Web scraping, also known as web harvesting or web data extraction, is the process of automatically extracting information from websites. It involves fetching web pages, parsing HTML or other structured data, and then extracting specific data elements for further analysis or storage. This technique allows you to turn unstructured web data into structured and actionable information.

There are several reasons why web scraping is a valuable skill:

**Data Collection**: Web scraping allows you to collect data from websites that don't offer APIs or downloadable datasets.**Real-time Information**: You can access and update data in real-time, making it ideal for tracking stock prices, weather updates, news headlines, and more.**Competitive Analysis**: Businesses can use web scraping to monitor competitors' prices, product listings, and customer reviews.**Research**: Researchers and academics can gather data for studies and analysis.

Web scraping typically involves the following steps:

**Sending HTTP Requests**: The process starts with sending HTTP requests to the target website's server. This request fetches the web page's HTML content.**HTML Parsing**: Once the HTML content is retrieved, you'll need to parse it. Libraries like BeautifulSoup (Python) or Cheerio (Node.js) help parse the HTML and extract relevant data.**Data Extraction**: After parsing, you can extract specific data elements such as text, images, links, or tables based on your requirements.**Data Storage**: Depending on your project, you can store the extracted data in various formats like CSV, JSON, or databases.

If you're interested in trying web scraping, here's a simplified example using Python and BeautifulSoup.

This code sends a request to '**https://example.com**,' parses the HTML, and extracts the page title. Of course, web scraping can be much more complex depending on the target website and the data you want to collect.

Before you dive into web scraping, it's crucial to be aware of legal and ethical considerations. Always check a website's `robots.txt`

file for scraping guidelines, respect the website's terms of service, and avoid overloading their servers with too many requests.

Web scraping is a powerful technique that empowers individuals and businesses to gather valuable data from the web. It offers endless possibilities for research, analysis, and decision-making. However, it's essential to approach web scraping responsibly and ethically. As you explore this field, you'll discover countless opportunities to leverage web data for various purposes.

If you're interested in diving deeper into web scraping, there are numerous online tutorials, courses, and libraries available to help you on your journey. Happy scraping!

]]>

Django is a high-level, open-source web framework written in Python. It follows the model-view-controller (MVC) architectural pattern, or more precisely, the model-template-view (MTV) pattern, which provides a structured and efficient way to build dynamic web applications. Django's main goal is to enable developers to build web applications quickly, with less code and without sacrificing scalability, security, and maintainability.

**Rapid Development:**Django emphasizes the principle of "don't repeat yourself" (DRY), promoting efficiency and reducing development time. It provides a vast array of built-in tools, libraries, and APIs that handle common web development tasks, allowing developers to focus on the unique aspects of their applications.**Object-Relational Mapping (ORM):**Django's ORM simplifies database access by providing an abstraction layer that allows developers to interact with the database using Python objects. This eliminates the need to write SQL queries manually and makes it easier to manage database operations.**Admin Interface:**Django comes with a built-in admin interface that automates many administrative tasks. It provides a customizable, user-friendly interface for managing data models, user authentication, permissions, and more.**URL Routing:**Django's URL routing system enables the mapping of URLs to specific views, allowing developers to create clean and logical URL structures for their applications. This flexibility makes it easy to handle different types of requests and build RESTful APIs.**Template Engine:**Django includes a powerful template engine that separates the presentation logic from the application's business logic. This allows for the creation of reusable and modular templates that can be dynamically rendered with data.**Security:**Django incorporates various security features, such as protection against common web vulnerabilities like cross-site scripting (XSS), cross-site request forgery (CSRF), and SQL injection. It also provides authentication mechanisms and tools for managing user sessions and permissions.

Django and Flask are both popular Python web frameworks, but they have different philosophies and are suited for different use cases. Here are some key differences between Django and Flask:

**Philosophy and Complexity:****Django:**Django follows the principle of "batteries included" and provides a comprehensive framework with many built-in features and conventions. It emphasizes rapid development, scalability, and code reusability. Django has a steeper learning curve due to its complexity and opinionated structure.**Flask:**Flask follows a minimalist approach and provides a lightweight framework with a minimal core. It aims to be flexible and allows developers to choose and integrate libraries and components according to their needs. Flask gives developers more control and freedom over the application's structure and design.

**Use Case and Project Size:****Django:**Django is well-suited for larger, complex projects that require a full-featured framework. It provides built-in features for user authentication, administration, database ORM, and more, making it ideal for content-heavy websites, e-commerce platforms, and enterprise-level applications.**Flask:**Flask is suitable for smaller projects or applications that require more flexibility and customization. It is often preferred for lightweight RESTful APIs, microservices, and single-page applications where developers want to have fine-grained control over the project's structure and components.

