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⭐️ 4.8/5 Coffret huiles essentielles - Bien-être intense x15 Quelle huile essentielle utiliser pour soigner la rate ? Quelle huile essentielle utiliser pour soigner la...

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⭐️ 4.8/5 Coffret huiles essentielles - Bien-être intense x15 Huile essentielle d'iris: quels sont ses bienfaits et propriétés ? Les bienfaits de l'huile essentielle d'iris...

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Bluetooth 5.1 / Carbon Fiber Pattern Design / Wireless Headphone / Everplay X

Double layer drivers and built-in aptX HD codec provide enhanced noise cancelling and optimal sound quality.

Tune out background noise or allow background so you can hear what's going on around you.

Enjoy your freedom to roam when connected to your phone, desktop or laptop with multiple devices all in one place.

"Memory Foam Earpads"
High quality memory foam ear pads provide a comfortable fit and insulates your ears so that the purest sound reaches your ear without any distortion.

"Quick Charging & 43hr Battery"
Convenient wireless charging, one hour of playback time with a 10-minute charge.

Intuitive touch interface gives you full control of all functionality. Turn music on or off, answer calls, and much more.

IPX4 sweat - and - water resistant design keeps your headphones protected during workouts or light rains.

Whether listening to music or watching a video, Smart Play & Pause will automatically stop playback once the headphones are removed and resume once worn again.

Digital hybrid noise cancelling

We have achieved a maximum performance of up to 98% noise reduction by using both the outside (feed forward) and the inside (feedback) microphones. 24-bit depth/192 kHz sampling rate of digital noise filtering gives the Legacy 900 the most precise and accurate noise cancelling performance.

Bluetooth 5.1

Built-in aptX HD codec provides a more efficient digital streaming platform and high fidelity 24-bit depth delivers superb acoustic performance.

Phiaton Sound Technology

Customized 40 mm Speaker Driver For Noise Cancelling.
Enhanced noise cancelling performance and optimal sound quality are achieved by stacking up two diaphragms with different acoustic characteristics.

Intuitive Touch Controls

Well placed and intuitive touch interface makes it easy to control your device with a light tap or swipe (PLAY, PAUSE, FF, REW, VOL+, VOL-, CALL FUNCTIONS)

Fast Charge
and The Longest Battery Life

10 min of fast charge provides 4 hrs of playtime. Up to 43 hours of playback time with noise cancelling turned on

Shockproof and water resistant case

Keep your 900 Legacy headphones looking new and ready to use.

Tech Specs

Communication Network

Bluetooth Version 5.1

Output Power

Power Class 2

Max. Communication Range

Discoverable within 10 m / 33 ft.

Music Play Time

39 hrs / 43 hrs (NC on / off)

Full Charge Time

3 hour 20 minutes

Fast Charging

Support 10 min Charging = 4 hrs Playing

Standby Time

Max. 320 Hours

Frequency Range

2.4 GHz (2.402 GHz ~ 2.480 GHz)

Bluetooth Profile Support

HFP v1.7 / HSP v1.2 / A2DP v1.3.1 / AVRCP v1.6

Battery

Lithium Polymer DC 3.7 V / 1000 mAh

Microphone Frequency Range

50 ~ 4,000 Hz

Microphone Sensitivity

-42 dB

Support Echo Cancellation and Talk Noise

cVc® Solution

Operating Temperature

-20 ~ 45 °C

Size

180 x 172 x 23 mm (H x W x D) /
7.1 x 6.8 x 0.9 inch (H x W x D)

Audio Codec

SBC, Qualcomm® aptXTMHD, aptXTM, AAC (If the 'Bluetooth Device' can not support Qualcomm® aptXTMor aptX-HDTMaudio or AAC mode, it will be connected with SBC mode.)

The power does not turn on?

Charge the device. If the power does not turn on even after charging, contact Phiaton Customer Service.

Why is the music sound quality is low?

If the 'Bluetooth Device' you are trying to connect with this device is accessed to HSP, change the access to A2DP.

Why is outside noise is too high or not reduced?

Check whether Noise Cancelling is turned ON or AUDIO TRANSPARENCY has been activated.

My device is paired with PC/Laptop but does not work:

PC/Laptop is equipped with various Bluetooth drivers depending on the manufacturer and it is possible it does not support 900 LEGACY.

FAQ

Got questions? We've got answers.
If you have some other questions, feel free to send us an email to support@phiaton.com. For general inquiries please check our FAQ page.

What our customers think about 900 Legacy

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Digital Hybrid Noise Cancelling Headphones. Longest Battery Life with up to 43 hours of battery life. Intuitive Touch Controls and Carbon Fiber Earpads. Everplay-X for tethered playback. Bluetooth 5.2

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Wondering which is best for you?

These are currently the only two Nordictrack Incline trainer treadmills for sale - with past models like the X7, X9 and X11 being discontinued a couple of years ago.

Overall, both the X22i and the X32i incline treadmills are commercial-grade, premium incline trainers. They both give you much more than you get with a standard treadmill. With inclines of up to 40% you can burn up to 5 times more calories than on a regular treadmill.

And with 6% decline you get added crosstraining options.

Both treadmills are iFit Coach compatible. So you can immerse yourself in famous world trails - hike the Swiss Alps, run along the beaches of Hawaii or take a guided walking tour through the streets of Florence.

You can also take a high-energy, studio fitness class right from your treadmill console - everything from HIIT training to Bootcamps to Yoga workouts.

So which treadmill is best for you?

While they look very similar, there are a few key differences you might want to know. Here's a comparison of the Nordictrack X22i vs X32i to help you decide:

Nordictrack X22i vs X32i - Price

Prices can jump around a bit depending on the season. However in general, the Nordictrack X22i trainer is the more affordable model - coming in around $3199 with Free Shipping here.

The X32i is the next model up with a few more perks to it. It comes in around $4299 with Special iFit Offer here . It's really the ultimate commercial incline trainer in the entire series. So is the X32i incline trainer worth the extra money?

Nordictrack X22i vs X32i: Console

The console is where you see a large difference between these two machines. The Nordictrack X22i comes with one of the largest consoles that Nordictrack makes - a full 22 inches across which you can see below:

This was actually the largest console that Nordictrack used to make - until the Nordictrack X32i came out.

The X32i model steps it up to a larger 32 inch Smart HD Touchscreen console .

While the X22i console is big, the X32i console is massive as you can see below:

The larger screen size can give you a more immersive workout experience . I had one user tell me that it was like they were actually running through the streets of Paris - they didn't want to get off the treadmill.

The larger screen can also give you (or someone in your home) that little extra motivation they might need to use the treadmill every day as well.

Here's another pic to give you an idea of the difference in screen sizes between the Nordictrack X32i vs X22i:

Both consoles screens will also tilt up or back so you can find the right viewing angle for you.

They also give you side auxiliary music ports with speakers, easy one-touch speed and incline buttons as well as built-in console cooling fans for extra comfort.

Nordictrack X32i vs X22i - Belt Size

The Nordictrack X22i incline trainer comes with a long 60 inch belt - which is longer than the standard 52-55 inch belts you get with a lot of treadmills these days.

So you have more room to stretch out and run without feeling like you're bumping up against the edge of the treadbelt.

However, the Nordictrack X32i trainer goes one better with an even longer 65 inch belt . This is the longest belt that Nordictrack makes and gives you even more room to stretch out.

Nordictrack X22i or X32i: Motor

Both treadmills come with very strong commercial-grade motors to support you in longer or more intense workouts. The Nordictrack X22i treadmill gives you a 4.0 CHP motor.

However the Nordictrack X32i steps it up to a stronger 4.25 CHP motor . So it can handle a bit more use and abuse.

Treadmill Weight Differences And Stability

While both treadmills are quite heavy (which helps them feel extra stable), there's a difference in machine weight that you'll want to consider.

The X22i comes in at a heavy . The X32i comes in even higher at . The heavier weight can help contribute to a more stable feeling treadmill.

However, it's also something to consider if you plan on moving your treadmill around in the future (although both treadmills are so heavy, you probably don't want to plan on moving them around often).

Bottom Line?

So which treadmill is the best value when comparing between the Nordictrack X22i vs X32i incline trainers?

Well the X22i incline trainer is more affordable. And it still has the amazing 40% incline and 6% decline for better crosstraining and more calorie burning (than a standard treadmill).

The X32i incline trainer however gives you the ultimate workout experience. With the larger screen and auto-adjust technology, you can really immersive yourself in the iFit world trail workouts or classes.

Plus it has a stronger motor and a heavier unit weight for higher stability. And taller users will appreciate the extra-long running belt.

At the end of the day, both treadmills are premium machines that will give you a fun, effective workout.

It really comes down to your preferences, budget and goals.

Want to learn more? Check out the links below to buy direct from the manufacturer, get Free Shipping and a special iFit offer on either treadmill:

Related:
Nordictrack X22i Incline Trainer - A Good Buy for You? Nordictrack X32i Incline Trainer Review

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Nordictrack X22i vs X32i Incline Treadmill 2023 - Which is Best for You?

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Trying to compare the Nordictrack X22i va X32i incline trainer? How do they stack up? And which is best for you? Here's what you need to know!

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AI agents are the future of project management and team collaboration. And they might just be the productivity boost you need. But what are they exactly? And how do they work?

Picture this: Your team is working on a big project launch. Just when you think you are about to hit a home run and get to the grand reveal, life throws a curveball. Or a couple of those.

Dave, the project coordinator, is stuck in a lengthy meeting. Meanwhile, Lisa, the data analyst, needs some input from Dave to finalize the pricing strategy. Over in marketing, Mike is ready to craft a promotional strategy but he's waiting for the data from Lisa to proceed.

It's a stalemate if we ever saw one. 🤷‍♂️

Now imagine a workforce of the future where autonomous AI agents work alongside humans, collaborating, exchanging data, organizing documents, and tackling tasks behind the scenes. 🤖

All Dave needs to do is let his AI agent transcribe and send the meeting notes to Lisa. Lisa's agent will scan the notes and push a bite-size summary to her inbox. And Mike? With his agent pulling the data from other agents, he can finalize his share and call it a day. Magic! 🪄

AI agents are a step up from traditional chatbots. They are designed to interact with large language models (LLMs) like GPT-4 to achieve a set of objectives mostly autonomously. Think of them as decision-making algorithms that create and prioritize tasks for AI to follow.

Some agents have the ability to selectively tap into several language models and choose the one that's most appropriate for a given task. They can also "chain" with external tools and apps to give LLMs long-term memory, enable web searches, or perform complex computations.

And here's the best part.

AI agents can interact with each other and "collaborate" on tasks just like humans do. Teams of agents are able to dynamically analyze tasks, pool required resources, select external tools, and share the workload depending on their unique skill sets and specialized knowledge.

But we'll get to the technical nitty-gritty in a moment.

The question of the day is, why on earth would you need a bunch of AIs running your tasks without supervision? Well, if you're a business owner, a team manager, or just want to work smarter and not harder, then AI agents could be your ticket to cruising above the daily grind.

These days, voice assistants can do much of what agents are starting to learn. Google Assistant can find a free slot in your calendar and set up a meeting. Alexa can read out your emails while you're getting ready in the morning. You can even ask Siri to summarize your day.

But all those tools work in a vacuum. They can't "talk" to each other and collaborate to make your life easier. They exist and operate within the walled gardens of glorified tech ecosystems.

So, what are the alternatives?

Earlier this year, researchers at Stanford and Google attempted to answer a similar question. They placed 25 AI agents in a virtual sandbox environment and gave each a unique personality, long-term memory, and a set of goals. And then they left that digital petri dish to simmer.

The results?

The agents performed their daily chores, engaged in conversations, formed relationships, and even planned a party in the village, all without any human intervention.

Of course, all those interactions unfolded in a confined virtual space. But what if those agents lived inside your task or project management apps? What if they could help you prioritize tasks, assign them to team members, and give you a nudge when deadlines are creeping up?

An agent with knowledge of your brand's voice and ethos could revolutionize campaign design and execution. It could not only help your team create stellar content in line with your brand's identity but also predict trends and suggest best platforms for maximum reach.

The potential here isn't just about automation. Agents can completely redefine the way get stuff done and think about work in general. They have the ability to manage mundane tasks but also offer strategic insights, streamline processes, and foster collaboration.

And that feature is just around the corner.

A world where artificial intelligence isn't just a tool, but a partner, a teammate? 🤔

Welcome to Taskade's vision of tomorrow, where customizable AI agents seamlessly collaborate with humans, reshaping the dynamics of the workplace. The future where AI agents work alongside humans, running in the background and enhancing productivity.

Roundtable is our latest addition to the two other types of AI agents - 🌐 SEO and 🔎 Research agents - you can already use in Taskade. It is an embodiment of a diverse AI team of specialized agents, each with a unique personality, role, and skills, ready to offer advice when you need it.

Need help with a particular aspect of your project or task? All you need to do is type /roundtable, and Taskade will generate a unique panel of AI experts ready to chime in.

Here are a few examples of AI agents generated with /roundtable:

👔 CEO Agent: Facing leadership challenges? The CEO agent can tap into the power of natural language processing and machine learning to guide you with world-class strategies.

🗂️ PM Agent: Struggling with team dynamics or project workflows? The PM Agent will assist you with effective communication and collaboration techniques.

🎨 Designer Agent: Whether it's untangling nuanced client feedback or brainstorming fresh ideas, the Designer Agent will bring clarity and creativity to the table.

🔀 Operations Manager Agent: Feeling bogged down by bottlenecks? The Operations Manager Agent will ensure that operations run smoothly across your business.

⚖️ Legal Agent: Contracts, compliance, or copyrights, the Legal Agent will help you stay up to speed with the ever-changing legal requirements and standards.

📤 Marketer Agent: Need a sharper marketing edge? Master content creation, branding, and the art of running digital campaigns with the Marketer Agent.

Whether you're a CEO navigating complex leadership challenges or a solopreneur who just wants to get work done, the Roundtable Agent will give you the support you can rely on.

But this is just the first step in this revolution. Before we go any further, let's see how agents actually work, where they are today, and most importantly, where they are going. 👇

The design of most modern AI agents is rooted in a paper titled "Task-driven Autonomous Agent" published in 2022 by Yohei Nakajima, a general partner at Untapped Capital.

The idea is simple - to create a "wrapper" that, in combination with a large language model, can create a task list, prioritize, and guide itself, or rather the LLM, through self-directed loops. All this to achieve an objective set by the user without the need for manual reprompting.

There are a few key components that make agents tick:

🧠 Large Language Model (LLM): The brains of an agent, LLMs act just like your computer's OS, but for processing language. Thanks to the power of machine learning and natural language processing, language models offer a vast knowledge of diverse subjects and excellent context comprehension, just the things you need to get work done.

✅ Execution / Task Creation Agent: Similar to a CPU, the primary agent function is to figure out what needs to be done and in what order. It's also responsible for "guiding" the LLM and "chaining it" with long-term memory and any external tools as needed.

💾 Memory: An agent's memory is a blend of your computer's Random Access Memory (RAM) and a hard drive. This is where the agent stores data for retrieval and processing. Modern use vector databases like Pinecone or Chroma to remember task context.

⚡ Additional Tools: Pairing an agent with a single LLM would be like using a PC without peripherals. Tools extend the functionality of agents by enabling web access, unlocking specialized knowledge, or combining them with multiple specialized AI models.

Some open-source agents like BabyAGI can already perform simple tasks including conducting basic market research or prototyping web pages. But just like voice assistants, most agents work in a vacuum, and combining them with existing workflows requires technical know-how.

The solution?

Fully customizable, long-running, autonomous task management seamlessly integrated with whatever tools you're already using. AI Agents that can operate without supervision over extended periods, running in the background and "talking" to your projects and tasks.

There is a lot to unpack here, so let's see how those agents can make an impact on your work.

Every project starts with diligent research, gathering information, pooling resources, analyzing risks, asking the right questions... That's tedious work. But what if the repetitive parts of the project initiation, like gathering preliminary data, could be handed off to an AI agent? 🤔

A long-running agent working from behind the scenes could proactively set the stage for all your projects. Tasks, dependencies, deadlines, obstacles, and solutions, ready the moment you type a project title. The manual slog? History. The endless back and forths? Reduced.

AI agents will seamlessly integrate with your existing workflows and streamline them for your team's convenience. Whether it's improving the flow of information between departments or updating project milestones based on real-time data, the possibilities here are endless.

There are a lot of tasks we would rather not do. Hours spent on data entry, sifting through endless emails, organizing files... Put up with it, or... build a team of custom, intelligent agents, each specialized in tackling a set of routine tasks, day in day out.

Burning through a load of projects every month?

Chances are there is a lot of stuff to clean up. Let an agent run in the background and do the housekeeping for you, archiving documents, chats, and tasks. It's like inbox zero but for projects.

Onboarding new hires? Train an agent using internal onboarding materials so it can drip-feed the knowledge to new team members, answer their questions, and show the way.

A team of friendly AIs tackling routine tasks and helping people solve problems?

Sign us up. 👍

How many times have you stared at a blank page, the dreaded cursor blinking back mockingly?

What if that was a thing of the past? What if AI could do the research, gather resources, create an outline, and flesh out your initial ideas before you even warm up your typing fingers? With all the pieces in place, you'd just need to embrace the muse and start typing,

Let's take this a step further.

Why not give your custom AI agent a unique personality and a set of skills to match? Then let it lose inside your workspace, "living" in your documents, ready to provide guidance and advice.

A snarky editor with a penchant for one-liners? Done.

A verbose novelist who knows how to churn out epic sagas? Check.

Just tweak the settings. Train your agent on documents of your choosing: be it your past masterpieces, or the works of literary giants you admire. Over time, the agent will evolve and adapt to your style and your voice. A perfect writing companion for the modern age.

Managing your online presence is challenging even with an army of PR experts watching your back. But if you're a solopreneur or an SMB owner, things can get hectic really quickly.

Answering questions, keeping track of analytics, replying to comments and questions, handling the occasional PR hiccup, you get the idea. An agent could help you cultivate your digital plots (think social media accounts and business websites) so you can focus on what you do best.

You can create an agent to proactively auto-draft product changelogs, outline press releases based on product changes, or auto-populate a social media content calendar with post ideas. Let AI stir the pot and humans handle the seasoning with stellar content.

How many hours do you spend poring over data, reading countless articles, or cross-referencing sources?Imagine if, instead of sifting through mountains of data, you could find the most relevant, curated, bite-sized insights the moment you open your computer.

Want to know what your competitors are up to?

Need to read company reports every morning?

Why not set up an agent that will distill key details and deliver bite-sized summaries to your inbox or Taskade project? Just so you have something to read to your morning coffee. ☕

So where are we going from here?

How to bridge the gap between what agents are today and what they can become?

Our vision is to usher in an era of autonomous, long-running AI Agents. We're creating the AI dream team of the future, fully customizable, seamlessly integrated with existing workflows, and collaborating side-by-side with your team to maximize performance.

This is truly an exciting time, but it's just the beginning. Here is a sneak peek at what we're working on at Taskade and what you can expect in the coming months.

Most AI agents require technical know-how to run. And even then, the chances of getting them to work with your existing workflow are slim. Our agent design is much simpler and fully customizable so you can control every aspect of your agents with a visual, intuitive interface.

Define your agent's:

👤 Name and avatar

🎯 Objectives and goals

🌟 Personality and tone

🧠 Knowledge and skills

⏰ Running schedule

✏️ Level of access

and much more...

A single workspace will include multiple agents, each with a different set of tasks and rules to follow, from a diligent data analyzer to a proactive project manager. This will give you the ability to create your perfect team of digital assistants tailored to your needs.

Agents are only as good as the tools they have at their disposal, and thanks to the upcoming Taskade API, you will be able to easily give agents access to external platforms. The API will enable thousands of apps to integrate with Taskade and communicate with your agents.

Here are a few examples:

👥 HR Teams: Streamline your recruitment process in a few clicks. Assign a custom agent to post job listings on platforms like LinkedIn, Indeed, or Glassdoor.

🎧 Customer Success Teams: Customer queries never stop coming. Use an agent to pull support tickets from Zendesk and turn them into tasks inside your Taskade project.

✍️ Marketing Teams: Got a story to tell? Let an agent handle the manual work, from drafting a complete content calendar to uploading articles to WordPress or Ghost.

💰 Finance Teams: Automate invoicing in a flash. As soon as a project/task is marked complete in Taskade, set an agent to trigger the creation of an invoice in QuickBooks.

🖥️ DevOps & IT Teams: Use an agent to automatically create a task inside a Taskade. project when a high-priority incident is reported in ServiceNow.

Have other integrations in mind? Let us know!

The beauty of AI agents is that they can be trained to better understand the context of your projects. Taskade agents will be able to learn from online resources or any documents you upload. So you can sit back and relax when they cram for their next task. 🧠

The new generation of Taskade agents will learn from the activity within a project, delve deep into comments, and analyze project and task history. They will parse and understand a variety of file formats including DOCX, PDF, and CSV to provide more accurate and tailored responses.

The internal team knowledge combined with a dynamic web search and access to external tools will make your agent's unique skill set. You'll have full control over what the agent can do, whether it has access to the Internet or stays offline, and what integrations it can tap into.

Taskade AI is always available where you need it. It generates new projects inside your workspaces and folders, it offers guidance and advice in the AI Chat, and it acts as a personal assistant performing contextual actions and generating content on the project level.

AI Agents will be ubiquitous, working alongside you and your team at all times:

🔀 Prioritizing and assigning tasks

📅 Adding due dates and tracking deadlines

✅ Creating/completing tasks

⏰ Scheduling projects and meetings

As you grow and develop your workflow, agents will adjust and adapt, just like regular members of your team. They will be omnipresent but working in the background so you can focus on what truly matters - creating great products and services! ⭐

The promise of generative AI goes beyond routine task completion - it aims to redefine our relationship with technology. With autonomous AI systems, we're paving the way for the workforce of the future where AI works in a synergy with human intuition.

The potential is clear: greater efficiency, less manual work, and amplified productivity. And we're working hard to bring that future closer and help you achieve more with less effort.

Here's a bite-sized recap of what we learned today and what's on the horizon.

🤖 Limitations of LLMs: GPT-4 and other Large Language Models are powerful, but they still need human guidance. The future is looking at autonomous AI agents.

🌐 Overcoming Limitations: Existing voice assistants, although useful, operate in silos. The vision is to enable AIs to talk to each other and give them agency to get work done.

