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Reading list

Machine Learning Basics for a Newbie
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

Automate Web Scraping Using Python AutoScraper Library

Akshay
Akshay Last Updated : 22 Oct, 2024
5 min read

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

Introduction

AutoScraper Library

Web Scraping is a method or art to get or scrap data from the internet or websites and storing it locally on your system. Web Scripting is a programmed strategy to acquire a lot of information from sites.

The vast majority of this information is unstructured information in an HTML design which is then changed over into organized information in an accounting page or a data set so it tends to be utilized in different applications. There is a wide range of approaches to perform web scraping to get information from sites. These incorporate utilizing on the web administrations, specific API’s or in any event, making your code for web scraping without any preparation.

AutoScraper Webscraping

 

Numerous enormous sites like Google, Twitter, Facebook, StackOverflow, and so on have API’s that permit you to get to their information in an organized organization. This is the most ideal choice yet different locales don’t permit clients to get to a lot of information in an organized structure or they are essentially not so mechanically progressed. Around there, it’s ideal to utilize Web Scraping to scratch the site for information.

AutoScraper Library Web

 

Web Scrapers can extract all the information on specific destinations or the particular information that a client needs. Preferably, it’s ideal if you indicate the information you need so the web scraper just concentrates that information rapidly. For instance, You should scratch an Amazon page for the sorts of juicers accessible, however, you may just need the information about the models of various juicers and not the client audits.
AutoScraper Library web scraper

 

So when a web scrubber necessities to scratch a website, first it is given the URLs of the necessary locales. At that point, it stacks all the HTML code for those destinations and a further developed scraper may even concentrate all the CSS and Javascript components also. At that point, the scraper acquires the necessary information from this HTML code and yields this information in the organization indicated by the client.
Generally, this is as an Excel bookkeeping page or a CSV record however the information can likewise be saved in different organizations, for example, a JSON document.
AutoScraper Library web scraping applications
  1. Requests
  2. Beautiful Soup 4
  3. lxml
  4. Selenium
  5. Scrapy

 

AutoScraper 

It is a python web scraping library to make web scraping smart, automatic fast, and easy. It is lightweight as well it means it will not impact your PC much. A user can easily use this tool for data scraping because of its easy-to-use interface. To get started, you just need to type few lines of codes and you’ll see the magic.

You just have to provide the URL or HTML content of the web page from where you want to scrap the data furthermore, a rundown of test information that we need to scratch from that page. This information can be text, URL, or any HTML label worth of that page. It learns the scraping rules on its own and returns similar elements.

In this article, we will investigate Autoscraper and perceive how we can utilize it to scratch information from the we.

AutoScraper Library python

Installation

There are 3 ways to install this library in your system.

  • Install from the git repository using pip:
pip install git+https://github.com/alirezamika/autoscraper.git
  • Install using PyPI:
pip install autoscraper
  • Install from source:
python setup.py install


Importing Library

We will just import an auto scraper as it is adequate for web scratching alone. Below is the code for importing:

from autoscraper import AutoScraper


Defining Web Scraping Function

Allow us to begin by characterizing a URL from which will be utilized to bring the information and the necessary information test which is to be brought. Suppose we want to fetch the titles for different articles on Machine Learning on the Analytics Vidhya website. So, we have to pass the URL of the Analytics Vidhya machine learning blog section and the secondly wanted list. The wanted list is a list that is sample data that we want to scrape from that page. For example, here wanted list is a title of any blog on Analytics Vidhya machine learning blog section.

url = 'https://www.analyticsvidhya.com/blog/category/machine-learning/'
wanted_list = ['Confusion Matrix: Detailed intuition and trick to learn']

We can add one or multiple candidates to the wanted list. You can also put URLs to the wanted list to retrieve the URLs.

AV

Initiate the AutoScraper

The next step after initiating the URL and wanted list is to call the AutoScraper function. We aim to use this function to build the scraper model and perform web scraping on that particular page itself.

This can be initiate by using the below code:

scraper = AutoScraper()


Building the Object

This is the final step in web scraping using this particular library. Here er create the object and show the result of web scraping.
Python Code:

from autoscraper import AutoScraper

url = 'https://stackoverflow.com/questions/2081586/web-scraping-with-python'

wanted_list = ["What are metaclasses in Python?"]

scraper = AutoScraper()
result = scraper.build(url, wanted_list)
print(result)

Here in the above image, you can see it returns the title of the blogs on the Analytics Vidhya website under the machine learning section, similarly, we can get the URLs of the blogs by just passing the sample URL in the wanted list we defined above.

Saving the Model

It allows us to save the model that we have to build so that we can reload it whenever required.

To save the model, use below code

scraper.save('blogs')      #Give it a file path

To load the model, use the below code:

scraper.load('blogs')

 

Note: Other than every one of these functionalities auto scraper additionally permits you to characterize proxy IP Addresses with the goal that you can utilize it to get information. We simply need to characterize the proxies and pass it as an argument to the build function as shown below:

proxies = {
    "http": 'http://127.0.0.1:8001',
    "https": 'https://127.0.0.1:8001',
}

result = scraper.build(url, wanted_list, request_args=dict(proxies=proxies))

For more information, check the below link: AutoScraper

 

Conclusion

In this article, we perceived how we can utilize Autoscraper for web scraping by making a basic and simple to utilize the model. We saw various formats in which information can be recovered utilizing Autoscraper. We can likewise save and load the model for utilizing it later which saves time and exertion. Autoscraper is amazing, simple to utilize, and efficient.

Thanks for reading this article and for your patience. Do let me in the comment section about feedback. Share this article, it will give me the motivation to write more blogs for the data science community.

Email id: gakshay1210@gmail.com

Follow me on LinkedIn: LinkedIn

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

Akshay
Akshay

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ILGAR GADIROV
ILGAR GADIROV

Thank you. Very useful information.

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