In conclusion, Django is a powerful, full-featured web framework that prioritizes rapid development, scalability, and code reusability. It provides a comprehensive set of built-in features and conventions, making it well-suited for larger, complex projects such as content-heavy websites, e-commerce platforms, and enterprise-level applications. Django's batteries-included approach streamlines development and reduces the need for manual configuration, allowing developers to quickly build robust web applications.

]]>Dynamic Programming or DP is just an optimization technique. It is a method for solving problems by breaking them down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions.

When the same subproblem occurs, we can simply look up the previously computed solution instead of recomputing its solution. It saves computation time at the expense of storage space.

It is vital to know when to use dynamic programming algorithms. There are two major characteristics to identify whether dynamic programming is the right fit.

**1. Optimal Substructure**

The problem should have optimal substructure properties. It means that the optimal solution can be evaluated from the optimal solutions of its sub-problems. This will also help you define the base case of the recursive algorithm.

Consider an example of the Fibonacci series. We define the nth number as the sum of the previous 2 numbers.

**2. Fib(n) = Fib(n-1) + Fib(n-2)**

We can see that a problem of size n can be broken down into sub-problems of size n-1 and n-2. We also know solutions of base cases, i.e., f(0) as 0 and f(1) 1. as 1.

**3. Overlapping subproblems**

The other necessary property is overlapping sub-problems. A problem is said to have overlapping sub-problem properties if the sub-problems can be seen recursively visiting the same sub-problems. In such cases, we can improve the performance of an algorithm by storing the results of each sub-problem once it is calculated.

There are two dynamic programming methods of implementation.

**Top-Down Approach**

This approach solves the bigger problem by recursively solving smaller sub-problems. As we solve the sub-problems, we store the result for later use. This way, we dont need to solve the same sub-problem more than once. This method of saving the intermediate results is called Memoization.

**Bottom-Up Approach**

The bottom-up method is an iterative version of the top-down approach. This approach starts with the smallest and works upwards to the largest sub-problems. Thus when solving a particular sub-problem, we already have results of smaller dependent sub-problems. The results are stored in an n-dimensional (n=>0) table. Thus, you can imagine when we arrive at the original problem, we have solved all its sub-problems. Now we just use the result set to find the best solution. This method is called Tabulation.

**EXAMPLE PROBLEM**

You are climbing a staircase. It takes `n`

steps to reach the top. Each time you can either climb `1`

or `2`

steps. In how many distinct ways can you climb to the top?

`Input: n = 2Output: 2Explanation: There are two ways to climb to the top.1. 1 step + 1 step2. 2 steps`

We'll explore multiple approaches from simple to more complex, incrementally improving upon each solution.

We can translate the recurrence we came up with earlier into code, as follows:

`class Solution: def climbStairs(self, n: int) -> int: if n == 1: return 1 if n == 2: return 2 else: return self.climbStairs(n - 1) + self.climbStairs(n - 2)`

However, running this yields Time Limit Exceeded. Why is it so inefficient? Let's think about calculating the ways to climb 6 stairs, `climbStairs(6)`

.

` climbStairs(6) / \ cS(5) + cS(4) / \ / \ cS(4) + cS(3) cS(3) + cS(2) / \ / \ / \ cS(3) + cS(2) cS(2) + cS(1) cS(2) + cS(1) / \ cS(2) + cS(1)`

As you can see from the recursion tree above, we are calculating `climbStairs(4)`

and `climbStairs(3)`

multiple times. Specifically, `climbStairs(4)`

is being recalculated twice, while `climbStairs(3)`

is being recalculated 3 times. If you think about what happens for larger values of `n`

, you can see that we are recalculating a lot of values!

**Complexity**

**Time**: Each additional level in the recursion tree is going to have double the amount of calls to`climbingStairs`

than the one above it. For n, this gives us a staggering 2^n function calls, for a O(2^n) time complexity. No wonder we get TLE!**Space**: We aren't storing any additional variables, so that's a O(1) space complexity.

Can we avoid repeated computation? Yes we can with the Top-Down DP.

What if instead of recomputing each value of `climbStairs`

, we made sure to save the unique values (such as `climbingStairs(5)`

), trading space for time? That's what a top-down dynamic programming approach called **memoization** is. We make use of a dictionary `memo`

in which we store the values of `climbStairs`

that we have computed, and if we ever have to compute that value again we just check `memo`

in (average) O(1) time instead of doing the work all over again.

`class Solution: def climbStairs(self, n: int) -> int: def climb(n): # inner function to make code simpler if n in memo: return memo[n] else: memo[n] = climb(n-1) + climb(n-2) return memo[n] memo = {1: 1, 2: 2} # base cases return climb(n)`

This top-down paradigm works well when we approach the problem from the top of the stairs (the last step we needed to climb, n) down.