🚀 AI Agents Are the Future: Modern AI agents define, prioritize, and refine tasks for large language models. They're like an external decision-making engine for AI.

🤝 Revolutionizing Collaboration: In the near future, AI agents will collaborate with regular teams and with each other to complete tasks and manage workflows.

🧪 AI Research: Studies, like the one by Stanford and Google, show that AI agents can work together. This is just a glimpse into what's possible.

So, are you ready to start building your workforce of the future with Taskade AI?

🤖 Research, discover, and strategize with AI Agents living in your projects.

✏️ Think, brainstorm, create, and refine with the AI Assistant.

🪄 Supercharge your projects with the Workflow Generator.

💬 Chat to your projects and navigate the daily grind with the AI Chat.

⏩ Kickstart personal and business projects with thousands of AI Generators.

...and much more!

Create a Taskade account and unlock the full power of Taskade AI! 🤖⚡

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What Are AI Agents? The Future Of Workflow Automation

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Roll off dumpsters Orlando provide an open-top waste container for cleanup projects. They're suitable for residential and commercial applications.

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You're not alone. These are both popular choices with buyers.

Both are highly-rated, runner-friendly treadmills that are in direct competition with each other.

They both give you strong motors, extra long belts and solid, sturdy decks that are perfect for runners or walkers alike.

The Nordictrack 1750 was upgraded to an entirely new look and larger touch-screen - similar to a health club treadmill - a couple of years ago.

The Sole F80 was upgraded this year with a similar streamlined look, and a new touch-screen designed console.

Which treadmill is best for you? How do they differ?

This post will show you the key differences between the Nordictrack 1750 vs Sole F80 treadmill (2023 models) so you can find the best fit for your home.

Let's dive in:

Nordictrack 1750 vs Sole F80 - Price

Both treadmills fall into the "mid-range" price category and prices can jump around a bit.

However, as of this writing, the Sole F80 comes in a bit more affordable. You can get it around $1899 with Free Shipping direct from the Manufacturer here.

The Nordictrack 1750 treadmills comes in a few hundred more at around $2299 with Free Shipping direct from the Manufacturer here .

So strictly from a budget perspective, the Sole F80 comes out ahead here.

Nordictrack 1750 vs Sole F80 - Console

Both treadmills have full-color, touch-screen consoles.

Sole updated their console to an Android tablet-style screen this year and it measures 10-inches across as seen below:

You can use the screen to view your workout stats during your run.

Plus, you can also pair and mirror your phone or tablet to the screen.

This is handy since you can basically watch Netflix, Disney+, Youtube, etc (whatever you watch on your phone) on the console screen. And a lot of people love having this option.

However it's fair to note that several buyers have complained that they're having a lot of problems mirroring their devices to this console.

Some have complained that the tablet used is cheap quality and doesn't seem to work very well (you can read the latest reviews at the ).

This can be a common problem with newer upgraded treadmills since the manufacturer hasn't yet had time to work out the "kinks" in a new machine model yet.

Nordictrack also uses a full-color, touch-screen console. However the screen is larger than the Sole F80 at 14-inches across:

You can connect through this console directly to iFit - Nordictrack's online streaming service - and run famous world trails or even take fitness classes from the comfort of your own home.

So while you can't mirror your phone to the screen, you can connect with iFit, take fitness classes, run world trails, track your results etc.

Plus, because Nordictrack has been using this kind of console screen in their treadmills for years now, you don't have the issues with the screens that the Sole may be currently experiencing.

The Nordictrack 1750 screen is also separated from the console base. Because of this design, it also tilts and swivels - unlike the Sole F80 treadmill.

So you can find the best viewing angle for you - or just turn the screen if you want to continue your workout off the treadmill:

So is there a clear winner here? Not really - it depends on what you want.

The Sole F80 console is smaller - but it does allow you to pair your phone or tablet and mirror your favorite shows to the screen (in theory).

The Nordictrack 1750 screen is larger and more fun to watch. But it doesn't allow phone mirroring. It does however have the iFit option so you can run trails all ove the world - or take live fitness classes from your home. It also swivels - so you can watch it off the treadmill (if you're doing an iFit stretching class or weight training for example).

Nordictrack 1750 vs Sole F80 - Media Shelf

The Sole F80 treadmill does come with a media shelf above the console screen. This is a handy place for your phone or tablet.

So even if you can't mirror your phone to the actual screen, you can always use your tablet or phone on the media shelf to watch shows that way.

The Nordictrack 1750 treadmill does not have a media shelf.

Nordictrack 1750 vs Sole F80 - Incline/Decline

The Sole F80 gives you up to 15 levels of incline - which is a great way to challenge yourself and add high-intensity intervals to your workout.

The Nordictrack 1750 treadmill offers up to 12% incline . But since Sole measures their incline by "levels" and not percentage, it's hard to really say which treadmill is better and gives you the highest incline.

They both look very similar overall in terms of the highest level of incline.

There is one key difference however - decline.

The Nordictrack 1750 also comes with up to 3% decline. This is a great way to add extra crosstraining to your workout.

The treadmill also integrates with the iFit world trails to incline or decline automatically to match the terrain you're running - giving you a more immersive experience .

Nordictrack 1750 vs Sole F80 - Weight

The weight of a treadmill is a two-edged sword. All things being equal, in general, a heavier machine will feel more stable and solid.

On the other hand, it can also be more of a challenge to move than a ligher-weight machine.

The Sole F80 treadmill is lighter than older models - coming in around 274 pounds.

The Nordictrack 1750 treadmill is heavier - coming in around 340 pounds.

So overall the 1750 will probably feel a bit more solid and stable - but it will also be harder to move.

Both treadmills do fold up with wheels on the bottom to make moving them easier. But it is something to consider when comparing them.

Other Differences You Should Know

There are a few other minor difference when it comes to the Nordictrack 1750 vs Sole F80 treadmills.

The Sole F80 comes wireless phone charging pad on the console. While it doesn't speak to the actualy quality of the treadmill itself, a lot of people do like this feature.

The Nordictrack 1750 uses a Smart Response Drive system which basically helps the treadmill to respond faster in real time to any changes you make.

So it will also respond faster to any world trail incline/decline that you're running - to give a more immersive, seamless feel.

The Nordictrack 1750 also offers iFit online training with world trail runs, fitness classes, goal tracking etc. Compare this to Sole which has a Sole plus app with several built-in programs.

In terms of comparisons, the iFit online training program is much more advanced than the Sole plus program. This is mainly because it's been around for longer and Nordictrack has put a lot of money and effort into it.

With iFit, Nordictrack is focusing on giving people the ultimate "online experience" at home through their equipment. Sole seems to be focusing more on the product. Neither is better or worse - it just depends on what you want.

Bottom Line?

So which is better when comparing the Sole F80 vs Nordictrack 1750?

Both treadmills give you high-end construction, longer belts, strong motors and excellent cushioning. These are all things that runners especially will love.

The Sole F80 treadmill is currently more affordable. Plus you can also mirror your phone or tablet to the screen to watch shows - or even charge your phone on the console as you run.

The Nordictrack 1750 gives you a larger screen that also swivels.

Plus it offers the decline for more crosstraining options. And with iFit world trails or online classes you'll never run out of new things to try.

At the end of the day it really depends on what you want in a home treadmill, your budget and preferences.

Regardless of which you choose, you can learn more about each treadmill - and even get Free Shipping - with the links below.

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Nordictrack 1750 vs Sole F80 - Which is Best For You? (2022 Models)

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Trying to compare the Nordictrack 1750 vs Sole F80 treadmills? Here's a simple rundown of the key differences between the 2022 models to help you decide!

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One of the most common questions people have about the popular Nordictrack 1750 treadmill is:

Can You Watch TV on the Nordictrack 1750 Treadmill?

This makes sense - the 1750 looks like a health club treadmill with a large, HD touch-screen console, similar to a TV screen. You can stream iFit world trail runs or even take streaming or live fitness classes right from the screen.

However, as of this writing,

No you cannot watch TV from the Nordictrack 1750 treadmill screen console.

Nordictrack has not configured the software on the screen to allow this. (Not to say that some people haven't tried of course. But I wouldn't recommend this - you might mess up the iFit software on your screen. And if this happens, then you have an even bigger problem on your hands when the screen/iFit/fitness programs won't work).

So What Can You Do if You Want to Watch TV (or Netflix) on Your Nordictrack 1750 treadmill?

Not to worry - I have 2 easy solutions for you.

The first of course, is that you can always set up your Nordictrack 1750 treadmill in front of your home TV (like we used to do in the old days). Then just watch it from there with your TV remote in hand.

If that doesn't appeal to you - there's another option for you to consider.

Instead of the Nordictrack 1750 treadmill, you can always check out the the (seen below)

The EXP14i treadmill is similar to the Nordictrack 1750 - it has the same 14-inch touch screen where you can stream iFit programs and scenery.

It also has up to 12% incline - just like the 1750 model.

But there's a major difference here: Unlike the 1750 treadmill, the EXP14i treadmill has a media shelf!

So you can simply grab your tablet or phone and stream whatever TV shows you like through that using your apps - Netflix, Disney Plus, Hulu etc. This way, if you want to watch movies or TV shows you just do it with your tablet or phone using the media shelf on the EXP14i console.

Or if you want to stream the iFit world trails or classes - you can to that using the console screen.

Another bonus?

If you buy the Nordictrack EXP14i treadmill , it's currently about than the Nordictrack 1750 treadmill!

So this is a great way to get a solid Nordictrack treadmill with iFit - but still be able to stream your favorite TV shows and movies - for $600 less (not a bad deal).

The only downside?

The EXP14i treadmill does not have 3% decline - like you get with the 1750 treadmill. But for the price difference - some people may not mind that too much.

So overall if you're wondering " Can I watch TV on the Nordictrack 1750 treadmill? " then technically - no. But the Nordictrack EXP14i treadmill is a good workaround to consider here.

Want to know more about the Nordictrack EXP14i treadmill?

Check out the link below where you can see pictures of the console with media shelf, full treadmill specs and even get a special iFit deal on this treadmill:

Related:
"Do I Need to Buy iFit with the Nordictrack 1750 Treadmill?" (Updated 2020)

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Can You Watch TV on the Nordictrack 1750 Treadmill?

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Can you watch TV on the Nordictrack 1750 treadmill? Here's the answer - along with some solutions to help you watch TV or Neflix on your Nordictrack iFit treadmill!

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Introduction

SVM is a powerful supervised algorithm that works best on smaller datasets but on complex ones. Support Vector Machine, abbreviated as SVM can be used for both regression and classification tasks, but generally, they work best in classification problems. They were very famous around the time they were created, during the 1990s, and keep on being the go-to method for a high-performing algorithm with a little tuning.

By now, I hope you've now mastered Decision Trees, Random Forest, Naïve Bayes, K-nearest neighbor, and Ensemble Modelling techniques. If not, I would suggest you take out a few minutes and read about them as well.

In this article, I will explain to you What is SVM, how SVM Algorithm works, and the math intuition behind this crucial ML algorithm.
This article was published as a part of the Data Science Blogathon!
What is a Support Vector Machine?

It is a supervised machine learning problem where we try to find a hyperplane that best separates the two classes. Note: Don't get confused between SVM and logistic regression. Both the algorithms try to find the best hyperplane, but the main difference is logistic regression is a probabilistic approach whereas support vector machine is based on statistical approaches.

Now the question is which hyperplane does it select? There can be an infinite number of hyperplanes passing through a point and classifying the two classes perfectly. So, which one is the best?

Well, SVM does this by finding the maximum margin between the hyperplanes that means maximum distances between the two classes.

Logistic Regression vs Support Vector Machine

Depending on the number of features you have you can either choose Logistic Regression or SVM.

SVM works best when the dataset is small and complex. It is usually advisable to first use logistic regression and see how does it performs, if it fails to give a good accuracy you can go for SVM without any kernel (will talk more about kernels in the later section). Logistic regression and SVM without any kernel have similar performance but depending on your features, one may be more efficient than the other.

Types of Support Vector Machine Algorithms

1. Linear SVM

When the data is perfectly linearly separable only then we can use Linear SVM. Perfectly linearly separable means that the data points can be classified into 2 classes by using a single straight line(if 2D).

2. Non-Linear SVM

When the data is not linearly separable then we can use Non-Linear SVM, which means when the data points cannot be separated into 2 classes by using a straight line (if 2D) then we use some advanced techniques like kernel tricks to classify them. In most real-world applications we do not find linearly separable datapoints hence we use kernel trick to solve them.

Important Terms

Now let's define two main terms which will be repeated again and again in this article:

Support Vectors: These are the points that are closest to the hyperplane. A separating line will be defined with the help of these data points.

Margin: it is the distance between the hyperplane and the observations closest to the hyperplane (support vectors). In SVM large margin is considered a good margin. There are two types of margins hard margin and soft margin. I will talk more about these two in the later section.

How Does Support Vector Machine Work?

SVM is defined such that it is defined in terms of the support vectors only, we don't have to worry about other observations since the margin is made using the points which are closest to the hyperplane (support vectors), whereas in logistic regression the classifier is defined over all the points. Hence SVM enjoys some natural speed-ups.

Let's understand the working of SVM using an example. Suppose we have a dataset that has two classes (green and blue). We want to classify that the new data point as either blue or green.

To classify these points, we can have many decision boundaries, but the question is which is the best and how do we find it? NOTE: Since we are plotting the data points in a 2-dimensional graph we call this decision boundary a straight line but if we have more dimensions, we call this decision boundary a "hyperplane"

The best hyperplane is that plane that has the maximum distance from both the classes, and this is the main aim of SVM. This is done by finding different hyperplanes which classify the labels in the best way then it will choose the one which is farthest from the data points or the one which has a maximum margin.

Mathematical Intuition Behind Support Vector Machine

Many people skip the math intuition behind this algorithm because it is pretty hard to digest. Here in this section, we'll try to understand each and every step working under the hood. SVM is a broad topic and people are still doing research on this algorithm. If you are planning to do research, then this might not be the right place for you.

Here we will understand only that part that is required in implementing this algorithm. You must have heard about the primal formulation, dual formulation, Lagranges multiplier etc. I am not saying these topics aren't important, but they are more important if you are planning to do research in this area. Let's move ahead and see the magic behind this algorithm.

Before getting into the nitty-gritty details of this topic first let's understand what a dot product is.

Understanding Dot-Product

We all know that a vector is a quantity that has magnitude as well as direction and just like numbers we can use mathematical operations such as addition, multiplication. In this section, we will try to learn about the multiplication of vectors which can be done in two ways, dot product, and cross product. The difference is only that the dot product is used to get a scalar value as a resultant whereas cross-product is used to obtain a vector again.

The dot product can be defined as the projection of one vector along with another, multiply by the product of another vector.

Image 2

Here a and b are 2 vectors, to find the dot product between these 2 vectors we first find the magnitude of both the vectors and to find magnitude we use the Pythagorean theorem or the distance formula.

After finding the magnitude we simply multiply it with the cosine angle between both the vectors. Mathematically it can be written as:

A . B = |A| cosθ * |B|

Where |A| cosθ is the projection of A on B

And |B| is the magnitude of vector B

Now in SVM we just need the projection of A not the magnitude of B, I'll tell you why later. To just get the projection we can simply take the unit vector of B because it will be in the direction of B but its magnitude will be 1. Hence now the equation becomes:
A.B = |A| cosθ * unit vector of B
Now let's move to the next part and see how we will use this in SVM.

Use of Dot Product in SVM

Consider a random point X and we want to know whether it lies on the right side of the plane or the left side of the plane (positive or negative).

To find this first we assume this point is a vector (X) and then we make a vector (w) which is perpendicular to the hyperplane. Let's say the distance of vector w from origin to decision boundary is 'c'. Now we take the projection of X vector on w.

We already know that projection of any vector or another vector is called dot-product. Hence, we take the dot product of x and w vectors. If the dot product is greater than 'c' then we can say that the point lies on the right side. If the dot product is less than 'c' then the point is on the left side and if the dot product is equal to 'c' then the point lies on the decision boundary.

You must be having this doubt that why did we take this perpendicular vector w to the hyperplane? So what we want is the distance of vector X from the decision boundary and there can be infinite points on the boundary to measure the distance from. So that's why we come to standard, we simply take perpendicular and use it as a reference and then take projections of all the other data points on this perpendicular vector and then compare the distance.

In SVM we also have a concept of margin. In the next section, we will see how we find the equation of a hyperplane and what exactly do we need to optimize in SVM.

Margin in Support Vector Machine

We all know the equation of a hyperplane is w.x+b=0 where w is a vector normal to hyperplane and b is an offset.

To classify a point as negative or positive we need to define a decision rule. We can define decision rule as:

If the value of w.x+b>0 then we can say it is a positive point otherwise it is a negative point. Now we need (w,b) such that the margin has a maximum distance. Let's say this distance is 'd'.

To calculate 'd' we need the equation of L1 and L2. For this, we will take few assumptions that the equation of L1 is w.x+b=1 and for L2 it is w.x+b=-1.

Now the question comes

1. Why the magnitude is equal, why didn't we take 1 and -2?

2. Why did we only take 1 and -1, why not any other value like 24 and -100?

3. Why did we assume this line?

Let's try to answer these questions

1. We want our plane to have equal distance from both the classes that means L should pass through the center of L1 and L2 that's why we take magnitude equal.

2. Let's say the equation of our hyperplane is 2x+y=2, we observe that even if we multiply the whole equation with some other number the line doesn't change (try plotting on a graph). Hence for mathematical convenience, we take it as 1.

3. Now the main question is exactly why there's a need to assume only this line? To answer this, I'll try to take the help of graphs.

Suppose the equation of our hyperplane is 2x+y=2:

Let's create margin for this hyperplane,

If you multiply these equations by 10, we will see that the parallel line (red and green) gets closer to our hyperplane. For more clarity look at this graph ( https://www.desmos.com/calculator/dvjo3vacyp)

We also observe that if we divide this equation by 10 then these parallel lines get bigger. Look at this graph ( https://www.desmos.com/calculator/15dbwehq9g).

By this I wanted to show you that the parallel lines depend on (w,b) of our hyperplane, if we multiply the equation of hyperplane with a factor greater than 1 then the parallel lines will shrink and if we multiply with a factor less than 1, they expand.

We can now say that these lines will move as we do changes in (w,b) and this is how this gets optimized. But what is the optimization function? Let's calculate it.

We know that the aim of SVM is to maximize this margin that means distance (d). But there are few constraints for this distance (d). Let's look at what these constraints are.

Optimization Function and its Constraints

In order to get our optimization function, there are few constraints to consider. That constraint is that "We'll calculate the distance (d) in such a way that no positive or negative point can cross the margin line". Let's write these constraints mathematically:

Rather than taking 2 constraints forward, we'll now try to simplify these two constraints into 1. We assume that negative classes have y=-1 and positive classes have y=1.

We can say that for every point to be correctly classified this condition should always be true:

Suppose a green point is correctly classified that means it will follow w.x+b>=1, if we multiply this with y=1 we get this same equation mentioned above. Similarly, if we do this with a red point with y=-1 we will again get this equation. Hence, we can say that we need to maximize (d) such that this constraint holds true.

We will take 2 support vectors, 1 from the negative class and 2 nd from the positive class. The distance between these two vectors x1 and x2 will be (x2-x1) vector. What we need is, the shortest distance between these two points which can be found using a trick we used in the dot product. We take a vector 'w' perpendicular to the hyperplane and then find the projection of (x2-x1) vector on 'w'. Note: this perpendicular vector should be a unit vector then only this will work. Why this should be a unit vector? This has been explained in the dot-product section. To make this 'w' a unit vector we divide this with the norm of 'w'.

Finding Projection of a Vector on Another Vector Using Dot Product

We already know how to find the projection of a vector on another vector. We do this by dot-product of both vectors. So let's see how

Since x2 and x1 are support vectors and they lie on the hyperplane, hence they will follow yi* (2.x+b)=1 so we can write it as:

Putting equations (2) and (3) in equation (1) we get:

Hence the equation which we have to maximize is:

We have now found our optimization function but there is a catch here that we don't find this type of perfectly linearly separable data in the industry, there is hardly any case we get this type of data and hence we fail to use this condition we proved here. The type of problem which we just studied is called Hard Margin SVM now we shall study soft margin which is similar to this but there are few more interesting tricks we use in Soft Margin SVM.

Soft Margin SVM

In real-life applications we don't find any dataset which is linearly separable, what we'll find is either an almost linearly separable dataset or a non-linearly separable dataset. In this scenario, we can't use the trick we proved above because it says that it will function only when the dataset is perfectly linearly separable.

To tackle this problem what we do is modify that equation in such a way that it allows few misclassifications that means it allows few points to be wrongly classified.

We know that max[f(x)] can also be written as min[1/f(x)], it is common practice to minimize a cost function for optimization problems; therefore, we can invert the function.

To make a soft margin equation we add 2 more terms to this equation which is zeta and multiply that by a hyperparameter 'c'

For all the correctly classified points our zeta will be equal to 0 and for all the incorrectly classified points the zeta is simply the distance of that particular point from its correct hyperplane that means if we see the wrongly classified green points the value of zeta will be the distance of these points from L1 hyperplane and for wrongly classified redpoint zeta will be the distance of that point from L2 hyperplane.

So now we can say that our that are SVM Error = Margin Error + Classification Error. The higher the margin, the lower would-be margin error, and vice versa.

Let's say you take a high value of 'c' =1000, this would mean that you don't want to focus on margin error and just want a model which doesn't misclassify any data point.

Look at the figure below:

If someone asks you which is a better model, the one where the margin is maximum and has 2 misclassified points or the one where the margin is very less, and all the points are correctly classified?

Well, there's no correct answer to this question, but rather we can use SVM Error = Margin Error + Classification Error to justify this. If you don't want any misclassification in the model then you can choose figure 2. That means we'll increase 'c' to decrease Classification Error but if you want that your margin should be maximized then the value of 'c' should be minimized. That's why 'c' is a hyperparameter and we find the optimal value of 'c' using GridsearchCV and cross-validation.

Kernels in Support Vector Machine

The most interesting feature of SVM is that it can even work with a non-linear dataset and for this, we use "Kernel Trick" which makes it easier to classifies the points. Suppose we have a dataset like this:

Here we see we cannot draw a single line or say hyperplane which can classify the points correctly. So what we do is try converting this lower dimension space to a higher dimension space using some quadratic functions which will allow us to find a decision boundary that clearly divides the data points. These functions which help us do this are called Kernels and which kernel to use is purely determined by hyperparameter tuning.

