DEV Community: Ravikumar N

Deploying a web server on the Linux virtual machine

Ravikumar N — Wed, 15 May 2024 17:58:35 +0000

To deploy a web server on a Linux virtual machine, you will first need to ensure that your virtual machine is up and running and that you have access to it, either through a command line interface or a remote desktop connection — to learn how to create a virtual machine on Azure Click Here.

here's a list of the five diverse use case examples for deploying a web server on a Linux virtual machine:

Hosting a Blog or Personal Website
E-commerce Platform
Corporate Intranet
Online Learning Platform
IoT Data Visualization

Once the Virtual machine is up and running you can follow these steps:

Access the Linux virtual machine: Use tools like putty to establish a connection to your Linux virtual machine. You will need the IP address or hostname of the machine, as well as the login credentials.

Once you click on Open, enter your login ID and password.

Update package Index: Once logged in, it's a good practice to update the package index to ensure you have the latest information about the available packages. Run the following command.

sudo apt-get update

Install Nginx Web Server: Nginx is a popular web server that you can install using the following command:

sudo apt-get install nginx

Manage Nginx Service: you can stop, start, or restart the Nginx service using the following commands:

sudo systemctl stop nginx     # Stop Nginx
sudo systemctl start nginx    # Start Nginx
sudo systemctl restart nginx  # Restart Nginx

Verify Nginx Installation: You can verify that Nginx is running correctly by accessing your virtual machine's IP address or hostname in a web browser. If Nginx is installed and running properly, you should see the default Nginx welcome page.

That's it! You should now have a functional Nginx web server deployed on your Linux virtual machine.

Create a Windows virtual machine in the Azure portal

Ravikumar N — Fri, 10 May 2024 13:30:52 +0000

In this blog, you will learn to deploy a simple virtual machine, open a network port for web traffic, and install a basic web server.

Applies to: ✔️ Windows VMs

Azure virtual machines (VMs) can be created through the Azure portal. This method provides a browser-based user interface to create VMs and their associated resources. This quickstart shows you how to use the Azure portal to deploy a virtual machine (VM) in Azure that runs Windows Server 2019. To see your VM in action, you then RDP to the VM and install the IIS web server.

If you don't have an Azure subscription, create a free account before you begin.

Step 1: Sign in to the Azure portal.
Step 2: Create virtual machine

Enter virtual machines in the search.
Under Services, select Virtual machines.
In the Virtual machines page, select Create and then Azure virtual machine. The Create a virtual machine page opens.
Under Instance details, enter myVM for the Virtual machine name and choose Windows Server 2022 Datacenter - x64 Gen 2 for the Image. Leave the other defaults.

Under Administrator account, provide a username, such as azureuser and a password. The password must be at least 12 characters long and meet the defined complexity requirements.

Under Inbound port rules, choose Allow selected ports and then select RDP (3389) and HTTP (80) from the drop-down.

Leave the remaining defaults and then select the Review + create button at the bottom of the page.

After validation runs, select the Create button at the bottom of the page.

After deployment is complete, select Go to resource.

Step 3: Connect to virtual machine
Create a remote desktop connection to the virtual machine. These directions tell you how to connect to your VM from a Windows computer. On a Mac, you need an RDP client such as this Remote Desktop Client from the Mac App Store.

On the overview page for your virtual machine, select the Connect > RDP.

In the Connect with RDP tab, keep the default options to connect by IP address, over port 3389, and click Download RDP file.

Open the downloaded RDP file and click Connect when prompted.
In the Windows Security window, select More choices and then Use a different account. Type the username as localhost\username, enter the password you created for the virtual machine, and then click OK.
You may receive a certificate warning during the sign-in process. Click Yes or Continue to create the connection

Note: Clean up resources
Delete resources when no longer needed, you can delete the resource group, virtual machine, and all related resources.

On the Overview page for the VM, select the Resource group link.