**Complexity**

**Time**: There are O(n) distinct subproblems to solve, each requiring only O(1) amount of work of getting the values of smaller subproblems from`memo`

and adding them together. When we encounter a subproblem we've already solved, we can get the answer in O(1) time.**Space**: We are using an additional`memo`

dictionary that will store the answer to each subproblem, so O(n) space complexity.

Can we be even more efficient and avoid the overhead of recursion? Yes , with the help of Bottom-Up DP.

**3. Bottom-Up DP**

Turns out we can build the solution from the ground up (quite literally in this case). From our recurrence relation, we saw that the number of ways to climb n stairs depends on the number of ways to climb n - 1 and n - 2 stairs. So instead of approaching the problem top-down and computing these values recursively, we compute them bottom-up, starting with the base cases and building upon the previous values until we reach n. We use a `dp`

array of length n + 1 (to accomodate for the 0-based indexing of Python; we could just have it be length n and return `dp[n - 1]`

but in this way we are aligning the step numbers with the indices) and successively build up each index from the previous two.

`class Solution: def climbStairs(self, n: int) -> int: if n == 1 or n == 2: return n dp = [-1] * (n + 1) # to accomodate for 0-based indexing dp[1], dp[2] = 1, 2 for i in range(3, n + 1): dp[i] = dp[i - 1] + dp[i - 2] return dp[n]`

**Complexity**

**Time**: As before, we are computing each subproblem once and each subproblem requires constant amount of work (just the addition of the previous 2 elements of the array). That's O(n) time complexity.**Space**: Since we are storing the answers to previous subproblems in the`dp`

array, this will be O(n) too.We can optimize one more time with the space optimization by maintaing just variables instead of storing the data in an array.

**Optimizied Bottom-Up DP**

While the above works well enough, we can optimize our approach even further by making a simple but important observation: we are only utilizing the last 2 subproblem answers when solving each subproblem. If you look at the recurrence again, you can see that the only pieces information we use are ways(n - 1) and ways(n - 2). Since we're computing from bottom-up, once we compute those answers, the smaller subproblems (such as ways(n - 3)) are not needed anymore. Thus, instead of keeping the entire`dp`

array, we can save some space and just maintain 2 variables that track our last 2 subproblem answers!`class Solution: def climbStairs(self, n: int) -> int: if n <= 2: return n ways = 0 # base cases two_below_curr = 1 # 2 steps below 3 - ways to take 1 step: 1 one_below_curr = 2 # 1 step below 3 - ways to take 2 steps: 2 for i in range(3, n + 1): # compute number of ways for i ways = one_below_curr + two_below_curr # step up to i + 1 # 1 step below becomes 2 steps below # current number of ways becomes 1 step below two_below_curr, one_below_curr = one_below_curr, ways return ways`

**Complexity:****Time**: As before, we are computing each subproblem once and each subproblem requires constant amount of work (just the addition of the previous 2 number of ways). That's O(n) time complexity.**Space**: O(1) since we are maintaining 3 extra variables only!

And that's it! With using Dynamic Programming we went from a TLE solution to an elegant and optimized version.

One might find dynamic programming a bit intimidating initially. But if one understands the basics well, one can master dynamic programming problems. Having a strong programming foundation is key to getting comfortable with such problems. Applications of dynamic programming are common and relevant to everyday challenges, and mastering dynamic programming gives you the superpower to tacproachkle them.

]]>Programmers might say that its about programming with Python and sophisticated mathematical algorithms.

Business stakeholders usually associate machine learning with data, and a dash of mystery.

Machine learning engineers tend to talk about model training and data wrangling.

So who is right? Everyone.

Machine learning is about data no lie there. Theres no machine learning without a decent amount of data for the machine to learn from. The amount of available data is growing exponentially, which makes machine learning development easier than ever.

The connection between machine learning and algorithms is also on point. Indeed, there are complex mathematical methods that force machines to learn. No math no machine learning.

Lastly, model training and data preparation is indeed the core of every ML project. Machine learning engineers spend a substantial amount of time training models and preparing datasets. Thats why its the first thing ML engineers think of.

Machine learning is about development, manipulating data, and modeling. All of these separate parts together form a **machine learning project life cycle**, and thats exactly what were going to talk about in this article.

The Machine learning life cycle involves seven major steps, which are given below:

**Gathering Data****Data preparation****Data Wrangling****Analyse Data****Train the model****Test the model****Deployment**

Data Gathering is the first step of the machine learning life cycle. The goal of this step is to identify and obtain all data-related problems.