Different Kernel Functions

Some kernel functions which you can use in SVM are given below:

1. Polynomial Kernel

Following is the formula for the polynomial kernel:

Here d is the degree of the polynomial, which we need to specify manually.

Suppose we have two features X1 and X2 and output variable as Y, so using polynomial kernel we can write it as:

So we basically need to find X ₁ 2 , X ₂ ² and X1.X2, and now we can see that 2 dimensions got converted into 5 dimensions.

2. Sigmoid Kernel

We can use it as the proxy for neural networks. Equation is:

It is just taking your input, mapping them to a value of 0 and 1 so that they can be separated by a simple straight line.

Image Source: https://dataaspirant.com/svm-kernels/#t-1608054630725

3. RBF Kernel

What it actually does is to create non-linear combinations of our features to lift your samples onto a higher-dimensional feature space where we can use a linear decision boundary to separate your classes It is the most used kernel in SVM classifications, the following formula explains it mathematically:

where,

1. 'σ' is the variance and our hyperparameter
2. || X₁ - X₂||is the Euclidean Distance between two points X₁ and X₂

4. Bessel function kernel

It is mainly used for eliminating the cross term in mathematical functions. Following is the formula of the Bessel function kernel:

5. Anova Kernel

It performs well on multidimensional regression problems. The formula for this kernel function is:

How to Choose the Right Kernel?

I am well aware of the fact that you must be having this doubt about how to decide which kernel function will work efficiently for your dataset. It is necessary to choose a good kernel function because the performance of the model depends on it.

Choosing a kernel totally depends on what kind of dataset are you working on. If it is linearly separable then you must opt. for linear kernel function since it is very easy to use and the complexity is much lower compared to other kernel functions. I'd recommend you start with a hypothesis that your data is linearly separable and choose a linear kernel function.

You can then work your way up towards the more complex kernel functions. Usually, we use SVM with RBF and linear kernel function because other kernels like polynomial kernel are rarely used due to poor efficiency. But what if linear and RBF both give approximately similar results? Which kernel do we choose now?

Example

Let's understand this with the help of an example, for simplicity I'll only take 2 features that mean 2 dimensions only. In the figure below I have plotted the decision boundary of a linear SVM on 2 features of the iris dataset:

Here we see that a linear kernel works fine on this dataset, but now let's see how will RBF kernel work.

We can observe that both the kernels give similar results, both work well with our dataset but which one should we choose? Linear SVM is a parametric model. A Parametric Model is a concept used to describe a model in which all its data is represented within its parameters. In short, the only information needed to predict the future from the current value is the parameters.

The complexity of the RBF kernel grows as the training data size increases. In addition to the fact that it is more expensive to prepare RBF kernel, we also have to keep the kernel matrix around, and the projection into this "infinite" higher dimensional space where the data becomes linearly separable is more expensive as well during prediction. If the dataset is not linear then using linear kernel doesn't make sense we'll get a very low accuracy if we do so.

So for this kind of dataset, we can use RBF without even a second thought because it makes decision boundary like this:

Implementation and hyperparameter tuning of Support Vector Machine in Python

For implementation on a dataset, we will be using the Income Evaluation dataset, which has information about an individual's personal life and an output of 50K or <=50. The dataset can be found here ( https://www.kaggle.com/lodetomasi1995/income-classification)

The task here is to classify the income of an individual when given the required inputs about his personal life.

First, let's import all required libraries.

# Import all relevant libraries from sklearn.svm import SVC import numpy as np import pandas as pd from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, confusion_matrix from sklearn import preprocessing import warnings warnings.filterwarnings("ignore") Now let's read the dataset and look at the columns to understand the information better. df = pd.read_csv('income_evaluation.csv')

df.head()

I have already done the data preprocessing part and you can look whole code here. Here my main aim is to tell you how to implement SVM on python.

Now for training and testing our model, the data has to be divided into train and test data.
We will also scale the data to lie between 0 and 1.

# Split dataset into test and train data

Now let's go ahead with defining the Support vector Classifier along with its hyperparameters. Next, we will fit this model on the training data.

#Define support vector classifier with hyperparameters

X_train, X_test, y_train, y_test = train_test_split(df.drop('income', axis=1),df['income'], test_size=0.2)

svc.fit(X_train,y_train)

svc = SVC(random_state=101)

accuracies = cross_val_score(svc,X_train,y_train,cv=5)

print("Train Score:"np.mean(accuracies))

printf("Test Score:"svc.score(X_test,y_test))

The model has been trained and we can now observe the outputs as well.

Below, you can see the accuracy of the test and train dataset

You can even hyper tune your model by the following code:

grid = {

    'C':[0.01,0.1,1,10],

    'kernel' : ["linear","poly","rbf","sigmoid"],

    'degree' : [1,3,5,7],

    'gamma' : [0.01,1]

}

 svm  = SVC ()

svm_cv = GridSearchCV(svm, grid, cv = 5)

svm_cv.fit(X_train,y_train)

print("Best Parameters:",svm_cv.best_params_)

print("Train Score:",svm_cv.best_score_)

print("Test Score:",svm_cv.score(X_test,y_test))

The dataset is pretty big and hence it will take time to get trained, for this reason, I can't paste the result of the above code here because SVM doesn't perform well with big datasets, it takes a long time to get trained.

Advantages of SVM

SVM works better when the data is Linear

It is more effective in high dimensions

With the help of the kernel trick, we can solve any complex problem

SVM is not sensitive to outliers

Can help us with Image classification

Disadvantages of SVM

It doesn't show good results on a big dataset

The SVM hyperparameters are Cost -C and gamma. It is not that easy to fine-tune these hyper-parameters. It is hard to visualize their impact

End Notes

In this article, we looked at a very powerful machine learning algorithm, Support Vector Machine in detail. I discussed its concept of working, math intuition behind SVM, implementation in python, the tricks to classify non-linear datasets, Pros and cons, and finally, we solved a problem with the help of SVM.
The media shown in this article are not owned by Analytics Vidhya and are used at the Author's discretion.
Frequently Asked Questions

Related

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Guide on Support Vector Machine (SVM) Algorithm

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Support Vector Machine or SVM, is a powerful supervised algorithm that works best on smaller datasets but on complex ones.

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In this article, we will understand the concept of Large Language Models (LLMs) and their importance in natural language processing.

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Introduction

"Skewness essentially is a commonly used measure in descriptive statistics that characterizes the asymmetry of a data distribution, while kurtosis determines the heaviness of the distribution tails."

Understanding the shape of data is crucial while practicing data science. It helps to understand where the most information lies and analyze the outliers in a given data. In this article, we'll learn about the shape of data, the importance of skewness, and kurtosis in statistics. The types of skewness and kurtosis and Analyze the shape of data in the given dataset. Let's first understand what skewness and kurtosis is.
Learning Objectives

In this article, you will learn about Skewness and its different types.

You will learn how to calculate the Skewness Coefficient.

This article will also help you learn about Kurtosis and its type.

What Is Skewness?

Skewness is a statistical measure that assesses the asymmetry of a probability distribution. It quantifies the extent to which the data is skewed or shifted to one side.

Positive skewness indicates a longer tail on the right side of the distribution, while negative skewness indicates a longer tail on the left side. Skewness helps in understanding the shape and outliers in a dataset.

If the values of a specific independent variable (feature) are skewed, depending on the model, skewness may violate model assumptions or may reduce the interpretation of feature importance.
In statistics, skewness is a degree of asymmetry observed in a probability distribution that deviates from the symmetrical normal distribution (bell curve) in a given set of data.
A skewed data set, typical values fall between the first quartile (Q1) and the third quartile (Q3).

The normal distribution helps to know a skewness. When we talk about normal distribution, data symmetrically distributed. The symmetrical distribution has zero skewness as all measures of a central tendency lies in the middle.

When data is symmetrically distributed, the left-hand side, and right-hand side, contain the same number of observations. (If the dataset has 90 values, then the left-hand side has 45 observations, and the right-hand side has 45 observations.). But, what if not symmetrical distributed? That data is called asymmetrical data, and that time skewness
comes into the picture.

Types of Skewness

Positive Skewed or Right-Skewed (Positive Skewness)

In statistics, a positively skewed or right-skewed distribution has a long right tail. It is a sort of distribution where the measures are dispersing, unlike symmetrically distributed data where all measures of the central tendency (mean, median, and mode) equal each other. This makes Positively Skewed Distribution a type of distribution where the mean, median, and mode of the distribution are positive rather than negative or zero.

In positively skewed, the mean of the data is greater than the median (a large number of data-pushed on the right-hand side). In other words, the results are bent towards the lower side. The mean will be more than the median as the median is the middle value and mode is always the most frequent value.

Extreme positive skewness is not desirable for a distribution, as a high level of skewness can cause misleading results. The data transformation tools are helping to make the skewed data closer to a normal distribution. For positively skewed distributions, the famous transformation is the log transformation. The log transformation proposes the calculations of the natural logarithm for each value in the dataset.

Negative Skewed or Left-Skewed (Negative Skewness)

A negatively skewed or left-skewed distribution has a long left tail; it is the complete opposite of a positively skewed distribution. In statistics, negatively skewed distribution refers to the distribution model where more values are plots on the right side of the graph, and the tail of the distribution is spreading on the left side.

In negatively skewed, the mean of the data is less than the median (a large number of data-pushed on the left-hand side). Negatively Skewed Distribution is a type of distribution where the mean, median, and mode of the distribution are negative rather than positive or zero.

Median is the middle value, and mode is the most frequent value. Due to an unbalanced distribution, the median will be higher than the mean.

How to Calculate the Skewness Coefficient?

Skewness can be calculated using various methods, whereas the most commonly used method is Pearson's coefficient.
Pearson's first coefficient of skewness
To calculate skewness values, subtract the mode from the mean, and then divide the difference by standard deviation.

As Pearson's correlation coefficient differs from -1 (perfect negative linear relationship) to +1 (perfect positive linear relationship), including a value of 0 indicating no linear relationship, When we divide the covariance values by the standard deviation, it truly scales the value down to a limited range of -1 to +1. That accurately shows the range of the correlation values.

Pearson's first coefficient of skewness is helping if the data present high mode. But, if the data have low mode or various modes, Pearson's first coefficient is not preferred, and Pearson's second coefficient may be superior, as it does not rely on the mode.
Rule of thumb :
If the skewness is between -0.5 & 0.5, the data are nearly symmetrical.
If the skewness is between -1 & -0.5 (negative skewed) or between 0.5 & 1(positive skewed), the data are slightly skewed.
If the skewness is lower than -1 (negative skewed) or greater than 1 (positive skewed), the data are extremely skewed.
Pearson's second coefficient of skewness
subtract the median from the mean, multiply the difference by 3, and divide the product by the standard deviation.

Excess kurtosis = Kurt - 3
What Is Kurtosis?

Kurtosis is a statistical measure that quantifies the shape of a probability distribution. It provides information about the tails and peakedness of the distribution compared to a normal distribution.

Positive kurtosis indicates heavier tails and a more peaked distribution, while negative kurtosis suggests lighter tails and a flatter distribution. Kurtosis helps in analyzing the characteristics and outliers of a dataset.

The measure of Kurtosis refers to the tailedness of a distribution. Tailedness refers to how often the outliers occur.

Peakedness in a data distribution is the degree to which data values are concentrated around the mean. Datasets with high kurtosis tend to have a distinct peak near the mean, decline rapidly, and have heavy tails. Datasets with low kurtosis tend to have a flat top near the mean rather than a sharp peak.

In finance, kurtosis is used as a measure of financial risk. A large kurtosis is associated with a high level of risk for an investment because it indicates that there are high probabilities of extremely large and extremely small returns. On the other hand, a small kurtosis signals a moderate level of risk because the probabilities of extreme returns are relatively low.

What Is Excess Kurtosis?

The excess kurtosis is used in statistics and probability theory to compare the kurtosis coefficient with that normal distribution. Excess kurtosis can be positive (Leptokurtic distribution), negative (Platykurtic distribution), or near zero (Mesokurtic distribution). Since normal distributions have a kurtosis of 3, excess kurtosis is calculated by subtracting kurtosis by 3.

Types of Excess Kurtosis

1. Leptokurtic or heavy-tailed distribution (kurtosis more than normal distribution).

2. Mesokurtic (kurtosis same as the normal distribution).

3. Platykurtic or short-tailed distribution (kurtosis less than normal distribution).

Leptokurtic (Kurtosis > 3)

Leptokurtic has very long and thick tails, which means there are more chances of outliers. Positive values of kurtosis indicate that distribution is peaked and possesses thick tails. Extremely positive kurtosis indicates a distribution where more numbers are located in the tails of the distribution instead of around the mean.
Key Takeaways
Platykurtic (Kurtosis < 3)

Platykurtic having a thin tail and stretched around the center means most data points are present in high proximity to the mean. A platykurtic distribution is flatter (less peaked) when compared with the normal distribution.
The media shown in this article on skewness and Kurtosis are not owned by Analytics Vidhya and is used at the Author's discretion.
Mesokurtic (Kurtosis = 3)

Mesokurtic is the same as the normal distribution, which means kurtosis is near 0. In Mesokurtic, distributions are moderate in breadth, and curves are a medium peaked height.

Conclusion

The skewness is a measure of symmetry or asymmetry of data distribution, and kurtosis measures whether data is heavy-tailed or light-tailed in a distribution. Data can be positive-skewed (data-pushed towards the right side) or negative-skewed (data-pushed towards the left side).

When data is skewed, the tail region may behave as an outlier for the statistical model, and outliers unsympathetically affect the model's performance, especially regression-based models. Some statistical models are robust to outliers like Tree-based models, but it will limit the possibility of trying other models. So there is a necessity to transform the skewed data to be close enough to a Normal distribution.

Skewness is a statistical measure of the asymmetry of a probability distribution. It characterizes the extent to which the distribution of a set of values deviates from a normal distribution.

Skewness between -0.5 and 0.5 is symmetrical.

Kurtosis measures whether data is heavily heavy-tailed or light-tailed.

Data sets with high kurtosis have heavy tails and more outliers, while data sets with low kurtosis tend to have light tails and fewer outliers.

Excess kurtosis can be positive (Leptokurtic distribution), negative (Platykurtic distribution), or near zero (Mesokurtic distribution).

Frequently Asked Questions

Related

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Skewness and Kurtosis: Quick Guide (Updated 2023)

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Calculate skewness and kurtosis, identify the different types of distributions and understand the implications of these statistical measures.

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What is Random Forest Algorithm?

Random Forest is one of the most popular and commonly used algorithms by Data Scientists. Random forest is a Supervised Machine Learning Algorithm that is used widely in Classification and Regression problems. It builds decision trees on different samples and takes their majority vote for classification and average in case of regression.

Random forest is a versatile machine learning algorithm developed by Leo Breiman and Adele Cutler. It leverages an ensemble of multiple decision trees to generate predictions or classifications. By combining the outputs of these trees, the random forest algorithm delivers a consolidated and more accurate result.

Its widespread popularity stems from its user-friendly nature and adaptability, enabling it to tackle both classification and regression problems effectively. The algorithm's strength lies in its ability to handle complex datasets and mitigate overfitting, making it a valuable tool for various predictive tasks in machine learning.

One of the most important features of the Random Forest Algorithm is that it can handle the data set containing continuous variables, as in the case of regression, and categorical variables, as in the case of classification. It performs better for classification and regression tasks. In this tutorial, we will understand the working of random forest and implement random forest on a classification task.
Learning Objectives

Learn the working of random forest with an example

Understand the impact of different hyperparameters in random forest

Implement Random Forest on a classification problem using scikit-learn

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

Real-Life Analogy of Random Forest

Let's dive into a real-life analogy to understand this concept further. A student named X wants to choose a course after his 10+2, and he is confused about the choice of course based on his skill set. So he decides to consult various people like his cousins, teachers, parents, degree students, and working people. He asks them varied questions like why he should choose, job opportunities with that course, course fee, etc. Finally, after consulting various people about the course he decides to take the course suggested by most people.

Working of Random Forest Algorithm

Before understanding the working of the random forest algorithm in machine learning, we must look into the ensemble learning technique. Ensemble simplymeans combining multiple models. Thus a collection of models is used to make predictions rather than an individual model.

Ensemble uses two types of methods:

1. Bagging - It creates a different training subset from sample training data with replacement & the final output is based on majority voting. For example, Random Forest.

2. Boosting - It combines weak learners into strong learners by creating sequential models such that the final model has the highest accuracy. For example, ADA BOOST, XG BOOST.

As mentioned earlier, Random forest works on the Bagging principle. Now let's dive in and understand bagging in detail.

Bagging

Bagging, also known as Bootstrap Aggregation, is the ensemble technique used by random forest.Bagging chooses a random sample/random subset from the entire data set. Hence each model is generated from the samples (Bootstrap Samples) provided by the Original Data with replacement known as row sampling. This step of row sampling with replacement is called bootstrap. Now each model is trained independently, which generates results. The final output is based on majority voting after combining the results of all models. This step which involves combining all the results and generating output based on majority voting, is known as aggregation.

Now let's look at an example by breaking it down with the help of the following figure. Here the bootstrap sample is taken from actual data (Bootstrap sample 01, Bootstrap sample 02, and Bootstrap sample 03) with a replacement which means there is a high possibility that each sample won't contain unique data. The model (Model 01, Model 02, and Model 03) obtained from this bootstrap sample is trained independently. Each model generates results as shown. Now the Happy emoji has a majority when compared to the Sad emoji. Thus based on majority voting final output is obtained as Happy emoji.

Boosting

Boosting is one of the techniques that use the concept of ensemble learning. A boosting algorithm combines multiple simple models (also known as weak learners or base estimators) to generate the final output. It is done by building a model by using weak models in series.

There are several boosting algorithms; AdaBoost was the first really successful boosting algorithm that was developed for the purpose of binary classification. AdaBoost is an abbreviation for Adaptive Boosting and is a prevalent boosting technique that combines multiple "weak classifiers" into a single "strong classifier." There are Other Boosting techniques. For more, you can visit

Steps Involved in Random Forest Algorithm

Step 1: In the Random forest model, a subset of data points and a subset of features is selected for constructing each decision tree. Simply put, n random records and m features are taken from the data set having k number of records.

Step 2: Individual decision trees are constructed for each sample.

Step 3: Each decision tree will generate an output.

Step 4: Final output is considered based on Majority Voting or Averaging for Classification and regression, respectively.

For example: consider the fruit basket as the data as shown in the figure below. Now n number of samples are taken from the fruit basket, and an individual decision tree is constructed for each sample. Each decision tree will generate an output, as shown in the figure. The final output is considered based on majority voting. In the below figure, you can see that the majority decision tree gives output as an apple when compared to a banana, so the final output is taken as an apple.

Important Features of Random Forest

Diversity: Not all attributes/variables/features are considered while making an individual tree; each tree is different.

Immune to the curse of dimensionality: Since each tree does not consider all the features, the feature space is reduced.

Parallelization: Each tree is created independently out of different data and attributes. This means we can fully use the CPU to build random forests.

Train-Test split: In a random forest, we don't have to segregate the data for train and test as there will always be 30% of the data which is not seen by the decision tree.

Stability: Stability arises because the result is based on majority voting/ averaging.

Difference Between Decision Tree and Random Forest

Random forest is a collection of decision trees; still, there are a lot of differences in their behavior.

Decision trees Random Forest

1. Decision trees normally suffer from the problem of overfitting if it's allowed to grow without any control.

1. Random forests are created from subsets of data, and the final output is based on average or majority ranking; hence the problem of overfitting is taken care of.

2. A single decision tree is faster in computation.

2. It is comparatively slower.

3. When a data set with features is taken as input by a decision tree, it will formulate some rules to make predictions.

3. Random forest randomly selects observations, builds a decision tree, and takes the average result. It doesn't use any set of formulas.

Thus random forests are much more successful than decision trees only if the trees are diverse and acceptable.

Important Hyperparameters in Random Forest

Hyperparameters are used in random forests to either enhance the performance and predictive power of models or to make the model faster.

Hyperparameters to Increase the Predictive Power

n_estimators: Number of trees the algorithm builds before averaging the predictions.

max_features: Maximum number of features random forest considers splitting a node.

mini_sample_leaf: Determines the minimum number of leaves required to split an internal node.

criterion: How to split the node in each tree? (Entropy/Gini impurity/Log Loss)

max_leaf_nodes: Maximum leaf nodes in each tree

Hyperparameters to Increase the Speed

n_jobs: it tells the engine how many processors it is allowed to use. If the value is 1, it can use only one processor, but if the value is -1, there is no limit.

random_state: controls randomness of the sample. The model will always produce the same results if it has a definite value of random state and has been given the same hyperparameters and training data.

oob_score: OOB means out of the bag. It is a random forest cross-validation method. In this, one-third of the sample is not used to train the data; instead used to evaluate its performance. These samples are called out-of-bag samples.

Coding in Python - Random Forest

Now let's implement Random Forest in scikit-learn.

1. Let's import the libraries.

# Importing the required libraries
import pandas as pd, numpy as np
import matplotlib.pyplot as plt, seaborn as sns
%matplotlib inline

2. Import the dataset.
Python Code:

3. Putting Feature Variable to X and Target variable to y.

# Putting feature variable to X
X = df.drop('heart disease',axis=1)
# Putting response variable to y
y = df['heart disease']

4. Train-Test-Split is performed

# now lets split the data into train and test
from sklearn.model_selection import train_test_split

# Splitting the data into train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=42)
X_train.shape, X_test.shape

5. Let's import RandomForestClassifier and fit the data.

from sklearn.ensemble import RandomForestClassifier

classifier_rf = RandomForestClassifier(random_state=42, n_jobs=-1, max_depth=5,
                                       n_estimators=100, oob_score=True)

%%time
classifier_rf.fit(X_train, y_train)

# checking the oob score
classifier_rf.oob_score_

6. Let's do hyperparameter tuning for Random Forest using GridSearchCV and fit the data.

rf = RandomForestClassifier(random_state=42, n_jobs=-1)

params = {
    'max_depth': [2,3,5,10,20],
    'min_samples_leaf': [5,10,20,50,100,200],
    'n_estimators': [10,25,30,50,100,200]
}

from sklearn.model_selection import GridSearchCV

# Instantiate the grid search model
grid_search = GridSearchCV(estimator=rf,
                           param_grid=params,
                           cv = 4,
                           n_jobs=-1, verbose=1, scoring="accuracy")

%%time
grid_search.fit(X_train, y_train)

grid_search.best_score_

rf_best = grid_search.best_estimator_
rf_best

From hyperparameter tuning, we can fetch the best estimator, as shown. The best set of parameters identified was max_depth=5, min_samples_leaf=10,n_estimators=10

7. Now, let's visualize

from sklearn.tree import plot_tree
plt.figure(figsize=(80,40))
plot_tree(rf_best.estimators_[5], feature_names = X.columns,class_names=['Disease', "No Disease"],filled=True);

from sklearn.tree import plot_tree
plt.figure(figsize=(80,40))
plot_tree(rf_best.estimators_[7], feature_names = X.columns,class_names=['Disease', "No Disease"],filled=True);

The trees created by estimators_[5] and estimators_[7] are different. Thus we can say that each tree is independent of the other.