At the top of the page for the resource group, select Delete resource group.

A page will open warning you that you are about to delete resources. Type the name of the resource group and select Delete to finish deleting the resources and the resource group.

Harnessing Machine Learning for Innovative Projects: The Potential Unleashed

Ravikumar N — Fri, 26 Apr 2024 02:10:48 +0000

The rapid advancements in technology are propelling industries into new realms of efficiency and innovation. Among these technologies, machine learning (ML) stands out as a transformative force, offering endless possibilities for enhancing projects, whether they're personal hobbies or large-scale enterprise operations.

Personal Projects: Giving Life to Your Ideas with ML

Imagine building a robot in your garage. Now, think about that robot having the ability to not just perform tasks but to learn from its environment. Machine learning can make this a reality. By integrating ML algorithms, your homemade robot can be programmed to recognize faces or navigate through your house. The inclusion of ML transforms a simple mechanical device into an intelligent entity capable of making decisions and learning from interactions.

Enterprise Applications: Turning Data into Decisions

In the corporate world, the amount of data generated by day-to-day operations is colossal. From user logs and financial records to production metrics and HR reports, every piece of data holds the potential to optimize and transform business processes. Machine learning acts as a key to unlock this potential.

Customer Segmentation and Targeted Marketing
With machine learning, businesses can analyze their customer data to segment them into distinct groups based on behaviors, preferences, and demographics. This segmentation allows for tailored marketing strategies that are much more effective than a one-size-fits-all approach. By targeting specific groups with strategies that resonate, companies can maximize their marketing ROI.

Personalized Product Recommendations
Online retailers and service providers can harness ML to analyze purchase histories and browsing behaviors to recommend products that a customer is more likely to buy. Similar to the technology used by leading e-commerce platforms, this approach not only enhances customer experience but also boosts sales efficiency.

Fraud Detection
Financial institutions and e-commerce companies can use machine learning to identify potentially fraudulent transactions with a high degree of accuracy. ML algorithms can learn from historical fraud data and detect patterns that are indicative of fraudulent activities, helping to preemptively stop these transactions and reduce losses.

The Road Ahead with Machine Learning

Adopting machine learning in your projects, be it at home or within a multinational corporation, involves understanding the core objectives and identifying the right algorithms that align with your goals. The integration of ML not only augments the capabilities of existing systems but also opens up new avenues for innovation and efficiency.

For those looking to dive into the world of machine learning, starting with a clear project goal and gradually building up your understanding of ML technologies and tools is key. Whether you are programming a robot to navigate through obstacles or analyzing complex data to streamline business operations, the journey with ML promises to be one of growth and significant rewards.

The potential of machine learning is only beginning to be tapped, and as technology evolves, so too will the opportunities for its application. Now is the perfect time to explore how ML can transform your projects and ideas into something truly revolutionary.

𝐂𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐑𝐢𝐠𝐡𝐭 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐞𝐭𝐫𝐢𝐜 𝐟𝐨𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧! 🚀

Ravikumar N — Wed, 03 Apr 2024 15:29:54 +0000

Feeling bewildered about which metrics to employ for evaluating your binary classification model? Let's navigate through and ascertain the optimal way to assess the classification model.

🎯 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲:
→ Indicates the proportion of correctly classified instances among all instances.
→ Inadequate for imbalanced datasets as it might be deceptive.

💡 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧:
→ Quantifies the proportion of true positives among all positive predictions.
→ High Precision is crucial in scenarios where false positives are undesirable.
→ It aids in addressing the query: "Among all the instances predicted as positive, how many are truly positive?"

📊 𝐑𝐞𝐜𝐚𝐥𝐥:
→ Computes the proportion of true positives among all actual positives.
→ Also referred to as sensitivity or true positive rate.
→ High Recall is crucial in scenarios where false negatives are undesirable.
→ It aids in answering the question: "Of all the actual positive instances, how many did we accurately identify?"

