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Google Maps is a powerful tool that provides a vast amount of geospatial data, including locations, addresses, reviews, ratings, and more. Being able to extract this data programmatically can be immensely useful for various applications such as business analysis, research, and data-driven decision-making. In this article, we will explore how to scrape data from Google Maps using Python. Step 1: Install Required Libraries To begin with, we need to install the necessary Python libraries that will facilitate the web scraping process. Open your command prompt or terminal and run the following commands: pip install requests pip install beautifulsoup4 ... Read More
Overview For finding any element Jquery provides the best Syntax for the selector, it gives us a one line code for selecting any element from the HTML document. So to find all the textareas in the HTML we will be using the Jquery selector which will select the textareas and then by using the Jquery CSS(Cascading Styles Sheet) manipulation we will be making a border around the textarea. The border makes a User Interface more attractive. Syntax The Syntax for selecting and styling any element in Jquery is given below $(selector).css(); $(selector) − . For selecting any ... Read More
Web scraping is a powerful technique used to extract data from websites. One popular library for web scraping in Python is BeautifulSoup. BeautifulSoup provides a simple and intuitive way to parse HTML or XML documents and extract the desired information. In this article, we will explore how to scrape all the text from the tag of a web page using BeautifulSoup in Python. Algorithm The following algorithm outlines the steps to scrape all text from the body tag using BeautifulSoup: Import the required libraries: We need to import the requests library to make HTTP requests and the BeautifulSoup ... Read More
Scaling is the process of preprocessing the data in data analysis and ensuring that all the features in a dataset have similar ranges, making them more comparable and reducing the impact of different scales on machine learning algorithms. We can scale Pandas dataframe columns using methods like Min-max scaling, standardization, Robust scaling, and log transformation. In this article we will dive into the process of scaling pandas dataframe scaling using various methods. Why Scaling is Important? Some features in the data may have larger values which can dominate when the analysis or model training is done. Scaling ensures ... Read More
Overview An array can be a collection of anything but should be of the same data type. We can store any digit and text of the array, so to filter the data from an array using the AngularJs can be achieved by some key value attributes pair. The filter rule plays a key role to create this feature, there are also some AngularJs attributes that play a major role to build this feature these are: ng−model and ng−repeat. The ng−model attribute defines the input type text search box to connect with the ordered list and filter it out an array. ... Read More
In this article we will get to know about wxPython and we will create a program to make a pop-up item menu. Pop-up menus are graphical user interfaces (GUIs) which allow us to show some specific items and options. Here in this tutorial, we will learn how to create and implement popup menus in wxPython. It is a GUI toolkit for python language which provides us with a set of bindings and allows developers to create cross platform applications having native interface. Installing wxPython Using pip Command pip install wxPython in case it is not getting installed type ... Read More
Density Estimation is an essential part of both machine learning and statistics. It means getting the probability density function (PDF) of a group. It is necessary for many things, like finding outliers, putting things into groups, making models, and finding problems. Based on deep learning, this study looks at all the ways to measure old and new density. Traditional Density Estimation Methods Histograms Whether you need to know in a hurry whether your data collection is complete, a histogram is the way to go. They take the data range and chunk it up into categories called " bins " to determine ... Read More
Introduction Python is a flexible and strong programming language that gives programmers a variety of tools and environments to create and run their code in. Two popular Python programming environments, Jupyter Notebook and Python IDLE, each have their unique advantages and capabilities. The definitions, features, processes, and use cases of Jupyter Notebook and Python IDLE will be thoroughly compared in this paper. By the conclusion, you will have a thorough understanding of different environments, allowing you to choose the one that best meets your coding requirements. Definition Jupyter Notebook Users can create and share interactive documents known as notebooks using ... Read More
Introduction The k-Nearest Neighbor is a powerful and straightforward technique to solve problems related to classification and regression. It makes the prediction on the input samples and checks for how similar the samples are to one another. In this present article we will explain k-NN technique and implementation of python, with two different types of approaches . To ensure a clear comprehension of this well-known technique, we will offer step-by-step explanations replete with executable code and results. k-Nearest Neighbor Algorithm A supervised machine learning ( ML) technique used for classification and regression problems is called the k-Nearest ... Read More
Introduction Clustering, which groups similar bits of data based on shared characteristics, is a prominent technique in unsupervised machine learning. K-Means clustering is a popular clustering algorithm. Data is divided into K clusters using the iterative K-Means technique, where K is a predetermined number. The process minimizes the sum of squared distances between the cluster centroids and the data points. In this post, we will look at how to use the Python Scikit-Learn package to conduct K-Means clustering on handwritten digits data. Definition A straightforward and efficient unsupervised learning approach called K-Means clustering seeks to separate a dataset into K ... Read More