8. Now let's sort the data with the help of feature importance

rf_best.feature_importances_

imp_df = pd.DataFrame({
    "Varname": X_train.columns,
    "Imp": rf_best.feature_importances_
})

imp_df.sort_values(by="Imp", ascending=False)

Random Forest Algorithm Use Cases

This algorithm is widely used in E-commerce, banking, medicine, the stock market, etc.

For example: In the Banking industry, it can be used to find which customer will default on a loan.

Advantages and Disadvantages of Random Forest Algorithm

Advantages

1. It can be used in classification and regression problems.

2. It solves the problem of overfitting as output is based on majority voting or averaging.

3. It performs well even if the data contains null/missing values.

4. Each decision tree created is independent of the other; thus, it shows the property of parallelization.

5. It is highly stable as the average answers given by a large number of trees are taken.

6. It maintains diversity as all the attributes are not considered while making each decision tree though it is not true in all cases.

7. It is immune to the curse of dimensionality. Since each tree does not consider all the attributes, feature space is reduced.

8. We don't have to segregate data into train and test as there will always be 30% of the data, which is not seen by the decision tree made out of bootstrap.

Disadvantages

1. Random forest is highly complex compared to decision trees, where decisions can be made by following the path of the tree.

2. Training time is more than other models due to its complexity. Whenever it has to make a prediction, each decision tree has to generate output for the given input data.

Conclusion

Random forest is a great choice if anyone wants to build the model fast and efficiently, as one of the best things about the random forest is it can handle missing values. It is one of the best techniques with high performance, widely used in various industries for its efficiency. It can handle binary, continuous, and categorical data. Overall, random forest is a fast, simple, flexible, and robust model with some limitations.
Key Takeaways

Random forest algorithm is an ensemble learning technique combining numerous classifiers to enhance a model's performance.

Random Forest is a supervised machine-learning algorithm made up of decision trees.

Random Forest is used for both classification and regression problems.

Frequently Asked Questions
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Related

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Understand Random Forest Algorithms With Examples (Updated 2023)

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Learn to use Random Forest algorithms in real-world. Tips to implement this machine learning technique, advantages of using it, and more.

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How Long do Batteries Last in Wireless Security Cameras

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How Long do Batteries Last in Wireless Security Cameras

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When comparing wireless security cameras for your home or business, battery life is an important factor to consider. The effectiveness and convenience of your security system depend greatly on it. To help you make an informed decision, we'll explore the typical battery life of wireless security cameras and factors that may impact their longevity.

Battery life in wireless security cameras can vary significantly depending on the camera's segments, model, and usage. Generally, the batteries can last up to six months on a single charge. However, the average time for most security cameras is about two to three months, especially in high-traffic areas.

Battery Life of Popular Wireless Security Camera Brands

Blink

Blink security cameras are known for their simplicity, affordability, and impressive battery life. What is the Blink Camera battery life? The batteries in Blink cameras can last up to two years, making them highly desirable for users who want a low-maintenance security solution.

Eufy

Eufy camera batteries are also quite reliable, with a life of up to one year. This means you won't need to constantly worry about changing the batteries in your Eufy camera, allowing you to focus more on keeping your home secure and monitored.

Arlo

Arlo cameras also offer solid battery life, ranging from 3 to 6 months, depending on usage, temperature, and Wi-Fi connection strength. Despite the shorter battery life compared to some competing brands, Arlo cameras are still favored for their advanced features and reliable performance.

Ring Stick Up Cam

The Ring Stick Up Cam is another popular security camera brand with a battery life typically lasting 6 to 12 months. Like Arlo, Ring cameras provide numerous advanced features, making them a popular choice for those willing to sacrifice longer battery life in exchange for a more feature-rich security solution.

Types of Batteries

Wireless security cameras have two main types of batteries: non-rechargeable and rechargeable batteries.

Non-Rechargeable Batteries

Non-rechargeable batteries, also known as primary batteries, are single-use batteries that must be replaced when their charge is depleted. Alkaline batteries are an example of non-rechargeable batteries. Wireless security cameras that use non-rechargeable batteries require replacement after a certain period of usage, which can range from a few months to a couple of years, depending on the camera model and usage patterns.

Advantages of using non-rechargeable batteries in your wireless security cameras:

Long shelf life, allowing you to store extra batteries for future use

Relatively affordable and widely available in the market

However, there are also some disadvantages:

Need for periodic replacement, which might increase maintenance costs

Environmental impact due to the disposal of used batteries

Rechargeable Batteries

Rechargeable or secondary batteries can be recharged after their charge has been depleted. This type of battery is commonly found in devices like smartphones and laptops. Rechargeable lithium-ion (Li-ion) batteries are often used in wireless security cameras due to their high energy density, long lifespan, and efficient charging capabilities.

Advantages of using rechargeable batteries in your wireless security cameras:

Cost-effective over time, as they can be recharged multiple times

Environmentally friendly due to reduced battery waste

Some disadvantages of using rechargeable batteries include:

Higher initial cost compared to non-rechargeable batteries

Gradually decrease in battery capacity over time with each charge cycle

Whether you choose non-rechargeable or rechargeable batteries for your wireless security cameras depends on your preferences, budget, and desired maintenance level. Each type of battery has advantages and disadvantages, so consider how they align with your specific needs and your camera's requirements when deciding.

Factors Affecting Battery Life

Weather

Temperature plays a crucial role in the battery life of wireless security cameras. Extreme temperatures, and freezing weather (below -4°F /-20°C), can significantly impact the performance and lifespan of lithium batteries, which are commonly used in these cameras. In low-temperature conditions, the chemical reactions within the battery slow down, leading to reduced capacity. Keeping your camera within the recommended temperature range is essential to ensure optimal battery performance.

Usage

The frequency of usage is directly related to battery life. Cameras set to record continuously drain the battery much faster than those recorded based on motion detection. To maximize battery life, consider using motion detection settings that reduce recording time when there is little activity. Night vision mode, which often requires additional power for infrared LEDs, can also consume more battery.

Features

Certain features in wireless security cameras can affect battery life. For instance, high video quality settings, such as higher resolution and frame rate, require more power, which may lead to faster battery drain. Using audio features, like two-way talk, can also impact battery life. To optimize battery performance, take time to select the features you actually use and need.

Connectivity

The strength of the Wi-Fi signal and the internet connection can impact battery performance. A weak signal may force the camera to work harder to maintain connectivity, which can consume more battery power. To extend and maintain battery life, ensure that your camera is positioned within optimal range from the Wi-Fi router and that your internet connection is stable. Use a mesh Wi-Fi system or range extenders to improve camera signal strength.

Extending Battery Life

Rechargeable Batteries

When choosing a wireless security camera, opt for a high battery capacity (e.g., 5000 mAh or higher) to ensure longer battery life between charges. Rechargeable batteries can last up to 6 months per charge. However, in high-traffic areas, they might last around 2 to 3 months.

To maximize the battery life of your security cameras, make sure you don't overcharge them by using a power adapter with a lower voltage. Also, avoid consistently letting the battery drain completely, which can shorten its lifespan.

Solar Power

Using solar power as a renewable energy source is an effective way to extend the battery life of your security camera. Some camera models come with a solar panel, while others may require purchasing a compatible one separately.

Installing a solar panel ensures the camera battery charges as needed, reducing the need for frequent manual recharging. Consider the amount of sunlight your camera's location receives and position the solar panel to maximize sunlight exposure for optimal charging.

Maintenance

Regular maintenance is important to prolong the battery life of your security cameras. Follow these maintenance tips:

1. Clean the camera lens and solar panel (if applicable) as needed to ensure optimal performance. Dust and dirt can negatively impact your camera's efficiency and solar charging capacity.

2. Periodically check the battery status and charge it before it reaches a critically low level.

3. Update the camera's firmware as recommended by the manufacturer to ensure efficient power consumption and optimal performance.

4. Adjust the camera's settings, such as motion detection sensitivity and recording duration, to minimize power usage without compromising security.

By following these tips and taking advantage of technologies like rechargeable batteries and solar power, you can effectively extend the battery life of your wireless security cameras.

Conclusion

The battery life of wireless security cameras can vary greatly, ranging from a few weeks to several months. Several factors influence the duration of battery life, including temperature, number of motion detections, video quality, and Wi-Fi connection strength.

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How Long Do Batteries Last in Wireless Security Cameras?

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When comparing wireless security cameras for your home or business, battery life is an important factor to consider. The effectiveness and convenience of your security system depend greatly on it. To help you make an

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Wondering which one is the best option for you?

The Nordictrack EXP 14i treadmill is the top model in the EXP series of treadmills. It's already gaining popularity since it offers some commercial-grade features for a mid-range price.

The Commercial 1750 has been around for years. And it's one of the most popular Nordictrack treadmills of all time.

Nordictrack regularly updates this model - and has updated the Commercial 1750 this year with some impressive new features.

The "Commercial" series of treadmills are geared more towards runners and have some stronger construction than Nordictrack's other series (longer decks, stronger motors etc.).

Both treadmills give you impressive touch-screen, full color consoles where you can stream iFit workouts, run famous world trails and even take LIVE fitness classes from the comfort of your own home.

Both models also fold up for easy storage and cleaning.

So what's the difference between these two treadmills? And which is best for you?

This post will show you the main differences between the Nordictrack EXP14i vs 1750 treadmill to help you make a more informed decision.

Nordictrack EXP14i vs 1750: Price

As of this writing the Nordictrack EXP14i is the more affordable of the two models. You can get it direct for around

The Nordictrack Commercial 1750 treadmill comes in higher - around

This makes sense - the Commercial 1750 is part of the Commercial series with stronger construction than the EXP treadmill - and a few more perks.

But if you're on a budget, the EXP 14i treadmill has the edge here.

Nordictrack EXP 14i or 1750: Console

This is where you see a bit of a difference. Both treadmills give you impressive, full-color, HD touch-screens that stream iFit classes and world trail workouts.

They also both measure 14-inches across . The generous-sized screen makes it more fun and enjoyable to watch the iFit workouts - and to read your workout stats.

However there's a difference in the layout. Here's the Nordictrack EXP14i console :

And here's the Nordictrack 1750 console:

As you can see, the Nordictrack 1750 console sits more upright and is a bit more substantial looking than the EXP14i Console.

It also gives you something that the EXP14i Console does not - you can turn it 180 degrees.

So if you want to get off the treadmill and continue your workout off the treadmill, you can turn this console easily and watch it from the floor:

The EXP14i treadmill console does not turn and swivel - however it does tilt forward and back. So you can find a good viewing angle when you're on the treadmill.

So while the screen sizes on these two treadmills are the same - the layouts are a bit different.

Nordictrack 1750 vs EXP14i: Media Shelf

You may have noticed from looking at the consoles above that the EXP14i treadmill has something that the Nordictrack 1750 treadmill does not have: a media shelf in front of the screen.

You can set up your tablet or phone on the media shelf and stream Netflix shows or watch Youtube videos this way as you walk.

It may seem like a small thing - but a lot of people really like it. It gives you another way to add some entertainment to your workouts.

Nordictrack 1750 vs EXP14i: Construction

Both treadmills come with strong motors, tough decks and the ability to fold up.

However, there are some key differences you need to know in regards to overall construction. As stated above, the "Commercial" series of treadmills was built tougher with stronger construction than the EXP models. They are really geared more towards runners.

One example? Motor power

The Nordictrack EXP14i comes with a 3.0 CHP motor - which is very good.

But the Nordictrack 1750 treadmill goes one better with a 3.5 CHP motor - which is better.

You'll also notice that the Nordictrack 1750 is heavier - coming in at . The Nordictrack EXP14i treadmill is a bit lighter at

A heavier treadmill, other things being equal, is generally a more stable treadmill . The extra weight helps anchor the machine - so when you pick up the pace there's less shaking or wobbling.

So while both treadmills are heavy and stable - the Nordictrack 1750 has the edge here. It will be especially handy if you're a runner.

Nordictrack EXP14i vs 1750: Decline

Both treadmills give you up to 12% incline - which is a great way to add challenge to your workout.

However the Nordictrack 1750 gives you decline as well - up to 3% decline.

So you have even more ways to crosstrain and add some variety to your workout.

The iFit also integrates with incline and decline to move the treadmill according to the terrain that you're running on the screen (very virtual reality). So having the decline on the Commercial 1750 treadmill gives you a bit more of an immersive experience to enjoy.

Nordictrack 1750 vs EXP 14i - Belt Size

The Nordictrack EXP14i treadmill gives you the a 20 x 55 inch running belt - which is standard and should be fine for more people.

The Nordictrack 1750 treadmills gives you a longer 20 x 60 inch running belt. So you have more room to stretch out and run if you prefer.

Bottom Line?

So what's the bottom line when comparing the Nordictrack EXP14i vs 1750 Commercial treadmill?

Each one has their benefits.

The Nordictrack EXP 14i is more affordable - making it better if you're on a budget. Plus it has everything you need for a fantastic workout including the HD, touch-screen and iFit streaming, strong motor and incline options.

The Nordictrack 1750 however includes a few more extras.

You get the different screen layout along with more runner-friendly construction and a more stable feeling machine. You also get a longer running belt, stronger motor and decline for more crosstraining options.

So it really comes down to what you want at the end of the day.

Regardless of which one you choose, you can get a special iFit offer on either treadmill and learn more by checking out the links below.

Related:
Nordictrack 1750 Treadmill Review - A Good Buy for You? Nordictrack EXP 14i Treadmill Review

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Nordictrack exp14i vs 1750 Treadmill - How Do They Compare?

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Choosing between the Nordictrack EXP14i vs 1750 Commercial treadmill? Here's a review of the key differences between them! Plus how to save!

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Note: This post may contain affiliate links. Click Here to read our full affiliate disclosure.

The Nordictrack AirGlide 14i Elliptical is a brand new, premium elliptical trainer introduced this year.

It's built with an extra-large, 14-inch touch screen which will give you a more immersive, fun training experience.

Plus it's one of the few elliptical trainers with both and decline.

This gives you more ways to crosstrain your lower body and burn extra calories per workout.

Is the Nordictrack AirGlide 14i elliptical a good buy for you?

Here's a review of what to know before buying:

Nordictrack AirGlide14i Specs:

Style: Front-Drive

Flywheel: 32 lbs

Stride Length: 17.5 - 18.5 Inches

Incline: up to 15% Incline and 5% Decline

Folding? No

Warranty: 10 Year Frame, 2 Year Parts, 1 Year Labor

Nordictrack AirGlide 14i Elliptical Review - Benefits:

Full Color, Touch-Screen HD Console

The HD console is full color and touch-screen sensitive. It's one of the largest elliptical console screens you can find - at 14-inches across.

So it's a lot more fun to watch the iFit world trail runs and take the fitness classes through your console screen.

The screen also tilts - so you can find the best viewing angle for you.

And to further enhance the experience, there are 2 two-inch digitally amplified speakers in the console. So you can easily follow the iFit instructor or listen to your favorite workout tunes through the console.

Incline and Decline

Finding an elliptical trainer with incline can be hard - but a lot of people prefer to have it. Why?

Incline changes the slope of your running path - forcing you to work different lower body muscles. This is a great way to add crosstraining and get the most optimal workout for your time.

The Nordictrack Airglide 14i elliptical comes with up to 15% incline - giving you plenty of room to grow.

Plus you also get decline on this machine! You get up to 5% decline to add even more crosstraining to your workout.

The Air Glide 14i incline/decline also comes with Autoadjust technology . This basically means that the elliptical adjusts automatically to the terrain that you're running - for a more virtual reality training feel.

Or it can respond to the workout class you're taking. The trainer can adjust it for you - you don't need to do anything - for a more hands-off experience.

Heavy, 32-Pound Flywheel

A heavier flywheel helps to give you a more smooth-feeling elliptical stride - especially between resistance transitions.

A heavier flywheel also helps to anchor the machine for a more stable-feeling workout.

The 32-pound flywheel is one of the heaviest flywheels that Nordictrack puts on their machines and it's pretty impressive.

Compare it to, for example, the Nordictrack SE9i model which has an 18-pound flywheel and you'll see it's a pretty big difference.

Travel the World With iFit

iFit Coach connects your elliptical trainer to the Internet using the Wifi in your home. From there you can track your workout progress over time to see how far you've come and set new goals.

You can also run or walk famous trails from all over the world . You can strolls the streets of Paris or hike up the Swiss Alps. You can even run the Boston Marathon and take in the sites along the way.

Need some extra challenge? Can't get to the gym?

No problem.

Take a live or streaming studio fitness class right from the comfort of your own home

The trainer can control your AirGlide elliptical incline or speed for you throughout the workout for a seamless class experience (you can also override this if you choose).

With the Live classes there's also a leaderboard where you can see your stats or compete with your friends in real time. It's the next best thing to taking a class at the gym with your friends.

iFit Coach basically opens the door to new training options, which means you'll ultimately get more out of your crosstrainer.

Plus, as of this writing, if you get the Nordictrack Airglide 14i direct from the Manufacturer through the link below, you'll get a Full family membership to iFit included for a short time. So you can try it out first and see if you like it!

Active Pulse Technology

The Nordictrack 14i AirGlide elliptical also gives you something called Active Pulse technology. This is an option you can use that automatically controls the elliptical trainers speed or incline based on your heart rate.

The goal is to help you stay in your optimal heart-rate zone as you train - without you having to actively monitor it yourself.

(Note: You will need to buy the wireless heart rate monitor to use this feature).

Moving - and Stationary - Arm Bars

You can work your upper body with the moving arm bars on the AirGlide 14i trainer. Or you can rest your upper body and just grasp the stationary arm bars attached to the console.

This is worth mentioning since some elliptical trainers - like the Freestride ellipticals - only come with moving arm bars and not stationary bars.

Nordictrack 14i AirGlide - Any Drawbacks?

No EKG Heart Rate Monitor

While this trainer will work with a wireless heart rate monitor, the EKG hand grip pulse sensor is not included on the arm bars.

Hard-To-Reach Water Bottle Holder

While Nordictrack has included a water bottle holder under the console, we found that it can be hard to reach. You may have to stop your workout to reach down and get it - which can be awkward.

Bottom Line?

If you want to try a premium front-drive crosstrainer with all the bells and whistles - you may want to consider the new AirGlide 14i Nordictrack elliptical.

It gives you a much larger screen that many other front drive trainers. So you can see your workout stats, world trails or workout classes in large, living color.

With the heavier flywheel on this unit, you also get a smoother, more stable-feeling ride. And the incline-decline option gives you multiple ways to crosstrain your lower body and get more from your workouts.

Where to Buy and Save

You can get the new Nordictrack Air Glide 14i crosstrainer and this will include the iFit membership (for a limited time).

You can also get a free gift with purchase (as of this writing) and see detailed specs before you buy.

Related:
Nordictrack 14.9 Elliptical - A Good Buy for You?

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Nordictrack Airglide 14i Elliptical - A Good Buy for You?

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Is the Nordictrack AirGlide 14i elliptical trainer a good buy for you? Here's what you need to know - how it compares and how to save!

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In today's fast-paced world, ensuring the safety of your home and loved ones has become increasingly important. Smart home security cameras offer numerous benefits, enhancing the convenience and security aspects of modern living. As you explore the benefits of these cutting-edge devices, you'll find that they can bring peace of mind to your daily life.
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Trying to decide between the Nutrisystem Basic vs Uniquely Yours Max plans?

Wondering which one is best for you?

The Nutrisystem Basic is the most affordable out of all 3 of the Nutrisystem plans.

The Uniquely Yours Max plan is the premium option - with some key benefits that you may want.

So which is best for you?

To help you decide, here's a rundown of the main differences between the Basic vs Uniquely Yours Max diet plans from Nutrisystem:

(Please note that this is mainly for the women's plans - and not the partners plans or the men's plans . However there are general similarities that apply to these plans as well).

Nutrisystem Basic vs Uniquely Yours Max - Price

The Basic option is Nutrisystem's most affordable option and comes in at the lowest price - around $10.71 per day .

If you're on a budget that's a really great deal considering that 90% of your food is covered for the day.

The Uniquely Yours Max plan is a more gourmet option and comes in a bit higher at around $12.86 per day .

(Nutrisystem has offered our visitors an instant discount on either plan if you use .)

There is a key difference here though: The Basic plan only gives you meals and snacks for 5 days a week .

You have to add in your own "Flex" meals for 2 days out of the week (like the weekend). You're given all the necessary tips and help to do this via the app. But it's something to be aware of.

With the Uniquely Yours Max plan you do have the option to get meals delivered 5 days a week (and add in your own Flex meals).

Or you can also choose to get your meals delivered for 7 days a week - and pretty much have all of your meals covered. So it's nice to have that option if you want a stress-free, prep-free plan.

With both the Basic and UY Max plans you can choose the meals yourself, based on your personal favorites. Or you can have them chosen for you based on Nutrisystem customer favorites.

This customization option was not offered with older versions of the Basic plan. So it's nice to see that Nutrisystem is now giving customers this option.

Both plans also give you:

Weight Loss Coaching with 1-on-1 support 7 days a week

Free Numi app that helps you track everything in one place

Free shopping lists, recipes and guides

Nutrisystem Basic vs Uniquely Yours Max - Meals

The real difference between the Basic and Uniquely Yours Max plans comes down to the quality of meals that you're getting.

The Basic plan only offers Nutrisystem ready-to-go meals . These meals are "packaged for portability" meaning that they don't need to be refrigerated.

You can easily take them with you on the go.

There's a lot to choose from including pasta, pizza, chocolate bars and more. However meals are, well...simpler than your options with Uniquely Yours Max.

With Uniquely Yours you get an expanded menu with ready-to-go meals, fresh-frozen meals, "Restaurant Faves" and Hearty Inspirations meals.

