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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
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Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
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Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Scraping Data Using Octoparse for Product Assessment

Shanthababu Pandian
Shanthababu Pandian Last Updated : 22 Jun, 2023
8 min read

In today’s data-driven world, it is crucial to have access to reliable and relevant data for informed decision-making. Often, data from external sources is obtained through processes like pulling or pushing from data providers and subsequently stored in a data lake. This marks the beginning of a data preparation journey where various techniques are applied to clean, transform, and apply business rules to the data. Ultimately, this prepared data serves as the foundation for Business Intelligence (BI) or AI applications, tailored to meet individual business requirements. Join me as we dive into the world of data scraping with Octoparse and discover its potential in enhancing data-driven insights.

This article was published as a part of the Data Science Blogathon.

Table of contents

Web Scrapping and Analytics

Yes! In some cases, we have e to grab the data from an external source using Web Scraping techniques and do all data torturing on top of the data to find the insight of the data with techniques.

Same time we do not forget to use to find the relationship and correlation between features and expand the other opportunities to explore further by applying mathematics, statistics, and visualisation techniques, on top of selecting and using machine learning algorithms and finding the prediction/classification/clustering to improve the business opportunities and prospects, this is a tremendous journey.

Focusing on excellent data collection from the right resource is the critical success of a data platform project. I hope you know that. In this article, let’s try to understand the process of gaining data using scraping techniques – zero code.

Before getting into this, I will try to understand a few things better.

Web Scrapping and Analytics|Octoparse 

Data Providers

Data Providers|Octoparse 

As I mentioned earlier, the Data Sources for DS/DA could be in from any data source. Here, our focus is on Web-Scraping processes.

What is Web-Scraping and Why?

Web-Scraping is the process of extracting data in diverse volumes in a specific format from a website(s) in the form of slice and dice for Data Analytics and Data Science standpoint and file formats depending on the business requirements. It would .csv, JSON, .xlsx,.xml, etc.. Sometimes we can store the data directly into the database.

What is Web-Scraping and Why?

Why Web-Scraping?

Web-Scraping is critical to the process; it allows quick and economical extraction of data from different sources, followed by diverse data processing techniques to gather the insights directed to understand the business better and keep track of the brand and reputation of a company to align with legal limits.

Web Scraping Process

RequestVsResponse

The first step is to request the target website(s) for the specific contents of a particular URL, which returns the data in a specific format mentioned in the programming language (or) script.

Parsing&Extraction

As we know, Parsing is usually applied to programming languages (Java..Net, Python, etc.). It is the structured process of taking the code in the form of text and producing a structured output in understandable ways.

Data-Downloading

The last part of scrapping is where you can download and save the data in CSV, JSON format or a database. We can use this file as input for Data Analytics and Data Science perspective.

There are multiple Web Scraping tools/software available in the market, and let’s look at a few of them.

In the market, many Web-Scraping tools are available, and let’s review a few of them.

Data-Downloading

ProWebScraper

ProWebScraper is one of the most powerful web-scraping tools and is web-based. It used to materialise with its classical customer service and cost-effectiveness. It helps us to extract data from website(s). and It’s designed to make the scraping process a completely uncomplicated exercise. Yes, it requires no coding, just pointing and clicking the required item, and the output will extract into our target dataset.

Features

  • Completely effortless exercise
  • It can be used by anyone can who knows how to browse
  • You can extract the desired data in a few seconds with simple clicks.
  • It can scrape Texts, Table data, Links, Images, Numbers and Key-Value Pairs.
  • It can scrape multiple pages.
  • It can be scheduled based on the demand (Hourly, Daily, Weekly, etc.)
  • Highly scalable, it can run multiple scrapers simultaneously and thousands of pages.

Let’s focus on Octoparse,

The web-Data Extraction tool, Octoparse, stands out from other devices in the market. You can extract the required data without coding, scrape data with modern visual design, and automatically scrapes the data from the website(s) along with the SaaS Web-Data platform feature.

Octoparse provides ready-to-use Scraping templates for different purposes, including Amazon, eBay, Twitter, Instagram, Facebook, BestBuy and many more. It lets us tailor the scraper according to our requirements specific.

Octoparse

Compared with other tools available in the market, it is beneficial at the organisational level with massive Web- Scraping demands. We can use this for multiple industries like e-commerce, travel, investment, social, crypto-currency, marketing, real estate etc.

Features

  • Both categories could find it easy to use this to extract information from websites.
  • ZERO code experience is fantastic.
  • Indeed, it makes life easier and faster to get data from websites without code and with simple configurations.
  • It can scrape the data from Text, Table, Web-Links, Listing-pages and images.
  • It can download the data in CSV and Excel formats from multiple pages.
  • It can be scheduled based on the demand (Hourly, Daily, Weekly, etc.)
  • Excellent API integration feature, which delivers the data automatically to our systems.

Now time to Scrape eBay product information using Octoparse.

