Introduction Natural language processing is the processing of languages used in the system that exists in an nltk library to process by transforming text dataset to new analyzable dataset for insights. If an NLP processing is done on another language, you have to add that language to the existing NLP library…

A Step-By-Step “NO-CODE” Approach To Web Scrape a News archives Sites with Several Pages and Multiple News links Per Page. In this article, I’ll teach you how to use the no-code “PARSEHUB” app to scrape multi-page news sites with multiple news links. For example, we’ll use the URL http://www.premiumtimesng.com/tag/accident for these “HOW-TO” tips. On each accident news page, we would extract the Title of the story, the owner of the…

This article is based on a classification of online games. Using Parsehub’s online web scraping tool, a Minecraft category review was scraped from the Commonsense Media website (https://www.commonsensemedia.org/game-reviews). The game review was labeled as having been written by a teen or kid. The Web scraped data was then cleaned utilizing…

Learning How To Use (MS-SQL) DDL Statements to create SQL OBJECTS(Tables, Constraints, Triggers, Procedures). In this article, we will look at how to create SQL Objects such as Tables, Procedures, Triggers, Indexes, and Security Objects(), Also, Some DML Statements would also be employed to test our created SQL objects. For better understanding, we would be building a Shopping_dat database from scratch using Strictly SQL…

End-to-End Light-Weight Machine learning model deployment (Using Statistics, Python, Streamlit, Heroku, and Github) Introduction This article is based on a Python@Home challenge organized by Technidus. The challenge is based on a real-life Data Science challenge and follows an entire AI modeling workflow from the start to the end. The Scenario : A real estate property Aggregation company with Property Aggregation websites needs a rent budget estimator feature…

The main Purpose of Data Cleaning is to identify and remove errors & duplicate data, in order to create a reliable dataset. This improves the quality of the training data for analytics and enables accurate decision-making. Data cleaning and data preparation is a critical first step in any AI/machine learning…