The ready to go meals are the same as the ones offered on the Basic plan. The fresh-frozen meals are much more gourmet and better tasting (as rated by Nutrisystem customers) than the ready-to-go meals.

For example, here are some examples of Fresh-Frozen meals offered on this plan:

>> Broccoli and Cheese-Stuffed Chicken Breast

>> Chicken Mozzarella Melt

>> Chocolate Cupcake

>> Roasted Turkey Medallions

>>Cherry Cheese Roll

You also get access to the new Nutrisystem Restaurant Faves menu - which are a selection of gourmet meals inspired by popular restaurants. Nutrisystem is also continuously adding new options as well.

And finally you also get access to the new Nutrisystem Hearty Inspirations meals . These are high-protein, skillet-style meals that can keep you feeling full for up to 5 hours!

If you're worried about feeling hungry as you lose weight, these meals are guaranteed to keep you satisfied until your next snack or meal.

Quick 9 Plan for Fast Weight Loss

Another benefit you get with the Uniquely Yours Max plan (that's not included with the Basic option)? The Quick 9 Weight loss Jump-Start plan!

This is a specially designed plan to give you maximum weight loss during your first 2 weeks of the diet. According to Nutrisystem you can lose up to 9 pounds in your first 2 weeks with this plan.

If you're looking to see fast weight loss to get the motivation to keep going and reach your goals, this is a great option.

Again however - this is not included with the Basic plan - just the Uniquely Yours Max plan.

Bottom Line?

So what's the final word on the Nutrisystem Basic vs Uniquely Yours Max diet plans?

Well, both plans have their benefits.

The Basic plan is more affordable by about $65 a month. And if you just want to do the diet and don't care about the food too much, you'll probably be fine with it.

However the Uniquely Yours Max program gives you a better quality of food, more menu options and heartier meals. You also get the Quick 9 plan for a fast jump start to your weight loss. So for many people it's worth the money to get those extra benefits.

Regardless of which plan you choose - if you use the links below, you can also get 50% off Your Plan plus Free Shipping.

Want to learn more?

Related: Nutrisystem Plans Compared - Which Plan Is Right For You?
What Is Nutrisystem and How Does It Work?

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Nutrisystem Basic vs Uniquely Yours Max Comparison - Which is Best?

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Trying to decide between the Nutrisystem Basic vs Uniquely Yours Max plans? Wondering which one is best for you? The Nutrisystem Basic is the most affordable out of all 3 of the Nutrisystem plans. The Uniquely Yours Max plan is the premium option - with some key benefits that you may want.

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Note: This post may contain affiliate links. Click Here to read our full affiliate disclosure.

Looking for the best foldable Nordictrack treadmill models?

You came to the right place!

In past years, Nordictrack used to make all of their treadmill models foldable.

But with the introduction of some higher end models, this has changed.

The good news?

As of this writing, most Nordictrack treadmill models do fold up.

But there are still differences between them that you should know.

So which is best for you?

This post will give you a rundown of the current foldable Nordictrack treadmill models, benefits of each - and how to learn more about each one. But first...

Which Nordictrack Treadmills are NOT Foldable?

First let's start with which Nordictrack treadmills are foldable: the .

Any incline trainer - the X22i, X32i or even older models like the X11i or X9i are NOT foldable. Why not?

Due to the limits of the super high-incline design, these units do not fold up. You can take the console apart from the base for easier movement - but you can't fold them up.

Another Nordictrack treadmill that is not foldable? The Nordictrack Elite treadmill.

While this one seems like it is being discontinued, Nordictrack does sometimes bring it back.

It is, in essence, also an incline trainer (even though Nordictrack calles it an Elite "treadmill"). But it still gives you super high inclines of up to 40% - so basically it's an incline trainer. And because of this, it doesn't fold up.

Foldable Nordictrack Treadmills Models - Which is Best?

Now let's talk about which Nordictrack treadmill models DO fold up - so you can find the best option for you. This can be broken down - as of this writing - into 3 groups:

The older models are the models that you might find kicking around in stores - models from years past that need to be moved out of inventory. In general - unless these treadmills are incline trainers - or over $4000 they will probably fold up.

The downside?

They're older models. So there might be a reason these models didn't sell (and Nordictrack sent them to the stores to try and move the inventory).

Also, be aware that - depending on how old the model is - you might not get all of the bells and whistles that Nordictrack currently puts on their treadmills - like iFit for example.

That brings us to the EXP series.

Currently there are 3 in this series - the EXP7i, EXP10i and EXP 14i (you can read the current reviews of each one using the links on the main menu bar above).

The EXP series of treadmill is the most affordable series of treadmills that Nordictrack currently makes - starting around $1000 and up to around $1800.

These treadmills have all of the basics you need for a good workout - plus a few extras like iFit workouts and even HD touch-screen consoles.

They don't have some of the frills of the Commercial grade models - which we'll talk about below.

But they do give you everything you need for a good basic treadmill workout. They all fold up with hydraulic-assisted lifting.

Plus they're more affordable - making them ideal if you're on a budget.

That leaves the last of the foldable Nordictrack models - the

This series of treadmills gives you stronger construction than the two other options above. They have stronger motors and longer belts - making them a great choice for runners or multiple-user families.

They also tend to have larger screens (depending on the model you're comparing) - for a more enjoyable experience with iFit classes and world trails.

Another benefit is that - unlike the EXP series - they offer you both incline and decline. So you have more ways to crosstrain and challenge yourself.

There are currently 3 different foldable Commercial Nordictrack treadmills - the 1250, 1750 and 2450. You can read more about each one using the links above.

Or check out the link below to compare them directly from the Manufacturer - and get a special iFit deal included.

So those are your options when looking for the best foldable Nordictrack treadmill model.

You have some great options. It really comes down to your budget, preferences, exercise style and goals.

Good luck and have fun!

Related:
Nordictrack 1750 vs 2450 Treadmills - Which is Best for You? Can A Nordictrack Treadmill Be Used Without iFit?

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Foldable Nordictrack Treadmill Models - Which is Best for You?

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Note: This post may contain affiliate links. Click Here to read our full affiliate disclosure. Looking for the best foldable Nordictrack treadmill models? You came to the right place! In past years, Nordictrack used to make all of their treadmill models foldable. But with the introduction of some higher end models, this has changed.

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Hãy làm cho các trận bóng thêm thú vị bằng cách đặt cược vào đội mà bạn yêu thích. Và còn gì bằng sự hồi hộp xem bóng đá và tận hưởng niềm vui chiến thắng nhà cái!

An toàn, bảo mật & thanh toán sòng phẳng đó là đặc điểm nổi trội của các trang cá độ bóng đá uy tín dưới đây.

Hướng dẫn cá độ bóng đá qua mạng uy tín nhanh gọn

Bên trên chúng tôi đã gợi ý cho các bạn top 6 trang bóng uy tín mà bạn nên lựa chọn. Tất nhiên với trách nhiệm cao nhất, Vi68 là trang cá cược chúng tôi đánh giá cao nhất và bạn không nên bỏ qua nhà cái này. Còn cách đặt cược trên app cá độ bóng đá online Vi68 như thế nào? Theo dõi hướng dẫn dưới đây của chúng tôi nhé!

Chúng ta có thể lấy Link vào trang bóng uy tín vi68 qua việc search Google từ khoá "Vi68". Hoặc truy cập trực tiếp vào đường link cá độ bóng đá này: https://vi68.co/

Sau khi vào được trang chủ rồi các bạn có thể bấm vào nút đăng ký để có thể tham gia và tải trang cá cược bóng đá Vi68 về máy nhé!

Để trở thành hội viên Vi68, việc đầu tiên là bạn cần làm là đăng ký trang cá cược bóng đá Vi68. Cách đăng ký tài khoản được thực hiện đơn giản như sau:

Bước 1: Truy cập vào đường link trang chủ của Vi68 hoặc mở App Vi68 trên điện thoại

Bước 2: Nhấn chọn mục "Đăng ký" để tạo tài khoản chơi game

Bước 3: Điền đầy đủ thông tin theo yêu cầu của nhà cái gồm:

Tên đăng nhập

Mật khẩu

Số điện thoại

Mã xác minh

Bước 4: Nhấn chọn Đăng ký.

Nếu mọi thông tin đều chính xác, bạn sẽ tạo tài khoản thành công. Nếu thông tin sai, bạn sẽ tiến hành điền lại thông tin theo yêu cầu.

Khi nói tới " cá cược bet nạp rút nhanh" là chúng ta sẽ nghĩ ngay tới app cá cược bóng đá online uy tín Vi68. Đơn giản vì, Vi68 nổi tiếng là nhà cái có tốc độ nạp rút tiền siêu siêu tốc. Đặc biệt là nhà cái này có hỗ trợ cá cược bằng btc, giúp bạn nạp rút ẩn danh và an toàn. Ngay bây giờ là chi tiết các bước để rút tiền về tài khoản của bạn như sau:

Bước 1: Người chơi vào trang chủ của Vi68, và đăng nhập vào tài khoản Vi68.

Bước 2: Chọn ngay nút Rút tiền hiển thị trên giao diện màn hình.

Bước 3: Tại đây, các bạn điền đầy đủ thông tin tài khoản ngân hàng để rút tiền bằng cách: Quản lý tài khoản >> Nạp tiền >> Ngân hàng nội địa >> Điền tài khoản ngân hàng >> Điền mật khẩu Rút tiền

Bước 4: Điền Số tài khoản, tên người nhận, tên ngân hàng sau đó Xác nhận

Bước 5: Điền số tiền cần rút, mật khẩu rút tiền và nhấn Xác nhận.

Và chỉ sau khoảng 2-3 phút, là lệnh rút tiền của bạn sẽ được bộ phận kế toán Vi68 hoàn thành. Sự tiện lợi do nghiệp vụ nạp rút thông minh của Vi68 mang lại, giúp cho hình thức cá độ bóng đá qua mạng chưa bao giờ dễ dàng như thế.

Vi68 đã tung ra hàng loạt chương trình khuyến mãi hấp dẫn dành cho thành viên mới và cũ. Cụ thể trang bóng đá khuyến mãi như sau:

Tải app và Đăng ký Vi68 Nhận Khuyến mãi 100% đến 688.000 VNĐ lần nạp đầu tiên. Nạp tối thiểu 200k chỉ 1 vòng cược!

Khuyến mãi nạp lần đầu nhận 128% tối đa nhận 3.800.000 VNĐ khi tải app và đăng ký tại Vi68

Nhận thêm Khuyến mãi 5% nạp lại đến 1tr5 cho thành viên cũ!

Tải App nhận ngay tiền mặt tại nhà cái Vi68. Người chơi tải app và xác minh tài khoản thành công sẽ được nhận 50k - 100k tùy vào may mắn!

Lưu ý: 1 điện thoại và số điện thoại chỉ tạo được 1 lần, nghiêm cấm hành vi gian lận!

Để tham gia đặt cược bóng đá online người chơi cần lựa chọn sảnh có tỷ lệ kèo nhà cái phù hợp với lối chơi của mình.

Saba Sports: Là sảnh chơi cá cược với các môn thể thao như: Bóng đá, cầu lông, bóng bàn, bóng rổ, đua ngựa.... Tỉ lệ kèo đặt cược rất đa dạng, đặc biệt rất mạnh về các kèo tài xỉu hay kèo cược xiên.

Sbo Thể Thao: Mang đến cho bet thủ các loại hình cá cược thể thao đầy đủ nhất như: Cược chẵn lẻ / cược 1X2 / cược tỷ số chung cuộc/ cơ hội kép/ cược bàn cuối - bàn đấu / cược trên dưới / cược hỗn hợp / cược chấp/,... Với số lượng kèo khủng đi kèm với chất lượng thì chắc chắn đem lại sự hài lòng cho người chơi.

CMD: cũng rất đa dạng các môn thể thao như: bóng đá, các giải đua xe, bóng rổ, bóng chày,... Thể thao CMD tại Vi68 phủ sóng tất cả các giải đấu lớn nhỏ hàng đầu thế giới như: Ngoại Hạng Anh,Champion League, La Liga, Ligue 1, Serie A, Bundesliga,...

Thể Thao Ảo: Mô phỏng từ thực tế vê 3D, với các môn thể thao nổi bật như: Bóng đá, bóng rổ, quần vợt, đua ngựa, đua chó...

Khi lựa chọn đúng sảnh cược thì phần trăm thắng cược của bạn sẽ tăng lên không hề ít. Bên cạnh đó giao diện cũng như cách đặt cược giữa app cá cược bóng đá trên ios và trên andoird cũng không có mấy sự khác biệt. Với bố cục tinh tế, chỉ cần vài thao tác đơn giản là bạn có thể xuống tiền thành công.

Lấy trang cá độ bóng đá trả sau có nên không?

Lấy mạng cá cược bóng đá trả sau ở Việt Nam hiện nay khá phổ biến. Tuy nhiên các trang bóng này bị triệt phá luôn xảy ra thường xuyên. Đâu là lý do, hãy đọc ngay những dòng bóc tách phân tích của chúng tôi dưới đây.

Cách lấy mạng bóng 88 nhanh nhất và đơn giản nhất đó là search Google, và bạn sẽ tìm thấy vô vàn trang mạng liên quan.

Tuy nhiên với những trang trả sau, khả năng thông tin cá nhân của bạn bị rò ri là điều không thể tránh khỏi.

Hãy đến với các trang bóng trả trước, nơi mọi giao dịch giữa bạn và nhà cái là trực tiếp - không thông qua đại lý.

Và H-ngm-n.com là địa chỉ tuyệt vời hân hạnh tổng hợp danh sách 6 nhà cái tiến cử đến bạn. Đây mới chính là cách lấy trang cá độ bóng đá an toàn và bảo mật nhất, ngoài ra những trang này còn có thế mạnh như:

Cá độ bóng đá trên mạng có bị bắt không? là câu hỏi mà nhiều anh em thắc mắc. Vậy tôi xin trả lời là "CÓ". Nhưng gần như 100% các trường hợp triệt phá, bị bắt là có liên quan đến các trang trả sau.

Hầu hết các trang mạng trả sau về cách thức hoạt động đều tương đối giống nhau, nên H-ngm-n.com sẽ chỉ phân tích về lấy trang bóng tổng BONG88.

Vậy nên thay vì lấy trang cá độ bóng đá trả sau thì các bạn nên chọn các web trả trước để tránh những rủi ro trên nhé!

Xem thêm Khi nào cá độ bóng đá được coi là hành vi vi phạm.

Với hình thức trả trước thì người chơi bắt buộc phải nộp tiền vào tài khoản thì mới có số dư để có thể đặt kèo. Các trang cá cược online tốt nhất hiện nay đều áp dụng hình thức này. Cụ thể là nạp bao nhiêu chơi bấy nhiêu, thắng thì rút tiền về tài khoản ngân hàng. Dưới đây chúng tôi nêu 1 số ưu điểm để cho các bạn hiểu rõ hơn hình thức này:

Với những ưu điểm nổi bật bên trên của trang mạng trả trước, hy vọng bạn sẽ thấy được rủi ro của việc lấy tổng bóng 88.

Xem thêm >>> Ưu Điểm Của Trang Cá Cược Online Trả Trước Bạn Nên Biết

Cách đọc kèo cá cược bóng đá trên mạng

Hiện nay có rất nhiều hình thức cá cược, nổi bật: Kèo Châu Á, kèo tài xỉu, kèo Châu Âu, kèo phạt góc, cược tỷ số chính xác... Dưới đây, h-ngm-n.com sẽ hướng dẫn bạn cách đọc kèo cũng như chia sẻ các công thức tính kèo cá cược bóng đá trên mạng "chuẩn" nhất!

Kèo đồng banh 0 trái

Kèo chấp đồng banh hay còn gọi là kèo 0 trái. Kèo này xuất hiện ở 2 đội bóng đối đầu có phong độ bằng nhau. Nếu cuối trận đấu kết quả tỷ số hòa thì bên cược sẽ không mất tiền.

Ví dụ:

Với kèo này sẽ không có bàn chấp, đội nào áp đảo về tỷ số sẽ là người thắng cuộc. Nếu hòa thì tiền ai về nhà nấy.

Kèo chấp 1/2 (0.5) - Còn gọi là kèo chấp nửa trái

Với kèo chấp 1/2 này thì đội cửa trên sẽ chấp cửa dưới nửa trái. Nếu hòa cho cửa dưới thắng.

Ví dụ tính tỷ lệ kèo chấp nửa trái như sau:

Nếu Eintracht Frankfurt thắng Eintracht Frankfurt thắng cả tiền, chọn Augsburg thua cả tiền.

Nếu Eintracht Frankfurt thua hoặc hòa thì Eintracht Frankfurt thua cả tiền, chọn Augsburg thắng cả tiền.

Kèo chấp 3/4 (0.75) - Còn gọi là kèo chấp nửa một

Trong kèo này đội cửa trên chấp nếu chỉ thắng 1 bàn thì ăn nửa tiền, thắng cách biệt 2 bàn trở lên thì ăn cả tiền.

Ví dụ:

Kèo chấp 1 quả (1 hòa)

Trong kèo bóng này đội cửa trên nếu chiến thắng với cách biệt 1 bàn thì hòa tiền. Nếu cửa trên thắng với cách biệt 2 bàn trở lên thì ăn cả tiền.

Ví dụ:

Kèo Chấp 1.25

Kèo chấp 1 1/4 (1.25) - Còn gọi là kèo chấp 1 thua nửa, hay kèo chấp 1 trái 1 trái rưỡi. Trong kèo bóng này, nếu chỉ thắng 1 bàn cách biệt thì đội cửa trên sẽ thua nửa tiền. Sẽ ăn cả tiền nếu thắng với cách biệt 2 bàn trở lên.

Ví dụ:

Kèo chấp 1 1/2 (1.5)

Kèo này còn gọi là kèo chấp 1 thua đứt, hay kèo chấp 1 trái rưỡi. Trong kèo bóng này nếu chỉ thắng với cách biệt 1 bàn thì đội cửa trên sẽ thua cả tiền, và ăn cả tiền nếu thắng với cách biệt 2 bàn trở lên.

Ví dụ:

Nếu Hamburger SV thắng với cách biệt 2 bàn trở lên, thì chọn Hamburger SV thắng cả tiền, Karlsruher SC thua cả tiền

Nếu Hamburger SV hòa hoặc thua hoặc chỉ thắng với cách biệt 1 bàn, thì Hamburger SV thua cả tiền, Karlsruher SC thắng cả tiền.

Kèo chấp 1 3/4 (1.75)

Hay còn gọi là kèo chấp một trái rưỡi hai, hay kèo 2 ăn nửa. Trong kèo bóng này, đội cửa trên sẽ được ăn nửa tiền nếu chỉ thắng cách biệt 2 bàn, và ăn cả tiền nếu thắng cách biệt là 3 bàn trở lên.

Kèo chấp 2 quả (2 hòa)

Trong kèo bóng chấp 2 quả này thì đội cửa trên chỉ thắng với cách biệt 2 bàn thì hòa tiền. Và ăn cả tiền nếu cửa trên thắng với tỉ số 3 bàn trở lên.

Ví dụ:

Kèo Châu Âu chính là kèo cá cược 1×2. Người chơi sẽ được đặt cược vào các cửa: thắng, thua và hòa. Hình thức cược này phổ biến ở các nước Châu Âu và Châu Mỹ. Cụ thể:

Ví dụ: Cách đọc kèo bóng đá Châu Âu (1x2)

Ở đây, đội chủ nhà Việt Nam sẽ là số 1, đội khách Triểu Tiên là số 2. X sẽ là trận hòa.

Xem thêm >>> Kèo châu Âu và kèo châu Á là gì? Có gì khác biệt giữa 2 loại kèo?

Trong kèo tài xỉu thì sẽ không quan tâm đến thắng thua mà chỉ dựa vào tổng số bàn thắng của cả 2 đội trong trận đấu.

Nếu tổng số bàn thắng trong cả trận đấu lớn hơn giá tài xỉu nhà cái đưa ra => Tài thắng, Xỉu thua

Nếu tổng số bàn thắng trong cả trận đấu nhỏ hơn giá tài xỉu nhà cái đưa ra => Xỉu thắng, Tài thua

Nếu trận đấu có tổng số bàn thắng BẰNG với giá tài xỉu nhà cái đưa ra => HÒA tiền.

Ví dụ: Cách đọc kèo tài xỉu

1: Giá tài xỉu mà nhà cái đưa ra. Như hình thì tài xỉu của trận này là 3 quả

2: Tỷ lệ ăn nếu đi kèo TÀI và thắng kèo (Đánh 100 điểm sẽ thắng 87 điểm, nếu thua thì thua 100 điểm)

3: Tỷ lệ ăn nếu đi kèo XỈU và thắng kèo (Đánh 100 điểm, nếu thua thì chỉ thua 97 điểm, nếu thắng thì ăn đủ 100 điểm)

4: Giá tài xỉu của hiệp 1. Như hình thì tài xỉu hiệp một là 1 1/4 hay một trái một trái rưỡi

5: Tỷ lệ ăn nếu đi kèo TÀI trong hiệp 1 và thắng kèo (Đánh 100 điểm, nếu thua thì chỉ thua 98 điểm, nếu thắng thì ăn đủ 100 điểm)

6: Tỷ lệ ăn nếu đi kèo XỈU trong hiệp 1 và thắng kèo (Đánh 100 điểm sẽ thắng 88 điểm, nếu thua thì thua 100 điểm)

Xem thêm >>> Tài xỉu bóng đá là gì? Hướng dẫn cách đọc kèo Tài Xỉu chuẩn nhất

Cược xiên hay còn gọi là cược xâu. Đối với kèo cược này, các bạn chỉ có thể thắng cược giả dụ đoán đúng phần lớn các kèo cược trong xâu. Tại các nhà cái châu Âu, cược xiên còn được gọi với các tên như: Accumulator, Multiple Beat hay Mix Parlay,...

Do phải đoán trúng đa dạng kèo cược đồng thời nên tỉ lệ thắng cược xâu rất thấp. Xác suất thắng cược như sau:

Cách cá cược bóng đá hiệu quả nhất 2023

Làm thế nào để bắt kèo bóng đá chuẩn nhất? Đây chắc hẳn là câu hỏi của không ít cược thủ đúng không nào. Vậy hãy cùng tìm hiểu những công thức cá độ bóng đá và các cách bắt kèo bóng đá không thua được tổng hợp dưới đây nhé!