Getting product information from eBay, Let’s open the eBay and select/search for a product, and copy the URL

Octoparse 2

Before starting your journey, you should download Octoparse version 8.5.2 for this demo purpose (https://www.octoparse.com/download/windows)

Octoparse 3

In a few steps, we were able to complete the entire process.

  • Open the target webpage
  • Creating a workflow
  • Scrapping the content from the specified web pages.
  • Customizing and validating the data using review future
  • Extract the data using workflow
  • Scheduling

Open Target Webpage

Let’s login Octoparse, paste the URL and hit the start button; Octoparse starts auto-detect and pulls the details for you in a separate window.

Creating Workflow and New-Task

Wait until the search reaches 100% so that you will get data for your needs.

Creating Workflow and New-Task

During the detection, Octoparse will select the critical elements for your convenience and save our time.

After your verification on the page, click on Create Workflow.

Creating Workflow and New-Task
Creating Workflow and New-Task

Note: To remove the cookies, please turn off the browser tag.

Scrapping the Content from the Identified Web-page

Creating Workflow and New-Task

Once we confirm the detection, the Workflow template is ready for configurations and data preview at the bottom. There you can configure the column as convenient (Copy, Delete, Customize the column, etc.,)

Creating Workflow and New-Task 6

Customizing and Validating the Data using Review Future

You can add your custom field(s) in the Data preview window, import and export the data, and remove duplicates.

Customizing and Validating the Data using Review Future

You can configure the list of columns as we require once done. you can preview the selected individual line item by clicking on the right side of the pane

Octoparse 

Extract the Data using Workflow

On the Workflow window, based on your hit on each navigation, we could move around the web browser. – Go to the web page, Scroll Page, Loop Item, Extract Data, and you can add new steps.

Extract the Data using Workflow| Octoparse 

We can configure time out, file format in JSON or NOT, Before and After the action is performed, and how often the action should perform. After the required configurations have been done, we could act and extract the data.

Extract the Data using Workflow 2| Octoparse 

Save Configuration, and Run the Workflow

Save Configuration, and Run the Workflow| Octoparse 

Schedule-task

You can run it on your device or in the cloud.

Schedule-task| Octoparse 

Data Extraction – Process starts

Data Extraction – process starts| Octoparse 
Data Extraction – process starts 2| Octoparse 

Data ready to Export

Data ready to Export| Octoparse 

Chose the Data Format for Further Usage

Chose the Data Format for Further Usage| Octoparse 

Saving the Extracted Data

Saving the Extracted Data| Octoparse 

Extracted Data is Ready in the Specified-format

Extracted data is ready in the specified-format| Octoparse 

Data is ready for further usage either in Data Analytics and Data Science

Data Analytics and Data Science| Octoparse 

What is Next! Yes, no doubt about that, have to load in Jupiter notebook and start using the EDA process extensively.

Conclusion

So far, we have explored various aspects, and I am sure that you could be able to understand  Data sources, Data processing, Data Science/ML lifecycle, What is Web-Scraping, the process involved in it, tools in the market, and its key features, along with very detailed steps to extract the product data from eBay using Octoparse, Here the quick summary of this article and take-away, 

  • Importance of Data Source
  • Data Science Lifecycle
  • What is Web Scrapping and Why
  • The process involved in Web Scrapping
  • Top Web Scraping tools and their overview
  • Octoparse Use case – Data Extraction from eBay
  • Data Extraction using Octoparse – detailed steps (Zero Code)

I have enjoyed this web-scraping tool and am impressed with its features; you can try and want it to extract free data for your Data Science & Analytics practice projects perspective.

Frequently Asked Questions

Q1. What is Octoparse used for?

A. Octoparse is a web scraping tool used for extracting data from websites. It allows users to automate the extraction process, collect data from various sources, and save it in structured formats for analysis.

Q2. Is Octoparse any good?

A. Octoparse is a reliable web scraping tool offering a user-friendly interface, robust features, and good customer support. It is widely used and recommended by users for its effectiveness in data extraction.

Q3. Can I use Octoparse for free?

A. Octoparse offers both free and paid versions. The free version provides limited functionality and is suitable for basic scraping needs. The paid version, Octoparse Professional, offers advanced features and is available as a subscription-based service.

Q4. Is Octoparse paid?

A. Octoparse offers a paid version, Octoparse Professional, which provides additional features and capabilities beyond what is available in the free version. The paid version provides more flexibility and power for complex scraping tasks.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

blogathondata analyticsdata scienceOctoparseweb scraping
Shanthababu Pandian
Shanthababu Pandian

Shanthababu Pandian has over 23 years of IT experience, specializing in data architecting, engineering, analytics, DQ&G, data science, ML, and Gen AI. He holds a BE in electronics and communication engineering and three Master’s degrees (M.Tech, MBA, M.S.) from a prestigious Indian university. He has completed postgraduate programs in AIML from the University of Texas and data science from IIT Guwahati. He is a director of data and AI in London, UK, leading data-driven transformation programs focusing on team building and nurturing AIML and Gen AI. He helps global clients achieve business value through scalable data engineering and AI technologies. He is also a national and international speaker, author, technical reviewer, and blogger.

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