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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
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
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
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

Scrap Images From E-Commerce Website Using AutoScraper

Mayur
Mayur Last Updated : 04 May, 2021
5 min read
This article was published as a part of the Data Science Blogathon.

Before going ahead, lets we take a brief discussion on Scraping and then AutoScraper:

What is Scraping?

Web scraping is a fundamental technique that is used for extracting useful information such as contact, emails, images, URLs, etc… from the websites. The other form of web scraping is crawling. It is used when we need a huge amount of data that was structured and labeled for industrial fundamentals. The web scraping software may directly access the world wide web by the HTML protocols.

AutoScraper library - webscraping

You know that the new forms of web scraping involve the observation of data feed on web servers, for example, JSON file which is used as a transporter between client and web server.

There are many large websites that Google, Facebook, Amazon, etc.. provide API that allows you to access their data in a structured or labeled format.

Now, we take a brief discussion of the AutoScraper library:

 

What is AutoScraper?

When we talk about scraping, then there is lots of stuff on the website that we want to scrap but writable scripts take lots of time to scrape data and it is a very lengthy process, to overcome this problem a group of python developers develops a library which will scrape the whole data from a website in an easy way. So AutoScraper is the web scraping python library that is used for scraping data from a website in a simple, easy and fast way. It has a user-friendly environment by this scraper can easily interact with this library.

What is AutoScraper library

It uses the URLs and HTML content of the website for scraping reliable information and data.

Point to be noted:  It learns the scraping rules and returns similar elements in a good format.

It is easy to scrap the content from the website which was easy to go through like title, price, name, ratings, etc… Wait a minute! what will we do for images? it is a big question that arises we can give the image during the execution of the program😅. I am finding a way to scrap the images from the websites. Let’s discuss below:

begin AutoScraper library

First, we go for the installation of this library:

Installation of AutoScraper

There is two way to install AutoScraper:

Using pip:-

Write this following code in the command prompt,

pip install autoscraper

or with git repository,

git clone https://github.com/brandonrobertz/autoscrape-py
cd autoscrape-py/
pip install .[all]

Now we import important modules:

Importing Modules

# Importing AutoScraper
from autoscraper import AutoScraper

Here we import AutoScraper class from the library.

Now we feed the URL to the AutoScraper function for further scraping:

URL:- https://www.bookswagon.com/

Here we feeding the E-commerce website URL to the AutoScraper class for extracting or scraping the images of books.

Now, before going ahead we first see the demo scraping od titles and prices of books to have a basic and better understanding of scraping code:

Demo Scraping

Now, we will feed the list of items for scraping, So first of all we have to initialize the  AutoScraper class with its object:

Wanted list:

create a list of elements

# create a list of elements
items = ['Rs.349' , 'The Secret of the Nagas']
AutoScraper library wanted

 

Object Creation:

# create object
scrape = AutoScraper()
# feeding for scraping
final_result = scrape.build(URL,items)
# display result
print(final_result)
AutoScraper library object creation

Time to Scrap Image

Now, you have an idea about the web scraping code that we discussed above, So we use this same method for scraping the images from the website with a few changes. So, we will discuss the method or technique for scraping the images from the data. let’s see below:

Step 1:

In the first step, we have to right-click on the mouse then select the inspect option from the list of  the menu:

step 1

Step 2:

After selecting the inspect option, then one HTML content page opens beside the display, after that will hover over the image of the book, at that time notice in the HTML content page you find the URL of the image. 

Step 2

When you find the URL of the particular image then copy that and we will use this in the items wanted list. This is only the change is required for scraping the images from the website.

Step 3:

Now, we will set that image URL  along with the books that came into our wanted list,

item = ['https://d2g9wbak88g7ch.cloudfront.net/productimages/mainimages/344/9789381626344.jpg','This is Not Your Story']

After creating a list, we do the same process as we do above:

# creating object
scrape = AutoScraper()
# building result
final_result = scrap.build( URL, item )
# display result
print(final_result)
Step 3

Note: Use URL of images for scraping the images from website

So, this is the process for scraping the images from any website.

Endnote

So, here we will discuss image scraping from the website, if you want to scrap images from the website then use this technique. I am very much amazed by using this AutoViz library. I hope you enjoyed this article and thank you for reading this article.😊

You can connect with me on Linkedin:  Profile URL

Also read my other articles: https://www.analyticsvidhya.com/blog/author/mayurbadole2407/

Thank You😎.

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

AutoScraperAutoScraper Libraryblogathon
Mayur
Mayur

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Automate Web Scraping Using Python AutoScraper Library
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