Để có thể bắt kèo cá độ đá banh online chuẩn nhất thì bạn không nên quá vội vàng đặt cược luôn. Hãy theo dõi sơ lược về đoạn đầu của trận đấu. Sau khoảng 10' trận đấu diễn ra, lúc này kèo tài xỉu được hạ xuống, giúp cho bạn có nhiều cơ hội để ăn thưởng hơn.

Cách tính kèo tài xỉu cũng là một trong những yếu tố quan trọng giúp bạn dễ chiến thắng hơn. Phân tích cẩn thận những thông tin để có thể đưa ra được quyết định chính xác nhất. Cùng tham khảo một số bí kíp đánh tài xỉu bóng đá của các cao thủ như:

Quản lý tốt số vốn của mình. Đây là yếu tố hàng đầu giúp bạn không bị thất thoát quá nhiều. Vậy nên bạn cần cân nhắc và tính toán cẩn thận số tiền của mình trước khi quyết định đặt cược

Nếu bạn chỉ đang mới tập soi kèo và chưa có nhiều kinh nghiệm thì nên chọn những cách đánh an toàn. Không nên vội vàng sử dụng cách đánh nhồi tài hoặc nhồi xỉu. Bởi đây là cách đánh khá mạo hiểm, nểu bạn không có nhiều vốn thì rất dễ trắng tay.

Hiểu rõ luật chơi và trang bị cho mình những kiến thức, kinh nghiệm từ những người chơi trước. Đây là một cách thông minh giúp bạn tăng cao khả năng chiến thắng của mình.

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Luôn giữ được tâm lý ổn định, không để cảm xúc chi phối đến quyết định của bạn.

Thay vì đặt cược trên web, hãy tải về điện thoại ứng dụng cá cược bóng đá uy tín. Với sự hoạt động ổn định cũng như lưu trữ dữ liệu thông minh, app cá độ bóng đá sẽ giúp bạn trải nghiệm trơn tru hơn.

Và cuối cùng, hãy xem đây chỉ là một hình thức giải trí mà thôi. Không nên quá đam mê với bộ môn này.

Cách bắt kèo bóng đá theo tỷ lệ kèo khá đơn giản. Với 2 tỷ lệ kèo là Châu Á và Châu Âu, mỗi loại kèo lại có công thức tính kèo bóng đá khác nhau.

Kèo Châu Âu: loại kèo khá phổ biến với đa dạng tỷ lệ cược cho bạn lựa chọn. Kèo Châu Âu thường đi cùng với những mùa giải lớn như FIFA hay World Cup,... Luật chơi của kèo này cũng khá đơn giản và không đòi hỏi phải theo dõi quá nhiều. Bạn chỉ cần quan tâm đến kết quả là hoà, thắng hay thua mà thôi.

Kèo Châu Á: sẽ không có cửa hoà, mà chỉ có cửa trên hoặc cửa dưới. Có nghĩa bạn sẽ chỉ được cược thắng hoặc thua giữa đội chấp và đội được chấp. Chơi cá độ bóng đá trực tuyến theo tỷ lệ kèo châu Á bạn có thể lựa chọn nhiều kèo khác nhau như: Kèo 0.5 (½), kèo 0.75 (¾), kèo 0.25 (¼), kèo 1.25 (1 nửa trái rưỡi) hay kèo 1 1.5 trái,...

Xem thêm video để tránh một số lỗi cơ bản trong cá cược đá banh nhé:

Đặt cược bóng đá online ngày trở lên hot, trong đó phải kể đến bóng cỏ. Bóng cỏ được dùng để gọi những trận đấu có quy mô nhỏ, vừa phải. Cùng tìm hiểu 3 kinh nghiệm đánh bóng cỏ phổ biến hiện nay của anh em cược thủ nhé

Trước hết, để có thể đánh bóng cỏ hay, bạn cần lựa chọn được nhà cái uy tín. Một sân chơi đảm bảo an toàn, chất lượng giúp bạn có thể yên tâm tham gia cá cược.

Bạn cần trang bị cho mình những kiến thức về cá cược bóng cỏ. Có thể nói, bóng cỏ cũng có hình thức cá cược như nhưng kèo bóng đá thông thường.

Cuối cùng chính là bạn cần chọn cho mình một kèo cược phù hợp. Chắc hẳn bạn cũng đã biết, trong thể thao thường sẽ có những loại kèo khác nhau như: kèo ¾, kèo tài xỉu, kèo phạt góc,... Việc nhận định và đưa ra quyết định đúng đắn sẽ giúp bạn dễ dành chiến thắng hơn.

Với những kèo cá độ trên mạng để có thể đem lại chiến thắng, bạn cần phải trang bị cho mình những kiến thức hay học hỏi từ cao thủ đi trước. Một số lưu ý sẽ là những mẹo hữu ích cho bạn đó:

Đặt kèo tỉnh táo là điều đầu tiên bạn cần lưu ý khi học cách đánh banh cơm gạo trong bất cứ trận đấu nào. Áp dụng chiến thuật cụ thể để đưa ra quyết định chọn cửa cược thay vì nghe theo lời nói của những người xung quanh là cách chơi thông minh của những cao thủ.

Giữ được tâm lý ổn định và kiềm chế bản thân. Thắng không tham, thua không nản. Giữ vững được tâm lý sẽ giúp bạn có những quyết định đúng đắn.

Lựa chọn nhà cái chất lượng. Như đã nói ở các kinh nghiệm cá cược trên. Đây là yếu tố quan trọng hàng đầu giúp bạn có những phút giây cá cược hấp dẫn nhất.

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Tại đây H-ngm-n.com sẽ chia sẻ các kiến thức và mẹo hay về cá cược trực tuyến uy tín, bài viết của chúng tôi được đông đảo độc giả đánh giá cao. Tổng hợp, đánh giá chi tiết trang cá cược nào uy tín nhất để cho anh em có nhiều lựa chọn hấp dẫn và phù hợp nhất!

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Tiên đề cho sự an toàn đó chính là bạn cần đăng ký tài khoản cá độ bóng đá tại những trang uy tín nhất. Vậy làm sao để có thể nhận biết được một nhà cái uy tín? Thì những trang cá cược trực tuyến uy tín nhất sẽ đảm bảo đáp ứng tiêu chuẩn sau:

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Xem thêm >>> Hướng dẫn cách cá cược bóng đá online mới nhất 2023

ình thức đánh bạc ăn tiền bị cấm tại Việt Nam. Tuy nhiên, khi Nghị định 06/2017/NĐ-CP được ban hành quy định việc Trang cá cược hợp pháp ở Việt Nam "không tồn tại". H hợp thức hoá cá cược bóng đá quốc tế. Các đơn vị nhà cái quốc tế muốn hoạt động tại Việt Nam phải tuân thủ đầy đủ các điều kiện và được cấp giấy phép hoạt động uy tín.

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Ngoài những luật cá cược bóng đá cơ bản ra, bạn còn cần tuân thủ đúng yêu cầu của các nhà cái:

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Khi lựa chọn tham gia cá cược bóng đá, người chơi có thể gặp 3 loại kèo phổ biến là kèo châu Á, kèo châu Âu và kèo tài xỉu. Vậy cách đọc của ba kèo này như thế nào như sau:

Kèo châu Á

Kèo châu Á có nhiều tỷ lệ khác nhau như sau: Kèo chấp 0-0, kèo chấp 1 trái, kèo chấp ¼, kèo chấp 0.5, kèo chấp 1.25, kèo chấp 2 trái, kèo chấp 3 trái,...

Kèo chấp 1 trái: Đội cửa trên thắng đội cửa dưới trên 2 bàn thì người chơi đặt cược đội cửa dưới sẽ thua và đội cửa trên sẽ thắng. Còn từ 1 bàn trở xuống thì người cho đặt cược cửa dưới sẽ ăn tiền cược và cược cửa trên sẽ mất tiền cược.

Kèo chấp ¼: Đội nào giành chiến thắng và người chơi đặt cược đội đó thì người chơi sẽ được ăn tiền thưởng. Còn nếu hai đội hoà nhau thì người chơi đặt cược đội cửa trên sẽ thua nửa tiền còn đội cửa dưới sẽ ăn nửa tiền.

Cách đọc kèo của các kèo khác cũng sẽ tương tự như cách đọc kèo của 2 loại kèo cơ bản như trên.

Kèo châu Âu

Kèo châu Âu hay còn được biết đến với cái tên là kèo 1x2.

1 là cược đội chủ nhà thắng

2 là người cho cược cho đội khách thắng

X là trường hợp hoà.

Kèo tài xỉu

Người chơi sẽ dựa vào tỷ số ban đầu của nhà cái và đưa ra kết quả cao hơn là tài và thấp hơn là xỉu.

Khi trận đấu kết thúc và cả đội bóng chỉ ghi được 2 bàn thì người chơi đặt xỉu sẽ thắng và tài sẽ thua.

Với tỷ lệ đặt cược là ¼ và người chơi đặt cược tài mà tỷ số chỉ có 2 bàn thì người chơi sẽ bị thua mất một nửa số tiền cược. Nhưng nếu đội bóng ghi thêm được 1 bàn nữa thì người chơi sẽ nhận được số tiền cược thắng.

Tỷ lệ cược 2 ¾, trận đấu ghi được từ 3 bàn thắng trở nên thì người chơi sẽ thắng được toàn bộ số tiền cược.

Xem thêm >>> Cách Đọc Kèo Nhà Cái Bóng Đá Chi Tiết Từ [A-Z]

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6+ trang Cá Độ Cá Cược Bóng Đá trực tuyến uy tín nhất 2023

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Khi lựa chọn tham gia cá cược bóng đá, người chơi có thể gặp 3 loại kèo phổ biến là kèo châu Á, kèo châu Âu và kèo tài xỉu.

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Dijaman yang serba instan dan canggih ini, memudahkan kita dalam berbagai hal salah satunya adalah konsultasi online dengan dokter.

Anda tak perlu menunggu antrian konsultasi ketika berkunjung ke klinik terdekat. Dapatkan layanan medis lengkap bersama Klinik Gonore Jakarta terbaik dan terpercaya hanya di Klinik Utama Pandawa.

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Sebelum membahas lebih lanjut, tonton video dibawah ini tentang ciri-ciri penyakit gonore atau biasa disebut kencing nanah.

Apa Itu Penyakit Gonore (Kencing Nanah)?

Seperti yang kita ketahui gonore merupakan infeksi menular seksual (IMS) umum yang disebabkan oleh bakteri yang disebut Neisseria gonorrhoeae (N. gonorrhoeae). Dalam bahasa Indonesia, penyakit ini lebih dikenal dengan sebutan penyakit kencing nanah.

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Umumnya, penyakit ini tidak menimbulkan gejala yang signifikan. Hal ini membuatnya mudah untuk menginfeksi pasangan Anda tanpa sadar karena tanda yang timbul mirip dengan gejala lainnya. Bahkan, beberapa diantaranya baru diketahui ketika Anda melakukan pemeriksaan dan diketahui sudah parah.

Penyebab dan Faktor Risiko Gonore (Kencing Nanah)

Selain penyebab utama penyakit gonore adalah bakteri kelamin, Neisseria Gonorrhoeae. Seseorang bisa terinfeksi penyakit kelamin lainnya karena sudah aktif berhubungan seks sejak remaja. Bahkan, ibu hamil yang terinfeksi bisa mentransmisikan bakteri gonore selama persalinan.

Berikut ini faktor risiko Anda terinfeksi penyakit gonore (kencing nanah):

Aktif seks sejak usia remaja.

Memiliki riwayat IMS.

Tidak gunakan pengaman tiap kali berhubungan seks.

Berhubungan seks dengan satu atau lebih pasangan yang belum di tes negatif gonore.

Memiliki hubungan sesama jenis

Jika Anda memiliki faktor risiko di atas sebaiknya lakukan pemeriksaan atau skrining penyakit menular seksual di klinik gonore terdekat.

Apa saja gejala gonore?

Gonore seringnya menyerang pria namun karena rentan menular wanita juga dapat ikut terinfeksi. Gonore tidak menimbulkan gejala pada wanita sehingga sering diabaikan.

- Gejala gonore pada wanita

Inilah yang perlu Anda perhatikan dan curigai;

Keputihan yang tidak biasa (kekuningan atau kehijauan).

Nyeri di perut bagian bawah atau panggul (perut).

Nyeri saat berhubungan seksual (dispareunia).

Buang air kecil terasa sakit

Perdarahan diluar masa menstruasi

- Gejala gonore pada pria

Keluarnya cairan kuning seperti nanah atau hijau di ujung penis.

Nyeri atau terbakar (mungkin parah) saat buang air kecil (disuria).

Nyeri testis dan testis bengkak.

Gejala gonore pada organ lain

Selain di atas, gejala gonore juga bisa dialami pada area dubur, mata, mulut dan juga tenggorokan.

Komplikasi Gonore

Gonore atau kencing nanah yang segera diobati dapat disembuhkan. Namun, bila dibiarkan atau salah pengobatan maka berisiko memicu komplikasi.

Komplikasi gonore pada wanita

Penyakit gonore yang tidak diobati pada wanita mampu menyebabkan:

Menyerang organ reproduksi lainnya, termasuk rahim dan saluran tuba, dan memicu penyakit radang panggul (PID). PID dapat memicu kemandulan dan kehamilan ektopik, yang dapat mengancam jiwa ibu (ibu kandung) dan bayinya.

Menyebabkan masalah kesehatan bayi yang lahir dari orang tua kandung yang tidak diobati. Seperti gangguan penglihatan dan lainnya

Menyebabkan persendian bengkak dan nyeri, radang hati, serta kerusakan katup jantung dan otak.

Komplikasi gonore pada pria

Pada orang yang ditetapkan laki-laki saat lahir, gonore yang tidak diobati dapat menyebabkan:

Infertilitas atau ketidaksuburan

Bekas luka di uretra.

Peradangan pada testis.

Nyeri dan pembengkakan prostat.

Memicu gangguan persendian, radang hati, serta kerusakan katup jantung dan otak.

Bagaimana gonore didiagnosis?

Ketika Anda curiga dan memiliki faktor risiko sebaiknya segera kunjungi klinik gonore terdekat. Untuk mendiagnosis gonore, dokter akan mengajukan pertanyaan sehubungan dengan riwayat medis dan seksual Anda, serta gejala-gejala yang dirasakan.

Pemeriksaan Gonore (Kencing Nanah)

Setelah mengetahui keluhan Anda, dokter akan melakukan pemeriksaan fisik dan juga pemeriksaan pendukung bila diperlukan untuk tes gonore. Seperti, tes urin, tes swab cairan kelamin atau tes darah untuk mengidentifikasi bakteri gonore atau infeksi kelamin lainnya.

Pengobatan Gonore di Klinik Gonore Jakarta

Apabila kamu menemukan keanehan di sekitar kemaluan, Anda sebaiknya segera kunjungi Klinik Kulit dan Kelamin seperti Klinik Utama Pandawa untuk mendapatkan pengobatan gonore tepat dan sesuai kondisi Anda.

Dokter spesialis kulit dan kelamin di Klinik Utama Pandawa, sudah profesional dan berpengalaman dibidangnya. Dokter SpKK akan memberikan pengobatan gonore yang tepat dan akurat.

Gonore atau kencing nanah dapat disembuhkan bila mendapatkan penanganan dan pengobatan tepat dari dokter. Pengobatan gonore yakni dengan menggunakan obat anti bakteri yang khusus dan diresepkan oleh dokter spesialis.

Selama pengobatan gonore, Anda dianjurkan untuk tidak berhubungan seks dahulu hingga kondisi sehat. Pada Ibu hamil, pengobatan gonore perlu diawasi juga oleh dokter kandungan dan dokter SpKK. Proses kelahiran juga akan didiskusikan mengikuti tingkat keparahan.

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Autonomous Task Management Productivity Powered by AI Agents

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Autonomous Task Management Productivity Powered by AI Agents

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In the beginning, there was ChatGPT, an AI chatbot based on the GPT-3.5 large language model (LLM) developed by OpenAI. And ChatGPT was smart. It had wit, charm, and an almost encyclopedic knowledge of language and culture. But ChatGPT was flawed in its design - it lacked autonomy and volition to perform complex tasks without human supervision.

LLMs and AI chatbots may have set the stage for widespread AI adoption. But the true power of artificial intelligence lies in the ability to go beyond language understanding and generate meaningful actions in the world. This is where autonomous task management come into play.

In today's article, we take a closer look at the concept of AI agents and their potential role in transforming project management, improving team collaboration, and reducing bottlenecks in task planning, prioritization, and execution. So buckle up and let's dive in. 🤖

AI systems can be broadly classified into two main categories.

Weak AI are a group of specialized systems capable of executing a limited range of tasks. Take ChatGPT as an example. While undeniably impressive, its primary function is processing and generating text, and it often needs a barrage of prompts just to produce a usable output.

The second category is much more impressive.

Strong AI (a.k.a. Artificial General Intelligence or AGI) is the digital boogeyman all fear-mongering futurologists have been telling us about. It's the apex of AI systems that boasts a broad range of cognitive abilities similar to those of humans, such as learning and reasoning.

At least in theory, because the road to AGI is not as straightforward as it may seem.

Most AI systems based on currently available LLMs are designed to carry out specialized tasks - think generating images or text - and lack the generalization skills that humans possess.

They can accomplish many remarkable feats, but they can't:

🤔 Interpret information and make decisions based on intuition or experience.

💬 Engage in complex social interactions or empathize with others.

🤹‍♂️ Adapt to unexpected situations that were not accounted for in their programming.

Letting a weak AI attempt to navigate a volatile project setting without adequate planning, risk mitigation, and supervision can't end well. Unless we give it a helping hand.

Recursive AI agents complete the AI "circle of life" by working with LLMs to perform tasks in a loop, without the need for constant prompting. You can think of them as taskmasters that tell all the powerful but infantile AI models how to carry out a list of tasks in a coordinated manner.

While AI agents have been making headlines for the past couple of weeks, the research into autonomous task management started back in the early 1990s.

In 1991, computer scientist and researcher Jose Brustoloni proposed a hierarchical architecture for intelligent agents, encompassing reaction, planning, and learning. Brustoloni also defined agents as "systems capable of autonomous, purposeful action in the real world."⁽¹⁾

Over the years, thanks to advancements in natural language procession (NLP) and machine learning (ML), autonomous agents started making their way into various industries in the form of self-driving cars, robotic process automation (RPA), drone delivery systems, or recommendation algorithms. But these systems lacked the sophistication to act without supervision on more diverse tasks that were not part of their original programming.

That's until OpenAI stirred the AI pot with GPT-3.5 and GPT-4.

On 23 March, 2023, Yohei Nakajima, general partner at Untapped Capital, published a paper introducing the concept of a "Task-driven Autonomous Agent," an AI system that can "plug" into existing LLMs like GPT-4 and perform complex tasks more or less autonomously.

For example, if you wanted to build a website with the help of ChatGPT, you would have to continuously prompt ChatGPT with instructions and follow-ups. Instead of asking for HTML / CSS code, you can simply ask the agent to "create a website," sit back, and enjoy the results.

Over the last few weeks, Nakajima's ideas have become a drop in the sea of similar projects exploring the agent architecture, with many more popping up every day. But before we go any further, there is one more thing we need to explain.

Most autonomous agents need three key ingredients to work properly - a task provided by the human operator, access to an AI model, and memory to store the output of the task.

You can think of an agent as a chef in a restaurant kitchen who wants to prepare a complicated dish. It can have access to the finest kitchen appliances (GPT-4), but without a recipe or instructions, the agent won't know which ingredients to use or how to prepare them correctly.

This is where a framework like LangChain comes in.

LangChain acts as a set of instructions or a recipe book, providing the agent with a standardized interface to communicate with LLMs like GPT-3 or GPT-4. LangChain also gives AI models access to tools that extend their functionality, e.g. by adding the ability to search the web.

But LLMs have one more critical flaw.

Most AI models can't remember context exceeding a medium-length conversation. For GPT-3.5 (legacy ChatGPT), that's 4,096 "tokens," which translates to around 8,000 words. GPT-4 can store around 50 pages of text, which is a step up but still not enough for more complex tasks.

For example, an AI used to distribute project workload and automatically assign tasks to team members would not be able to draw on the experience of past projects and information about each team member's skill set without the ability to store that information in the first place.

AI agents aim to extend LLMs capabilities by connecting them to vector databases like Pinecone or Chroma that serve as AI equivalents of human long-term memory.

In other words, vector databases give LLMs a scalable place where they can store and retrieve data points in a multi-dimensional space. It allows deep generative models to "remember" and "recall" information, similar to how humans store and retrieve memories.

But not all agents are created equal. Here are some of the top contenders in the space.

Auto-GPT is an open-source project developed by Toran Bruce Richards and released on GitHub on March 30 2023. Like other agents, Auto-GPT is a Python application that uses OpenAI's GPT-3.5 and GPT-4 APIs to automatically plan and execute tasks.

The workflow is simple. All you need to do is type a goal in a terminal - it can be something as simple as creating a grocery list or more complex like booking a flight - and Auto-GPT will start prompting the LLM in a loop until it completes the task.

Nakajima's BabyAGI is a streamlined version of his task-driven Autonomous Agent he originally published in late March 2023. The agent consists of 140 lines of Python code that, just like Auto-GPT, uses LanChaing to communicate with GPT-4 and Pinecone to store data.

In late April, Yohei released a beefed-up version of the script called BabyBeeAGI. The agent can now stop once it's completed a task instead of running in a continuous loop. It can also handle more complex task prioritization and auto-assign the most relevant tools for the objective.

Who said that AI can't have its share of fun? In a peculiar experiment at Stanford University, researchers created a virtual sandbox for AI agents to interact with each other. The project involved 25 agents, each with a unique identity, occupation, and memories.

The results? Left alone, agents started interacting with each other, engaging in conversations, and bonding in human-like relationships. They even coordinated social events and shared their experiences in an eerily organic way, all without human intervention.

CAMEL (Communicative Agents for "Mind" Exploration of Large Scale Language Model Society) takes a slightly different approach to agent architecture. It enables role-playing-style interactions between two independent agents, each working on a specific part of a task.

You can test-run CAMEL on the project's website and watch a chat between agents in real-time as they collaborate on the objective. All you need to do is assign agent roles, choose the objective, and set the number of messages or interactions you want the tool to generate.

It's been only a few months since ChatGPT's debut, but the technological advancements in the AI space have already transformed existing task and project management processes.