📐 𝐅1 𝐒𝐜𝐨𝐫𝐞:
→ Represents the harmonic mean of precision and recall.
→ Incorporates both precision and recall, yielding a unified metric that balances the two.

🔍 𝐋𝐞𝐭'𝐬 𝐝𝐢𝐬𝐜𝐮𝐬𝐬:
→ Which evaluation metric do you primarily utilize in your domain?
→ Are there any additional metrics you employ aside from the ones discussed?

P.S. - Seeking professional advice to elevate your Data Science career? Feel free to drop me a DM with specific inquiries.

Loan Default Prediction and Investment Decisions for Lending Club

Ravikumar N — Tue, 02 Apr 2024 18:44:16 +0000

Introducing CreditWiseInvest: A free ML app to help you pick the best loan pool on a risk-reward basis

Problem Statement:

Peer-to-peer lending marketplaces such as LendingClub and Prosper Marketplace operate on a model where they act as intermediaries connecting investors with borrowers. These platforms charge a broker's fee for facilitating these transactions and are motivated to increase the total number of transactions occurring on their platforms.

However, the current approach to credit risk assessment on these platforms relies on off-the-shelf models that categorize borrowers into grouped buckets based on credit scores. While this approach may be convenient, it fails to account for the uniqueness of each loan and the extensive data available from borrowers. Assessing credit risk on a more granular and continuous basis, rather than discrete grouped buckets, presents an opportunity to enhance the efficiency and accuracy of risk assessment in peer-to-peer lending.

Understanding and managing credit risk is crucial for investors in peer-to-peer lending and bond markets. Institutions, like large banks, dedicate resources to analyze borrower data, aiming to enhance risk assessments and investment decisions.

The primary risk investors face is borrower default, where borrowers fail to make required payments, resulting in potential loss of principal and interest. To assess potential returns, investors consider loan terms and borrower profiles.

For everyday investors seeking simplicity and effectiveness, a user-friendly tool is needed. This tool should offer improved risk assessment compared to off-the-shelf advisors, empowering investors to make informed decisions and potentially outperform the market.

Solution: CreditWiseInvest

CreditWiseInvest helps individual investors augment their portfolio by intelligently allocating funds to Peer-to-Peer Lending Marketplaces using machine learning trained on LendingClub.com historical loan data to assess risk and predict return.

CreditWiseInvest recommends the best loans to invest in given a user’s available funds, maximum risk tolerance, and minimum desired annualized return. There are plenty of institutions that are thought leaders in utilizing modern, alternative datasets to assess credit risk and predict investment potential, but I’ve simplified the problem into 2 key models:

(1) scoring risk by predicting the probability that a loan defaults, and

(2) predicting annualized returns.

Important Terms & Concepts:

Peer-to-Peer Lending (also known as P2P or Crowdlending):

Peer-to-Peer Lending (P2P): Online platforms connecting lenders directly with borrowers, offering higher returns for lenders and lower interest rates for borrowers compared to traditional banks.

Artificial Neural Network (ANN): A computational learning system that uses a network of functions to translate data inputs into desired outputs. Inspired by the functioning of neurons in the human brain, ANN can understand and process inputs from various sources.

Random Forest Model: An ensemble learning method that constructs multiple decision trees during training and outputs the mode of classes or mean prediction of individual trees. It is used for classification, regression, and other tasks, providing robust and accurate predictions.

Probability of Default: An estimate of the likelihood that a borrower will fail to meet its debt obligations, indicating the risk associated with lending to that borrower.

Annualized Return: Returns over a period scaled down to a 12-month period, providing a standardized measure for comparing investment performance. The formula for annualized return takes into account the loan amount, total payment made by the borrower, and the duration of the loan.

GitHub, Website
Let’s connect! I encourage you to like, comment, share, or message me directly with your thoughts on the ideas presented here, or suggestions on interesting topics I should look into going forward.