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Showing posts from August, 2024

ADABOOST

AdaBoost This blog post will provide you with a comprehensive overview of Adaboost, exploring the theory behind this probabilistic algorithm and demonstrating its implementation using Python libraries. Dive in to uncover the advantages and disadvantages of neural network, as well as its real-world applications across various domains. With that, enjoy your journey in QDO! What is  Adaboost AdaBoost (Adaptive Boosting) is an ensemble learning technique that combines multiple weak classifiers (often decision trees) to create a strong classifier. It works by training the weak classifiers sequentially, giving more weight to misclassified instances at each step so that subsequent classifiers focus more on the harder cases. The final prediction is made by combining the weighted votes of all weak classifiers. AdaBoost is effective at reducing bias and variance, and it’s particularly good for binary classification problems. However, it can be sensitive to noisy data and outliers. Concepts o...
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WEB SCRAPPING

Web Scrapping Figure 1: Web scrapping This blogpost will brief you the concept of Web Scrapping, a technique that assist in gathering information online in the nick of time. After reading this blogpost, you will learn the concepts of web scrapping , its advantages and disadvantages that comes with it as well as the implementation of web scrapping in both python and real life by companies to maintain their operation. What is  Web Scrapping Web scraping is the automated process of extracting data from websites. It involves using software or scripts to navigate through web pages, retrieve specific information, and store it for analysis or other purposes. This technique is often used to gather large amounts of data quickly and efficiently from publicly accessible web pages, such as product prices, user reviews, or social media content. Concept of  Web Scrapping The concept of web scraping revolves around mimicking the behavior of a user browsing the web but in an automated manner....
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SENTIMENT ANALYSIS

Sentiment Analysis Figure 1: Sentiment Analysis What is Sentiment Analysis Sentiment Analysis, otherwise called Opinion Mining, is a part of Natural Language Processing that deals with the identification and extraction of subjective information from any given text. It can be considered the computational treatment of opinions, sentiment, and subjectivity of text. Emotion may reside in one sentence or the whole document. It classifies opinions expressed in text into different sentiment categories, normally as positive, negative, or neutral. This helps businesses, researchers, and developers to be informed of the pulse of public sentiment, feel the pulse of their customers, and comprehend the market trends. Concept of Sentiment Analysis Sentiment Analysis assumes that words are associated with human emotions.  It undertakes the analysis of the choice of  words phrases syntax  to derive inferences regarding the emotional context and sentiment of the author.  Majorly, var...
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K-NEAREST NEIGHBOUR (KNN)

  K-NEAREST NEIGHBOUR (KNN) Figure 1: KNN This blogpost will brief you the concept of K-nearest neighbor (KNN) , a machine learning model capable of both descriptive and predictive modelling. After reading this blogpost, you will learn the parameters of KNN that you can tune while using it, its advantages and disadvantages that comes with it as well as the implementation of KNN machine learning model in both python and real life by companies to tackle daily tasks. What is KNN The nearest neighbor (NN) algorithm is one of the simplest and most intuitive algorithms in the field of machine learning and can also be referred to by the acronym KNN (k-Nearest Neighbors). KNN is applied to classification and regression. In essence, the KNN algorithm classifies or predicts the value of a new data point by considering the "k" closest data points in the feature space.  In the case of classification, KNN assigns the new data point to the most frequent class among its k nearest neighbors ...
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