At Taskade, we're using OpenAI's GPT-4 language model to power our AI Chat functionality that allows project teams to ask AI questions, generate structured workflows, and populate projects with content. Our AI writing assistant helps generate outlines, write long-form documents, and improve writing, all seamlessly integrated into the existing project workflow.

Bringing AI agents into the fold seems like the next logical step in AI-assisted project management. Giving AI a degree of autonomy and a specific task to complete can free up time for higher-level, creative tasks, and unlock new possibilities for workflow optimization.

For instance, Auto-GPT can effectively double as a powerful market research tool. Combined with access to the Internet, it can perform a competitive analysis, complete with pros and cons of each competitor. It can even create its side-kick agents to speed up the process.

Whoa.. still not convinced of AI Agents? This might change your mind...

I pretended to be a fake shoe company and gave AutoGPT a simple objective:
- Do market research for waterproof shoes
- Get the top 5 competitors and give me a report of their pros & cons

Here's how it went: pic.twitter.com/mFttG4PXrk
- Sully (@SullyOmarr) April 9, 2023

But let's take it a step further.

Chances are you're spending hours in your inbox, replying to emails, sending project updates, and scheduling meetings. There's an agent Gmail integration that can answer your questions and schedule Google Calendar events inside your inbox, without breaking the workflow.

So, what about more complex tasks?

Let's say you have a team of designers and developers tasked with building a website. Instead of manually building prototypes, you can ask Auto-GPT to generate a basic structure for the page while your team focuses on the more creative, high-level tasks.

Alright, this is getting too crazy. Soon you won't even need to code anymore.

I setup AutoGPT and it I asked it to build a website for me.

And it succeeded. In under 3 minutes. Using react and tailwindcss. All by itself. pic.twitter.com/OW7qSNqq2B
- Sully (@SullyOmarr) April 7, 2023

Of course, these are only a handful of projects taking advantage of agent architecture. One day, agents may cover the entire project management lifecycle, from planning to monitoring. And with AI behind the wheel, we might finally retire the old saying: "time, cost, quality: pick two."

Agents like Auto-GPT and BabyAGI are still in their infancy (pun not intended). But they already offer a promise of a much more efficient use of existing AI models.

We're likely to see more multimodal systems that can perform a variety of tasks that nowadays require switching between models. For instance, Microsoft's JARVIS (a blatantly obvious reference to Iron Man's personal assistant, minus Paul Bettany's silky-smooth voice) can use one of over 20 AI models depending on the task and execute it in a loop like other agents do.

There's also the question of whether we can make existing models cost-effective and user-friendly enough to be run continuously with minimal supervision. Top-tier LLM APIs like GPT-4 are still pricey and require know-how to properly implement into existing workflows.

One solution to this problem could be integration with more user-friendly web interfaces. A good example of this approach is the Cognosys project that brings the power of BabyAGI and Auto-GPT to the web browser in a simple chat-like interface we know from ChatGPT.

Once we eliminate those bottlenecks, agents may become an integral part of more diverse workflows. From project management to healthcare, finance to manufacturing, agent architecture can change the way we think about decision-making and task delegation.

In 2003, philosopher Nick Bostrom designed a thought experiment that described an artificial intelligence with a single, immutable goal - manufacturing paperclips.

In the experiment, an AI is given the goal of producing as many paperclips as possible. As the system becomes more advanced and capable, it begins to maximize its paperclip production by taking over resources and eliminating any obstacles in its way, including us, humans.

"The AI will realize quickly that it would be much better if there were no humans because humans might decide to switch it off. Because if humans do so, there would be fewer paper clips."
Nick Bostrom in an interview with Huffington Post⁽⁵⁾

At this point, AI agents are just smart but amusing toys we still haven't figured out how to use properly. But if the technology grows, we'll need to put safeguards in place before we fully shed control in high-stakes tasks that currently require an adult (human) in the room.

In Christopher Nolan's Inception, a business magnate hires a professional team of "extractors" to plant an idea in the mind of his business rival... through dream-sharing.

Prompt injections are a fundamental security vulnerability of AI systems that works in a similar way. By inserting a specific set of instructions into the input data, it's possible to steer AI models toward generating outputs that are more aligned with a particular goal or agenda.

For example, an autonomous HR agent designed to identify high-performing job candidates may inadvertently reinforce gender or racial biases if it is trained on a biased dataset. Or an AI-based financial advisor could be manipulated to invest in a specific set of stocks.

An agent with access to sensitive information and resources, running in an endless loop on data contaminated by injected prompts is a scalability problem we'll need to face at some point.

AI models can hallucinate, just like humans. Well, not exactly like us (they don't need psychoactive substances to do that), but the results can be equally mind-bending.

According to OpenAI, the hallucination rate for GPT-4 is 40% lower than for GPT-3. This may seem like a huge improvement, but it's still enough to compound when put into a recursive loop where the agent keeps prompting the model and feeding on accumulating errors.

So, what can stop AI from spewing out endless stream nonsense? Higher quality and more coherent data sources seem like a good start, but that's not nearly enough.

There are other methods of keeping AI on track like retrieval-augmented generation that involves two neural networks where one acts like a fact-checker for its peer. But so far, even this combination produces spotty results that still require human intervention.

Are autonomous agents the future of AI? Or are they just a small step in AI evolution?

The current generation of autonomous agents led by Auto-GPT and BabyAGI has shown some promise in streamlining workflows and improving efficiency, but we're only just beginning to scratch the surface of the full potential of agent architecture.

As the technology matures, we can expect more seamless integrations between AI agents and existing project management workflows. Only time will tell how far AI agents will go, but one thing is certain: the future of AI looks bright, and we're excited to see where it takes us!

💡 Before you go... Want to learn more about natural language processing (NLP), machine learning (ML), and other fundamental topics in artificial intelligence? Read our article on What Is Open AI and check the list of some of the best AI tools for productivity next.

What is Autonomous Task Management?

What are autonomous processes?

Can anyone use Auto-GPT?

How good is Auto-GPT?

What is LangChain used for?

🔗 Resources

1. https://www.cs.cmu.edu/~jcb/

2. https://yoheinakajima.com/task-driven-autonomous-agent-utilizing-gpt-4-pinecone-and-langchain-for-diverse-applications/

3. https://arxiv.org/pdf/2304.03442.pdf

4. https://arxiv.org/abs/2303.17580

5. https://www.huffpost.com/entry/artificial-intelligence-oxford_n_5689858

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Autonomous Task Management: The Next Productivity Revolution, Powered by AI Agents

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In the beginning, there was ChatGPT, an AI chatbot based on the GPT-3.5 large language model (LLM) developed by OpenAI. And ChatGPT was smart. It had wit, charm, and an almost encyclopedic knowledge of language and culture. But ChatGPT was flawed in its design - it lacked autonomy and volition to perform complex tasks without human supervision.

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Red Boost™ Natural Ingredients | Official Store USA

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Red Boost Is A Powerful New Formula For Male Performance That Increases Blood Flow And Helps You Last Longer In Bed.

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Note: This post may contain affiliate links. Click Here to read our full affiliate disclosure.

Wondering which one is right for you?

This can be a challenge.

They're both in Nordictrack's "Commercial" series of treadmills. These treadmills are built tougher than the entry-level models.

And they look very much alike, both with iFit technology, sleek designs, incline and decline.

The Nordictrack Commercial 1250 is a brand new model introduced this year. It's the most affordable of all of the Commercial treadmills as of this writing.

The Nordictrack Commercial 1750 is by far Nordictrack's most popular treadmill of all time. It has a long history (with updates every year) and was even called "The Best Overall Treadmill" by Runner's World.

So it's a tough act to follow.

Which treadmill is right for you?

In this post, we'll cover the main differences between the Nordictrack 1250 vs 1750 treadmills. So you can choose the best fit for you:

Nordictrack 1250 vs 1750 Treadmill - Price

The Nordictrack 1250 treadmill has the edge here. It's the more affordable of the treadmills - coming in around $1999 with Free Shipping and a Free Gift Here.

The Nordictrack 1750 treadmill is the next model up and a few hundred more. You can currently get it for around

So is the extra money worth it on the 1750 treadmill? Here are some of the differences between the two treadmills to help you decide:

Nordictrack 1250 vs 1750 - Console

The console is really where you see a large difference between these two treadmills.

While they both come with full-color, HD touch-screen consoles, the Nordictrack 1250 screen is 10-inches across:

The Nordictrack 1750 treadmill screen is larger at 14-inches across:

So you can see that the 1750 screen is easier to read. It's also more enjoyable to use - especially with the iFit scenery and HD workout videos.

It's kind of like having a bigger phone or computer screen - for many people bigger is better.

Note that both consoles swivel and tilt . So you can find the best viewing angle for you.

And you can swivel either console - for example, if you want to get off the treadmill and continue your workout on the floor.

Here's another view of the Nordictrack 1250 screen:

And here's the Nordictrack 1750 screen:

Nordictrack 1250 vs 1750 - Motor Power

The Nordictrack 1750 treadmill has a slightly more powerful motor than the 1250 treadmill - 3.5 CHP vs 3.0 CHP.

What does this mean for you?

Basically it means that the 1750 can take a bit more use and abuse. If you plan on longer workouts or running frequently, the stronger motor is better and will last longer.

However for moderate use, the 3.0 CHP motor should be fine. It's still more than adequate.

Nordictrack 1750 vs 1250 Treadmill: Incline

This is interesting and I'm not sure how long this difference will last.

Both treadmills give you up to 3% decline.

However the Nordictrack 1750 treadmill gives you more incline - 15% (vs 12% on the Nordictrack 1250 treadmill).

As of this writing though, Nordictrack is moving to close out this particular 1750 treadmill model and will most likely be replacing it with a new model.

Since they've reduced the incline on the higher-end Nordictrack 2450 treadmill, it's safe to assume that the new Nordictrack 1750 will come with the same 12% incline that you find on the Nordictrack 1250.

However for now, you do still get a higher incline on the Nordictrack 1750 treadmill.

Nordictrack Commercial 1250 or 1750: Cup Holders

Again, this will probably change with any new 1750 treadmill models that Nordictrack introduces this year.

However, the current Nordictrack 1750 does not have cup holders on the console. A lot of people don't like this - which is probably why Nordictrack added cupholders on the new 1250 model.

So if you want the extra space for your water bottle or TV remote, then the 1250 treadmill is the better option here.

Nordictrack 1750 vs 1250: Belt Width

Both treadmills give you the extra-long 60-inch belt that's great for stretching out and running.

However, the 1750 treadmill gives you a wider belt - 22 inches (vs 20 inches on the 1250 treadmill).

It's not a huge difference, and certaintly not a deal breaker. But if you want a bit of extra room, then the Nordictrack 1750 has the advantage here.

Bottom Line?

So which is better when comparing the Nordictrack 1250 vs 1750 treadmills?

Well, they both give you iFit training, extra-long belts and full-color, HD touch-screens.

The Nordictrack 1250 is the newer model and comes in at a more affordable price. It also has cup holders - which are not on the 1750 model.

However the Nordictrack 1750 has a larger, more enjoyable screen. It also has a stronger motor and wider belt.

Plus it has a higher incline (for now) - which many people will prefer as it gives you more room to grow.

So it really depends on what you're looking for at the end of the day.

Regardless of which you choose, you can learn more about each treadmill direct from the manufacturer with the links below:

Related:
Nordictrack 1250 Treadmill Review - A Good Buy for You? Nordictrack 1750 Treadmill Review - A Good Buy for You?

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Nordictrack 1250 vs 1750 Treadmill Comparison - Which is Best for You?

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Note: This post may contain affiliate links. Click Here to read our full affiliate disclosure. Wondering which one is right for you? This can be a challenge. They're both in Nordictrack's "Commercial" series of treadmills. These treadmills are built tougher than the entry-level models.

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Python Interview Questions | 90+ Q&A to Get a Data Science Job

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Python Interview Questions | 90+ Q&A to Get a Data Science Job

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Introduction

Python is an interpreted and general-purpose programming language that is in high demand nowadays due to its usage in Artificial Intelligence(AI). Therefore, it becomes indispensable for every Data Science aspirant to have a strong understanding of functions used in Python.

Almost every interview for the position of Data Scientist starts with the basics of Machine Learning, where the focus is mostly on Decision Trees, SVM, and kNN models. If the job description includes the term " Deep Learning," then questions on Deep Learning models may be asked. Once the basics have been asked, the interviewer will move to Python coding, where they want to know about your logic and coding skills, so starts with the basics of Python coding, useful for beginners or otherwise. Advanced-level coding is generally not asked in interviews. So, it is always better to have a strong grip on the basics instead of getting into the rat race.

In this article, some important and frequently asked Python coding interview questions and Python programming interview questions will be discussed to help Data Science aspirants get a good grasp of the topic. So, let's start.

Table of Contents

Python Coding Interview Questions

Q1. Convert a given string to int using a single line of code.

Ans. We can convert a given string to an integer using a built-in function int(). e.g.-

a = '5' print(int(a))

Variable 'a' is a string that is now converted to an integer, as shown below:
Output:
5

Q2. Write a code snippet to convert a string to a list.

Ans. Below is the code to convert a string to a list in Python.

str1 = "Analytics Vidhya" print(str1.split(" "))

The split() function separates the given string by the defined delimiter, i.e., space(" ") here. Therefore, Analytics and Vidhya break down into two strings in a list.
Output: ['Analytics', 'Vidhya']
Q3. Write a code snippet to reverse a string.

Ans. Here, we have reversed a string without using any in-built function.

str1 = "Analytics Vidhya" str2 = "" for i in str1: str2 = i + str2

print("The original string is: ",str1) print("The reversed string is: ",str2)

The above code picks the first letter, i.e., 'A', then adds 'n' at the beginning.
Further, 'nA' is taken as str2, ' a' is added before it, and so on.
Then 'anA' becomes str2, and the next letter, i.e., 'l', is appended at the start of str2 to make it 'lanA.'
This is how the above code works to reverse the string.
Output:
ayhdiV scitylanA

Q4. Write a code snippet to sort a list in Python.

Ans. The list in Python can be sorted using the sort() function. The following code will illustrate this:

my_list = [3, 2, 1] my_list.sort() print(my_list)

The above code sort the list using the sort() function.
Output: [1, 2, 3]
Q5. What is the difference between mutable and immutable?

Ans. Mutable objects: They can't be updated once defined. e.g., list.
Immutable objects: They can be updated when required. e.g., tuples.

Q6. How can you delete a file in Python?

Ans. The file in python can be deleted using the os module. The remove() function of the os module is used to delete a file in Python by passing the filename as a parameter. e.g.

import os os.remove("txt1.txt")

Q7. How to access an element of a list?

Ans. The element in a list can be accessed using list_name [index]. For instance:

Given a list [1, 2, 3, 4].

The indexing of the list starts from 0.

The first element of the list can be accessed using list[0], which will print element "1".

The second element can be accessed using list[1] and so on.

Q8. Discuss different ways of deleting an element from a list.

Ans. Two ways in which elements can be deleted from the list:

1. By using the remove() function

The remove () function deletes the mentioned element from the list.

list1 = [1, 2, 3, 4] list1.remove(2) print(list1)
Output: [1, 3, 4]
2. By using the pop() function

Pop() function delete element mentioned at a specific index from the list

list1.pop(1) print(list1)
Output: [1, 4]
Q9. Write a code snippet to delete an entire list.

Ans. A list in python can be deleted by using the clear() function. The following code will illustrate this:

list1 = [1, 2, 3, 4] list1.clear()

It will delete the entire list.

Q10. Write a code snippet to reverse an array.

Ans. The two ways of reversing an array are as follows:

1. Using the flip() function

import numpy as np arr1=np.array([1, 2, 3, 4]) arr2=np.flip(arr1) print(arr2)
Output: [4, 3, 2, 1]
2. Without using any function

import numpy as np arr1=np.array([1, 2, 3, 4]) arr2= arr1[::-1] print(arr2)
Output: [4,3,2,1]
Q11. Write a code snippet to get an element, delete an element, and update an element in an array.

Ans. Access: The element of an array can be accessed by using array_name[index].

print(arr[index])

Delete: The element of an array can be deleted using the delete() function.

arr2 = [1,2,3,4] import numpy as np x= np.delete(arr2,0) print(x)

Output:
[2,3,4]
Update: The element of an array can be using the below syntax:

array_name[index] = element

Q12. Write a code snippet to concatenate lists.

Ans. Suppose, given two lists are:

List1= ["W", "a", "w","b"]

List2 = ["e", " ","riting","log"]

And the output should be:
['We', 'a ', 'writing', 'blog']
This can be done using zip() function, which iterates through both lists and combines them index-wise.

lst1= ['W', 'a', 'w','b'] lst2 = ['e', ' ','riting','log'] lst3 = [x + y for x, y in zip(lst1, lst2)] print(lst3)
Output: ['We', 'a ', 'writing', 'blog']
Q13. Write a code snippet to generate the square of every element of a list.

Ans. First, create an empty list. We used a for loop to iterate through every element of a list and multiply the element by itself to generate a square of it. Then, append it to the newly generated list.

lst = [1, 2, 3, 4] lst_final = [] for x in lst: lst_final.append(x * x) print(lst_final)

The for loop takes the first element, i.e., 1, multiply it with itself, and then appends it to the list. Then the second element, i.e., 2 is taken, multiplied by itself, and appended to the list, and so on.

Input: [1, 2, 3, 4]

Output: [1, 4, 9, 16]

Q14. What is the difference between range and xrange?

Ans. In Python 2, range() returns a list, and xrange() returns an iterator. But in Python 3 xrange() no longer exists while range() returns an iterator.

Q15. What is pickling and unpickling in python?

Ans. Pickling is converting a Python object (list, dict, function, etc.) to a byte stream(0s and 1s), and unpickling is converting the byte stream back to a python object. It is used to transfer and store various python objects. We can use pickle or dill python packages for this.

Q16. What is init in python?

Ans. __ init__ method is used in python to initialize the attributes of the object when the object is created. So, it is similar to the constructor in java or C++. It is declared within the class as a reserved method.

Q17. What is the PEP-8 Style Guide?

Ans. It is the recommended coding conventions guide to follow for easier readability of the code. Since Multiple people work on the same project, it is preferable to follow a similar style for better readability and consistency. However, we can use our judgment about what style to follow, If there is any need to deviate from conventions.

Q18. Which is faster, python list or Numpy arrays, and why?

Ans. NumPy arrays are faster than Python lists for numerical operations.

NumPy is an open-source library for working with arrays in Python, and it provides a number of functions for performing operations on arrays efficiently.

They are faster than Python lists because they are implemented in C, while Python lists are implemented in Python. This means that operations on NumPy arrays are implemented in a compiled language, which makes them faster than operations on Python lists, which are implemented in an interpreted language.

Python also has an inbuilt array library for basic operations. W can use it as 'import array as arr'

Q19. What is the difference between a python list and a tuple?

Ans. In Python, a list is an ordered collection of objects that can be of different types. Lists are mutable, which means that you can change the value of a list element or add or remove elements from a list. Lists are created using square brackets and a comma-separated list of values.

A tuple is also an ordered collection of objects, but it is immutable, which means that you cannot change the value of a tuple element or add or remove elements from a tuple.

Lists are defined using square brackets ([ '' ]), while tuples are defined using parentheses (('', )).

Lists have several built-in methods for adding, removing, and manipulating elements, while tuples do not have these methods.

In general, tuples are faster than lists in Python.

Q20. What are python sets? Explain some of the properties of sets.

Ans. In Python, a set is an unordered collection of unique objects. Sets are often used to store a collection of distinct objects and to perform membership tests (i.e., to check if an object is in the set). Sets are defined using curly braces ({ and }) and a comma-separated list of values.

Here are some key properties of sets in Python:

Sets are unordered: Sets do not have a specific order, so you cannot index or slice them like you can with lists or tuples.

Sets are unique: Sets only allow unique objects, so if you try to add a duplicate object to a set, it will not be added.

Sets are mutable: You can add or remove elements from a set using the add and remove methods.

Sets are not indexed: Sets do not support indexing or slicing, so you cannot access individual elements of a set using an index.

Sets are not hashable: Sets are mutable, so they cannot be used as keys in dictionaries or as elements in other sets. If you need to use a mutable object as a key or an element in a set, you can use a tuple or a frozen set (an immutable version of a set).

Q21. What is the difference between split and join?

Ans. Split and join are both functions of python strings, but they are completely different when it comes to functioning.

The split function is used to create a list from strings based on some delimiter, for e.g., space.

Eg. a = 'This is a string'

Li = a.split(' ')

print(li)

Output: ['This', 'is', 'a', 'string']

The join() method is a built-in function of Python's str class that concatenates a list of strings into a single string. It is called on a delimiter string and invoked with a list of strings to be joined. The delimiter string is inserted between each string in the list when the strings are concatenated.

Here is an example of how to use the join() method:

E.g. " ".join(li)

Output: This is a string

Here the list is joined with a space in between.

Q22. Explain the logical operations in python.

Ans. In Python, the logical operations and, or, and not can be used to perform boolean operations on truth values (True and False).

The and operator returns True if both the operands are True, and False otherwise.

The or operator returns True if either of the operands is True, and False if both operands are False.

The not operator inverts the boolean value of its operand. If the operand is True, not return False, and if the operand is False, not return True.

Q23. Explain the top 5 functions used for python strings.

Ans. Here are the top 5 Python string functions:

len(): This function returns the length of a string.

s = 'Hello, World!'

len(s)

13

strip(): This function removes leading and trailing whitespace from a string.

s = ' Hello, World! '

s.strip()

'Hello, World!'

split(): This function splits a string into a list of substrings based on a delimiter.

s = 'Hello, World!'

s.split(',')
['Hello', ' World!']
replace(): This function replaces all occurrences of a specified string with another string.

s = 'Hello, World!'

s.replace('World', 'Universe')

'Hello, Universe!'

upper() and lower(): These functions convert a string to uppercase or lowercase, respectively.

s = 'Hello, World!'

s.upper()

'HELLO, WORLD!'

s.lower()

'hello, world!'

In addition to them, string also has capitalize, isalnum, isalpha, and other methods.

Q24. What is the use of the pass keyword in python?

Ans. pass is a null statement that does nothing. It is often used as a placeholder where a statement is required syntactically, but no action needs to be taken. For example, if you want to define a function or a class but haven't yet decided what it should do, you can use the pass as a placeholder.

Q25. What is the use of the continue keyword in python?

Ans. continue is used in a loop to skip over the current iteration and move on to the next one. When continue is encountered, the current iteration of the loop is terminated, and the next one begins.