In this step, we need to identify the different data sources, as data can be collected from various sources such as **files**, **databases**, the **internet**, or **mobile devices**. It is one of the most important steps of the life cycle. The quantity and quality of the collected data will determine the efficiency of the output. The more will be the data, the more accurate will be the prediction.

After collecting the data, we need to prepare it for further steps. Data preparation is a step where we put our data into a suitable place and prepare it to use in our machine learning training.

In this step, first, we put all data together, and then randomize the ordering of data.

This step can be further divided into two processes:

**Data exploration:**

It is used to understand the nature of the data that we have to work with. We need to understand the characteristics, format, and quality of data.

A better understanding of data leads to an effective outcome. In this, we find Correlations, general trends, and outliers.**Data pre-processing:**

Now the next step is preprocessing of data for its analysis.

Data wrangling is the process of cleaning and converting raw data into a usable format. It is the process of cleaning the data, selecting the variable to use, and transforming the data into a proper format to make it more suitable for analysis in the next step. It is one of the most important steps of the complete process. Cleaning of data is required to address the quality issues.

The data we have collected don't need to be always for our use as some of the data may not be useful. In real-world applications, collected data may have various issues, including:

**Missing Values****Duplicate data****Invalid data****Noise**

So, we use various filtering techniques to clean the data.

Now the cleaned and prepared data is passed on to the analysis step. This step involves:

**Selection of analytical techniques****Building models****Review the result**

This step aims to build a machine-learning model to analyze the data using various analytical techniques and review the outcome. It starts with the determination of the type of the problems, where we select the machine learning techniques such as **Classification**, **Regression**, **Cluster analysis**, **Association**, etc. then build the model using prepared data, and evaluate the model.

Hence, in this step, we take the data and use machine learning algorithms to build the model.

Now the next step is to train the model, in this step, we train our model to improve its performance for a better outcome of the problem.

We use datasets to train the model using various machine-learning algorithms. Training a model is required so that it can understand the various patterns, rules, and, features.

Once our machine learning model has been trained on a given dataset, then we test the model. In this step, we check for the accuracy of our model by providing a test dataset to it.

Testing the model determines the percentage accuracy of the model as per the requirement of the problem.

The last step of the machine learning life cycle is deployment, where we deploy the model in a real-world system.

If the above-prepared model is producing an accurate result as per our requirement with acceptable speed, then we deploy the model in the real system. But before deploying the project, we will check whether it is improving its performance using available data or not. The deployment phase is similar to making the final report for a project.

]]>Tree is considered a non-linear data structure. The data in a tree are not stored in a sequential manner i.e, they are not stored linearly. Instead, they are arranged on multiple levels or we can say it is a hierarchical structure. For this reason, the tree is considered to be a non-linear data structure.

Binary Tree is defined as a tree data structure where each node has at most 2 children. Since each element in a binary tree can have only 2 children, we typically name them the left and right child.

A Binary tree is represented by a pointer to the topmost node (commonly known as the root) of the tree. If the tree is empty, then the value of the root is NULL. Each node of a Binary Tree contains the following parts :

1. Data

2. Pointer to left child

3. Pointer to right child

We create a tree data structure in python by using the concept of node discussed earlier. We designate one node as root node and then add more nodes as child nodes.

We just create a Node class and add assign a value to the node. This becomes tree with only a root node.

When the above code executes it gives the result as shown:

25

To insert into a tree we use the same node class created above and add an insert method to it. Here we are creating a Binary Search Tree.

A binary search tree is a rooted binary tree in which the nodes are arranged in strict total order in which the nodes with keys greater than any particular node is stored on the right sub-trees and the ones with equal to or less than are stored on the left sub-tree satisfying the binary search property.

The insert method compares the value of the node to the parent node and decides to add it as a left node or a right node. Finally, the printTree method is used to print the tree.

The code when executes gives the result -->

The output result is given as :

[5,12,45,58,100]

In this way we can implement a Tree Data structure in Python.

]]>Data Science is all about - - > **Observing --> Analyzing --> Reacting.**

As in real life, we observe people analyze their behaviour and then react in a specific way. As our behaviour towards children is really different from our behaviour towards our friends. So, what we do here is, we analyze historical behaviour and react according to that.

Data Science is simply applying advanced analytics techniques **to draw valuable insights from data** for better decision planning, future planning and whatever you want to do with those insights. **Those insights** may help you with your future strategies to increase profit if you are a business professional and you use those insights to your advantage.

Machine learning (a field of Data Science) **allows the user to feed a computer algorithm an immense amount of data** and have the computer analyze and make data-driven recommendations and decisions based on any input data given by the user.