• The Compliance Risks of Synthetic Data Generation

    What Is Synthetic Data? Synthetic data is machine-generated data based on real-world data. It requires building a machine learning (ML) model to capture the patterns in the original, real data before generating new synthetic data based on these patterns. The generated data accurately represents the original data’s statistical distributions, patterns, and properties.  Synthetic data is useful for applications facing privacy concerns – it is not regarded as personally identifiable information (PII), because it is not directly traceable to real individuals. Thus, organizations can freely share and use synthetic data with minimal technical and administrative controls. This process requires a high level of automation, relying on fewer human resources and skills…

  • Using Neo4j to visualize medicines’ class and their ingredients

    In my first post about Neo4j, I explored Python modules and their dependencies. Now in this post, I am discussing how you can find the relationship between medicines, their ingredients, and their classification. But why did I choose this dataset in the first place so here is the background: Background: Currently, there is a shortage of Panadol in Pakistan, a very famous brand to cure headaches and bodily pain because the company has stopped producing it as they don’t find it viable anymore. Existing Panadol strips which used to cost Rs. 30 are even available for Rs.80 because of the lack of awareness and amazing marketing by the company. Many…

  • Creating an e-commerce bot to buy online items with ScrapingBee and Python

    I wrote about ScrapingBee a couple of years ago where I gave a brief intro about the service. ScrapingBee is a cloud-based scraping service that provides both headless and lightweight typical HTTP request-based scraping services. Recently I discovered that they are providing some cool features which other online services are not providing as such. What are those features? I thought to explore and explain them with a real use case. I used Python language to automate the Daraz group’s shopping website, a famous e-commerce website service in Asian countries like Pakistan, Nepal, Bangladesh, and Sri Lanka.  I am automating DarazPK since I am in Pakistan. You can view the demo…

  • Getting started with Rocksdb and Python

    In this post, I am going to discuss RocksDB. RocksDB is an embeddable persistent key-value store system developed by Facebook. It was originally forked from LevelDB which was created by Google. According to Wikipedia: RocksDB is a high performance embedded database for key-value data. It is a fork of Google’s LevelDB optimized to exploit many CPU cores, and make efficient use of fast storage, such as solid-state drives (SSD), for input/output (I/O) bound workloads. It is based on a log-structured merge-tree (LSM tree) data structure. It is written in C++ and provides official language bindings for C++, C, and Java; alongside many third-party language bindings. RocksDB has particularly been optimized…

  • Deploying Laravel Applications in AWS

    Image Source What is Laravel?  Laravel is a free and open source PHP framework that provides a set of tools and resources for building PHP applications. It is known for its ease of use, provides extensive features and a complete ecosystem of packages and extensions. For example, it provides useful functions like token based authentication, unit testing, and file upload out of the box. Laravel provides powerful database tools including an Object Relational Mapper (ORM) called Eloquent and built-in mechanisms for database migration and seed generation. The command-line tool Artisan allows developers to bootstrap new models, multiple controllers, and other pre-built application components. Why Choose AWS For Your Web Application? …

  • 6 Cloud Computing Concerns as We Enter 2022

    Image Source Guest Post Cloud computing will continue to grow in 2022. Gartner predicts that global end-user spend on the cloud will grow by 20% in 2022 to reach $600 billion by 2023. The largest spending category is Software-as-a-Service (SaaS), followed by Platform-as-a (PaaS) and Infrastructure as a Service (IaaS). After reading this article, you will understand the state of cloud computing today and learn how to identify risk mitigation strategies required for cloud-based applications, especially mission-critical apps you migrate to the cloud. 1. Security Cloud computing services store data in a way that you cannot always know its exact physical location. Therefore, data security is a major concern for…

  • Introduction to technical Analysis in Python using TA-Lib

    In this tutorial, I am going to discuss TA-Lib, a technical analysis library for Python apps. Before I move on and discuss how you can do technical analysis in Python, allow me to discuss what technical analysis is and how it helps to take a decision whether you buy an asset, sell or hold it. What is Technical Analysis From Investopedia: Technical analysis is a trading discipline employed to evaluate investments and identify trading opportunities by analyzing statistical trends gathered from trading activity, such as price movement and volume. In short, it is the study of past and current data and trying to figure out what’s going to be in…

  • Getting started with Celery and Python

    In this post, I am going to talk about Celery, what it is, and how it is used. What is Celery From the official website: Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. Wikipedia says: Celery is an open source asynchronous task queue or job queue which is based on distributed message passing. While it supports scheduling, its focus is on operations in real time. In short, Celery is good to take care of asynchronous or long-running tasks that could be delayed and do not require real-time interaction. It can also…

  • Develop Ali Express Scraper in Python with Scraper API

    This is another post in ScrapeTheFamous, in which I will be parsing some famous websites and will discuss my development process. The posts will be using Scraper API for parsing purposes which makes me free from all worries about blocking and rendering dynamic sites since Scraper API takes care of everything. In this post, we are going to scrape AliExpress. AliExpress is a Chinese B2C portal to buy stuff. The script I am going to make consists of two parts, or I say, two functions: fetch and parse. The fetch will accept a category and return all links of individual items and parse will parse an individual entry and returns a few data points in…

  • Develop Google scraper in Python with Scraper API

    This is another post in ScrapeTheFamous, in which I will be parsing some famous websites and will discuss my development process. The posts will be using Scraper API for parsing purposes which makes me free from all worries about blocking and rendering dynamic sites since Scraper API takes care of everything. So this post is about scraping Google search results, the script will accept a keyword and would return results across multiple pages. The data will be stored in a text file in JSON format. The code that is parsing the result is pretty straightforward and given below: def google_scraper(query, start=0): records = [] try: URL_TO_SCRAPE = "http://www.google.com/search?q=" + query.replace(' ', '+') +…