This is a step by step web scrapping tutorials from scratch
👉Learn how to scrape data from any website for data analysis


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Getting data from webpage and transform it to structure format for data analysis is one of the skills that you need to have 🐍🐍

This is a step by step tutorials of scraping and transforming table data from website

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Harry up 😁 programmers is increasing linearly.



According to the search results, programmers are expected to be 30 million in 2024 in the world workforce.



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Fraud Detection for E-commerce and Bank





This project leverages machine learning to detect fraudulent transactions in e-commerce and banking, aiding in proactive security and risk management. The goal is to provide a robust fraud detection pipeline with explainability, deployment, and dashboard visualization for actionable insights.

As a data scientist 70-80 percent of your time spending on data cleansing. If you have given data which contains special characters and you may need to avoid those special characters, what methods do you use to avoid it?

In this topic modeling project-based tutorial, I have gone through the following steps:

1. Loads the documents(Generating sample documents)
2. Preprocesses the text by removing stop words and stemming words.
3. Creates a TF-IDF vector representation of the documents.
4. Performs LDA topic modeling with the specified number of topics.
5. Extracts the document-topic weight matrix.
6. Prepares the data for CSV format, including document IDs and topic weights.
7. Saves the results to the specified CSV file.

Managing class attributes with property decorator is clear and concise than using traditional approach with getter and setter methods. Here is a step by step guide

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🤔 Think of yourself as a Data Scientist and given data to you to clean it. The data might contains unnecessary characters(i.e #*()/?@&$%\;[]{}) and you're required to remove those special characters from your data.

Therefore, in this tutorial, you will be learning about how to remove special characters or punctuations from any data using three different methods.

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Learn about how to scrape data, identify and extract internal and external links, detect backlinks from websites through web scraping using Python and get help to obtain data, identify internal linking opportunities, and also help to improve SEO.

1. Scraping price information from ebay website with beautiful soup:

2. Detecting and scraping backlinks from any website:

3. Scrape internal and external links from any website:


4. Scraping table data from webpages




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Python Programming for beginners Roadmap

Basic Python Programming:

Data Structures with Projects full tutorial for beginners


OOP in Python - beginners Crash Course

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Mathematics for Machine Learning RoadMap

🔗 Link to Linear Regression
🔗 Link to Linear Algebra
🔗 Link to Probability Distribution
🔗 Link to Telegram Group

INTRODUCTION TO PROBABILITY DISTRIBUTION FOR MACHINE LEARNING WITH PYTHON

1. What is a random variable?
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2. Types of a random variable
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3. Calculating probability using probability mass function
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4. Calculating probability over a range
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5. Calculating Probability using the cumulative distribution function
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6. Calculating probability of continuous variable using density function and cumulative distribution function
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Creating and parsing XML documents in Python is a valuable skill for managing and exchanging structured data. In this tutorial, I'll cover the basics of creating XML documents and parsing them using Python's built-in XML module.

If you are facing challenges of parsing and transforming nested XML document into a user friendly pandas DataFrame, this tutorial is for you. Please 🙏 Like, Share and comment any words you feel about this tutorial. Thanks for your support.

Creating and parsing XML documents in Python is a valuable skill for managing and exchanging structured data. In this tutorial, I'll cover the basics of creating XML documents and parsing them using Python's built-in XML module.

What is Pandas?

Pandas is an open source library, providing high-performance, easy-to-use data structures and data analysis tools for Python.

The DataFrame is one of Pandas' most important data structures. It's basically a way to store tabular data where you can label the rows and the columns. One way to build a DataFrame is from a dictionary and also importing from CSV(comma-separated value).

Here are the most common pandas functions for data analysis

Harry up 😁 programmers is increasing linearly.



According to the search results, programmers are expected to be 30 million in 2024 in the world workforce.



This is amazing for newbies.



Data source: bardai

Visualization is done by



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This is a Python for data science, machine learning or Artificial Intelligence tutorial for beginners. In this tutorial you will have a solid understanding of the following basic Python topics:
Chapters:
0:00 Introduction to Python programming - Python basics
35:33 Data object types  and Type conversion
48:17 Operators and Expressions
1:14:15 Exception Handling
1:34:36 String Methods for Manipulating String Data
2:13:25 Functions
2:28:38 Function Scope
2:38:38 Function Arguments
2:47:54 Conditional Statements and Loops
3:14:41 Essential Built-in Modules
3:26:26 Develop a Simple Game Program



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Project Idea: Building a spam classifier

Introduction

Spam detection is one of the major applications of Machine Learning in the interwebs today. Pretty much all of the major email service providers have spam detection systems built in and automatically classify such mail as 'Junk Mail'.

In this mission we will be using the Naive Bayes algorithm to create a model that can classify dataset SMS messages as spam or not spam, based on the training we give to the model. It is important to have some level of intuition as to what a spammy text message might look like.
What are spammy messages?

Usually they have words like 'free', 'win', 'winner', 'cash', 'prize', or similar words in them, as these texts are designed to catch your eye and tempt you to open them. Also, spam messages tend to have words written in all capitals and also tend to use a lot of exclamation marks. To the recipient, it is usually pretty straightforward to identify a spam text and our objective here is to train a model to do that for us!

Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam' and nothing else. Also, this is a supervised learning problem, as we know what are trying to predict. We will be feeding a labelled dataset into the model, that it can learn from, to make future predictions.