precision Recall: It is ability of a model to find all the data points of inte