Q26. What are immutable and mutable data types?

Ans. In Python, an immutable object is an object whose state cannot be modified after it is created. This means that you can't change the value of an immutable object once it is created. Examples of immutable objects in Python include numbers (such as integers, floats, and complex numbers), strings, and tuples.

On the other hand, a mutable object is an object whose state can be modified after it is created. This means that you can change the value of a mutable object after it is created. Examples of mutable objects in Python include lists and dictionaries.

Understanding the difference between immutable and mutable objects in Python is important because it can affect how you use and manipulate data in your code. For example, if you have a list of numbers and you want to sort the list in ascending order, you can use the built-in sort() method to do this. However, if you have a tuple of numbers, you can't use the sort() method because tuples are immutable. Instead, you would have to create a new sorted tuple from the original tuple.

Q27. What is the use of try and except block in python?

Ans. The try and except block in Python are used to handle exceptions. An exception is an error that occurs during the execution of a program.

The try block contains code that might cause an exception to be raised. The except block contains code that is executed if an exception is raised during the execution of the try block.

Using a try-except block will save the code from an error to occur and can be executed with a message or output we want in the except block.

Q28. What are 2 mutable and 2 immutable data types in python?

Ans. 2 mutable data types are Dictionary and List. You can change/edit the values in a python dictionary and a list. It is not necessary to make a new list which means that it satisfies the property of mutability.

2 immutable data types are Tuples and String. You cannot edit a string or a value in a tuple once it is created. You need to either assign the values to the tuple or make a new tuple.

Q29. What are python functions, and how do they help in code optimization?

Ans. In Python, a function is a block of code that can be called by other parts of your program. Functions are useful because they allow you to reuse code and divide your code into logical blocks that can be tested and maintained separately.

To call a function in Python, you simply use the function name followed by a pair of parentheses and any necessary arguments. The function may or may not return a value that depends on the usage of the turn statement.

Functions can also help in code optimization:

1. Code reuse: Functions allow you to reuse code by encapsulating it in a single place and calling it multiple times from different parts of your program. This can help to reduce redundancy and make your code more concise and easier to maintain.

2. Improved readability: By dividing your code into logical blocks, functions can make your code more readable and easier to understand. This can make it easier to identify bugs and make changes to your code.

3. Easier testing: Functions allow you to test individual blocks of code separately, which can make it easier to find and fix bugs.

4. Improved performance: Functions can also help to improve the performance of your code by allowing you to use optimized code libraries or by allowing the Python interpreter to optimize the code more effectively.

Q30. Why does NumPy have huge popularity in the field of data science?

Ans. NumPy (short for Numerical Python) is a popular library for scientific computing in Python. It has gained a lot of popularity in the data science community because it provides fast and efficient tools for working with large arrays and matrices of numeric data.

NumPy provides fast and efficient operations on arrays and matrices of numerical data. It uses optimized C and Fortran code behind the scenes to perform these operations, which makes them much faster than equivalent operations using Python's built-in data structures. It provides fast and efficient tools for working with large arrays and matrices of numeric data.

NumPy provides a large number of functions for performing mathematical and statistical operations on arrays and matrices.

It allows you to work with large amounts of data efficiently. It provides tools for handling large datasets that would not fit in memory, such as functions for reading and writing data to disk and for loading only a portion of a dataset into memory at a time.

NumPy integrates well with other scientific computing libraries in Python, such as SciPy (Scientific Python) and pandas. This makes it easy to use NumPy with other python libraries to perform more complex data science tasks.

Q31. Explain list comprehension and dict comprehension.

Ans. List comprehension and dict comprehension are both concise ways to create new lists or dictionaries from existing iterables.

List comprehension is a concise way to create a list. It consists of square brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result is a new list that evaluates the expression in the context of the for and if clauses.

Dict comprehension is a concise way to create a dictionary. It consists of curly braces containing a key-value pair, followed by a for clause, then zero or more for or if clauses. A result is a new dictionary that evaluates the key-value pair in the context of the for and if clauses.

Q32. What are global and local variables in python?

Ans. In Python, a variable that is defined outside of any function or class is a global variable, while a variable that is defined inside a function or class is a local variable.

A global variable can be accessed from anywhere in the program, including inside functions and classes. However, a local variable can only be accessed within the function or class in which it is defined.

It is important to note that you can use the same name for a global variable and a local variable, but the local variable will take precedence over the global variable within the function or class in which it is defined.

# This is a global variable

x = 10

def func():

# This is a local variable

x = 5

print(x)my_function

func()

print(x)

Output: This will print 5 and then 10

In the example above, the x variable inside the func() function is a local variable, so it takes precedence over the global variable x. Therefore, when x is printed inside the function, it prints 5; when it is printed outside the function, it prints 10.

Q33. What is an ordered dictionary?

Ans. An ordered dictionary, also known as an OrderedDict, is a subclass of the built-in Python dictionary class that maintains the order of elements in which they were added. In a regular dictionary, the order of elements is determined by the hash values of their keys, which can change over time as the dictionary grows and evolves. An ordered dictionary, on the other hand, uses a doubly linked list to remember the order of elements, so that the order of elements is preserved regardless of how the dictionary changes.

Q34. What is the difference between return and yield keywords?

Ans. Return is used to exit a function and return a value to the caller. When a return statement is encountered, the function terminates immediately, and the value of the expression following the return statement is returned to the caller.

Yield, on the other hand, is used to define a generator function. A generator function is a special kind of function that produces a sequence of values one at a time instead of returning a single value. When a yield statement is encountered, the generator function produces a value and suspends its execution, saving its state for later.

Q35. What are lambda functions in python, and why are they important?

Ans. Python supports the lambda function, which is a small anonymous function. You can use lambda functions when you don't want to define a function using the def keyword.

Lambda functions are useful when you need a small function for a short period of time. They are often used in combination with higher-order functions, such as map(), filter(), and reduce().

Here's an example of a lambda function in Python:

a = lambda x : x + 10

a(5)

15

In this example, the lambda function takes one argument (x) and adds 10 to it. The lambda function returns the result of this operation when it is called.

Lambda functions are important because they allow you to create small anonymous functions in a concise way. They are often used in functional programming, a programming paradigm that emphasizes using functions to solve problems.

Q36. What is the use of the 'assert' keyword in python?

Ans. In Python, the assert statement is used to test a condition. If the condition is True, then the program continues to execute. If the condition is False, then the program raises an AssertionError exception.

The assert statement is often used to check the internal consistency of a program. For example, you might use an assert statement to check that a list is sorted before performing a binary search on the list.

It's important to note that the assert statement is used for debugging purposes and is not intended to be used as a way to handle runtime errors. In production code, you should use try and except blocks to handle exceptions that might be raised at runtime.

Q37. What are decorators in python?

Ans. In Python, decorators are a way to modify or extend the functionality of a function, method, or class without changing their source code. Decorators are typically implemented as functions that take another function as an argument and return a new function that has the desired behavior.

We can use the decorator function by adding it just before the function we are applying using @decorator_function syntax.

Q38. What are built-in data types in Python?

Ans. The 5 built-in data types in Python are: None Type, Numeric Types (int, float, complex, bool), Sequence Types (list, tuple, range, str), Map Type (dictionary), and Set types (set, frozenset).

Q39. What's the difference between a set and a frozenset?

Ans. A frozenset is an immutable variant of a set, like how a tuple is an immutable variant list.

Q40. Where can we use a tuple instead of a list?

Ans. We can use tuples as dictionary keys as they are hashable. Since tuples are immutable, it is safer to use if we don't want values to change. Tuples are faster and have less memory, so we can use tuples to access only the elements.

Q41. Is removing the first item or last item takes the same time in the Python list?

Ans. No, removing the last item is O(1), while removing the first item is O(n)

Q42. How can we remove any element from a list efficiently?

Ans. We can use a deque from the collections module, which is implemented as a doubly linked list, to remove elements faster at any index

Q43. What is negative indexing?

Ans. Python sequence data types can be accessed with both positive and negative numbers. With negative indexing, -1 refers to the last element, -2 refers to the penultimate element, and so on

Q44. Why do floating-point calculations seem inaccurate in Python?

Ans. While representing floating point numbers like 2.5 is easier in the decimal system, representing the same in a binary system needs a lot of bits of information. Since the number of bits is limited, there can be rounding errors in floating-point calculations.

Q45. What is docstring in python?

Ans. Docstring is the short form for documentation string. It is a type of string used to document how a function or a class works, its various input parameters, and any examples for the usage of the function or class. It is enclosed by triple quotes(""" """). A well-written docstring helps people to understand the usage of the function or class.

Q46. What are args and *kwargs in Python?

Ans. args and *kwargs are syntax used for denoting variable length arguments and variable length keyword arguments, respectively. Here and * represents the syntax, while the args and kwargs are words used by convention. We can also use other words.

Q47. What are generators in Python?

Ans. A generator in Python returns an iterator that produces sequences of values one at a time. We use the yield keyword to produce values.

Q48. What is the use of generators?

Ans. Since the generator doesn't produce all the values at the same time, it saves memory if we use the generator to process the sequence of values without the need to save the initial values.

Q49. How can we iterate through multiple lists at the same time?

Ans. We can use zip() function to aggregate multiple lists and return an iterator of tuples where each tuple contains elements of different lists of the same index.

Q50. What are the various ways of adding elements to a list?

1. We can use insert() to add a given index to the existing list.

2. We can use append() to add at the end of a list a single item.

3. We can use extend() to add each element of an iterable(list, tuple, or set) separately at the end of the list.

4. We can also use the + operator to concatenate two lists, similar to extend, but it works only with one list to another list but not one list to another tuple or set.

Q51. Write a program to check whether a number is prime or not.

Ans. def prime_or_not(number):

for i in range(2, int(sqrt(number))+1):

if n % i == 0:

return 0

return 1

Q52. What is the time complexity of the above program, and is there a faster way to do it?

Ans. It has a time complexity of O(sqrt(n)). We can do it faster with the sieve of Eratosthenes.

Q53. What are the ways to swap the values of two elements?

Ans. One way is by using a third variable

temp = a

a = b

B = temp

In Python, we can also do it without using the third variable

a, b = b, a

Q54. Write a program in Python to return the factorial of a given number using recursion.

Ans. def factorial(n):

if n == 1:

return n

else:

return n*factorial(n-1)

Q55. Is there a way to calculate factorial faster than above?

Ans. We can use the divide and conquer algorithm technique to calculate faster. It is implemented in a built-in math library with math.factorial().

Q56. How do you find the minimum value in a list with a lambda function?

Ans. reduce(lambda x, y : x if x < y else y, b)

This will give the minimum value in the list

Q57. Write a code to convert a list of characters to a string of characters separated by a comma.

Ans. ','.join(list)

Q58. Write a code to select only odd numbers using list comprehension.

Ans. [i for i in range(n) if i%2 != 0]

Q59. What is the difference between del and remove on lists?

Ans. 'Remove' removes the first occurrence of a given element. 'Del' removes elements based on the given index.

Q60. Write a code to get the minimum value in a dictionary.

Ans. dict_[min(dict_.keys(), key=(lambda k: dict_[k]))

Q61. Write a program to return the mean value of a tuple of tuples.

Ans. def mean_tuple(numbers):

result = [sum(x) / len(x) for x in zip(*numbers)]

return result

Q62. What is the use of self in Python?

Ans. Self is used to represent the instance of the class. With this, we can access the attributes and methods of the class with an object. It binds the attributes of the object with the given arguments. Self is not a keyword. We can also use any other non-keyword though it is better to follow convention.

Q63. What are the different types of variables in Python OOP?

Ans: The 3 different types of variables in Python OOP (object-oriented programming) are:

1. Class variables: They are defined inside the class but outside other methods and are available to access for any instance of the class.

2. Instance variables: They are defined for each instance of the class separately and accessible by that instance of the class.

3. Local variables: They are defined inside the method and accessible only inside that method.

Q64. What are the different types of methods in Python OOP?

Ans: The 3 different types of methods in Python OOP are:

1. Class methods: They can access only class variables and are used to modify the class state.

2. Instance methods: They can access both class and instance variables and are used to modify the object (instance of a class) state.

3. Static methods: They can't access either class or instance variables and can be used for functions that are suitable to be in class without accessing class or instance variables.

Q65. What is inheritance, and how is it useful?

Ans. With inheritance, we can use the attributes and methods of the parent class in the child class. We can also modify the parent class methods to suit the child class.

Q66. What are access specifiers?

Ans. We can use (underscore) or _(double underscore) to denote protected and private variables. They can be used to restrict access to the attributes of the class.

Q67. In numpy, how is array[:, 0] is different from array[:[0]]?

Ans. array[:, 0] returns 1 st column as 1D array. array[:, [0]] returns 1st columns as multi-dimensional array.

Q68. How can we check for an array with only zeros?

Ans. We can use the size method of the array to check whether it returns 0. Then it means the array can contain only zeros.

Q69. How can we concatenate two arrays?

Ans. We can use concatenate method.

a = np.array([[10, 4], [8, 12]])

b = np.array([[15, 6]])

c = np.concatenate((a,b), axis=0)

The result will be [[10, 4] [8, 12] [15, 6]]

Q70. How can we check for the local maxima of an array?

Ans. A local maxima is a point greater than both of its neighbors from either side. In a given array, the following code can produce local maxima points.

np.where((arr[1:-1] > arr[0:-2]) * (arr[1:-1] > arr[2:]))[0] + 1

Q71. What's the difference between split() and array_split()?

Ans. The split() function is used to split an array in n number of equal parts. If it is not possible to split it into an equal number of parts, split() raises an error. On the other hand, array_split() splits an array into n unequal parts.

Q72. Write code to insert commas between characters of all elements in an array.

Ans. resulted_arr = np.char.join(",", array)

Q73. How can you add 1 to all sides of an existing array?

Ans. We can use the pad method of numpy.

New_arr = np.pad(existing_arr, pad_width=1, mode='constant', constant_values=1)

Q74. How can we swap axes of a numpy array?

Ans. We can use the swapaxes method.

np.swapaxes(arr,0,1)

Q75. How to get the indices of n maximum values in a given array?

Ans. argsort() method returns indices that can sort the array.

array.argsort( ) [ -N: ][: : -1] gives indices of n maximum values.

Q76. What is categorical data in pandas?

Ans. When a variable takes a limited number of possible values, we can use category datatype for that variable which is internally represented with numbers, so faster to process.

Q77. How can we transform a true/false value to 1/0 in a dataframe?

Ans. df["column"] = df["column"].astype(int)

Q78. How are loc() and iloc() different?

Ans. loc() is used to access the rows or columns using labels, while iloc() is used to access using position based indexing.

Q79. How do you sort a dataframe by two columns?

Ans. df.sort_values(by=['col1', 'col2'])

Q80. Find the row which has the maximum value of a given column.

Ans. We can use idmax method for that.

df['column'].idxmax() return row index with the maximum value of the column specified.

Q81. How can you split a column which contains strings into multiple columns?

Ans. df['column'].str.split(pat=" ", expand=True)

Here we can specify the pattern by which to split the column with the pat argument.

Q82. What is the difference between map() and applymap()?

Ans. While they can be used for elementwise operations, map() is used for series, while applymap() is used for dataframes.

Q83. How can we convert the True values of a query to False and vice-versa?

Ans. We can use the tilde(~) operator to convert True values to False and vice versa.

Q84. How can we find a change in percentage from one row to another?

Ans. We can use df.pct_change(periods=1) to find the percentage change between the previous row to the current row.

Q85. How can we find the coefficient of variance for data frame columns?

Ans. Since coefficient of variance is standard deviation divided by mean, we can use df.std()/df.mean()

Q86. How can we remove the leading whitespace for a string?

Ans. We can use the .lstrip() method to remove the leading whitespace for a string.

Q87. What is enumerate() in python?

Ans. enumerate() in python iterates a sequence and returns the index position and its corresponding value.

Q88. What are deepcopy and shallowcopy?

Ans. Deepcopy copies the contents of the object to another location. Changes made in one object do not affect the other. In shallow copy, only the reference is copied. So, changes made in one affect the other object.

Q89. What is a callable object in Python?

Ans. An object which can invoke a process is a callable object. It uses the call method. Functions are examples of that. Callable objects have () at the end, while non-callable methods don't have () at the end.

Q90. How can we find unique elements and value counts of a list using Numpy?

Ans. We can use numpy.unique(list, return_counts=True)
This will return values and their counts in two arrays.

Q91. What is the difference between indexing and slicing in NumPy?

Ans. Indexing creates an index, whereas slicing makes a shallow copy. Different types of indexing are possible, but there is no slicing category.

Conclusion

Phew! We've finally reached the end of this very comprehensive article. Hope these Python interview questions and answers have been beneficial to you. I'm sure you've all learnt a few things from this and are now confident about your next interview. These coding questions also act as python tutorials for freshers and intermediate-level programmers and cover functions that most Python developers frequently use.

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90+ Python Interview Questions to Ace Your Next Job Interview in 2023

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Python is an interpreted and general-purpose programming language that is in high demand nowadays due to its usage in Artificial Intelligence(AI). Therefore, it becomes indispensable for every Data Science aspirant to have a strong understanding of functions used in Python.

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Hypothesis Testing | Difference between Z-Test and T-Test

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Hypothesis Testing | Difference between Z-Test and T-Test

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Python Join Types | Joins in Pandas | Pandas Join Types

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Python Join Types | Joins in Pandas | Pandas Join Types

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K-Means Clustering Algorithm in Python - The Ultimate Guide

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K-Means Clustering Algorithm in Python - The Ultimate Guide

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Overview

K-Means Clustering is a simple yet powerful algorithm in data science

There are a plethora of real-world applications of K-Means Clustering (a few of which we will cover here)

This comprehensive guide will introduce you to the world of clustering and K-Means Clustering along with an implementation in Python on a real-world dataset

Introduction

I love working on recommendation engines. Whenever I come across any recommendation engine on a website, I can't wait to break it down and understand how it works underneath. It's one of the many great things about being a data scientist!

What truly fascinates me about these systems is how we can group similar items, products, and users together. This grouping, or segmenting, works across industries. And that's what makes the concept of clustering such an important one in data science.

Clustering helps us understand our data in a unique way - by grouping things together into - you guessed it - clusters.

In this article, we will cover k-means clustering and it's components comprehensively. We'll look at clustering, why it matters, its applications and then deep dive into k-means clustering (including how to perform it in Python on a real-world dataset).

And if you want to directly work on the Python code, jump straight here. We have a live coding window where you can build your own k-means clustering algorithm without leaving this article!
To learn more about clustering and other machine learning algorithms (both supervised and unsupervised) check out the following courses-
Table of Contents

1. What is Clustering?

2. How is Clustering an Unsupervised Learning Problem?

3. Properties of Clusters

4. Applications of Clustering in Real-World Scenarios

5. Understanding the Different Evaluation Metrics for Clustering

6. What is K-Means Clustering?

7. Implementing K-Means Clustering from scratch in Python

8. Challenges with K-Means Algorithm

9. K-Means ++ to choose initial cluster centroids for K-Means Clustering

10. How to choose the Right Number of Clusters in K-Means?

11. Implementing K-Means Clustering in Python

What is Clustering?

Let's kick things off with a simple example. A bank wants to give credit card offers to its customers. Currently, they look at the details of each customer and based on this information, decide which offer should be given to which customer.

Now, the bank can potentially have millions of customers. Does it make sense to look at the details of each customer separately and then make a decision? Certainly not! It is a manual process and will take a huge amount of time.

So what can the bank do? One option is to segment its customers into different groups. For instance, the bank can group the customers based on their income:

The groups I have shown above are known as clusters and the process of creating these groups is known as clustering. Formally, we can say that:

Clustering is the process of dividing the entire data into groups (also known as clusters) based on the patterns in the data.

Can you guess which type of learning problem clustering is? Is it a supervised or unsupervised learning problem?

Think about it for a moment and make use of the example we just saw. Got it? Clustering is an unsupervised learning problem!

How is Clustering an Unsupervised Learning Problem?

Let's say you are working on a project where you need to predict the sales of a big mart:

Or, a project where your task is to predict whether a loan will be approved or not:

We have a fixed target to predict in both of these situations. In the sales prediction problem, we have to predict the Item_Outlet_Sales based on outlet_size, outlet_location_type, etc. and in the loan approval problem, we have to predict the Loan_Status depending on the Gender, marital status, the income of the customers, etc.

So, when we have a target variable to predict based on a given set of predictors or independent variables, such problems are called supervised learning problems.

Now, there might be situations where we do not have any target variable to predict.

Such problems, without any fixed target variable, are known as unsupervised learning problems. In these problems, we only have the independent variables and no target/dependent variable.

In clustering, we do not have a target to predict. We look at the data and then try to club similar observations and form different groups. Hence it is an unsupervised learning problem.
We now know what are clusters and the concept of clustering. Next, let's look at the properties of these clusters which we must consider while forming the clusters.

Properties of Clusters

How about another example? We'll take the same bank as before who wants to segment its customers. For simplicity purposes, let's say the bank only wants to use the income and debt to make the segmentation. They collected the customer data and used a scatter plot to visualize it:

Property 1

All the data points in a cluster should be similar to each other. Let me illustrate it using the above example:

If the customers in a particular cluster are not similar to each other, then their requirements might vary, right? If the bank gives them the same offer, they might not like it and their interest in the bank might reduce. Not ideal.

Having similar data points within the same cluster helps the bank to use targeted marketing. You can think of similar examples from your everyday life and think about how clustering will (or already does) impact the business strategy.

Property 2

The data points from different clusters should be as different as possible. This will intuitively make sense if you grasped the above property. Let's again take the same example to understand this property:

Which of these cases do you think will give us the better clusters? If you look at case I:

Customers in the red and blue clusters are quite similar to each other. The top four points in the red cluster share similar properties as that of the top two customers in the blue cluster. They have high income and high debt value. Here, we have clustered them differently. Whereas, if you look at case II:

Points in the red cluster are completely different from the customers in the blue cluster. All the customers in the red cluster have high income and high debt and customers in the blue cluster have high income and low debt value. Clearly we have a better clustering of customers in this case.

Hence, data points from different clusters should be as different from each other as possible to have more meaningful clusters.

So far, we have understood what clustering is and the different properties of clusters. But why do we even need clustering? Let's clear this doubt in the next section and look at some applications of clustering.

Applications of Clustering in Real-World Scenarios

Clustering is a widely used technique in the industry. It is actually being used in almost every domain, ranging from banking to recommendation engines, document clustering to image segmentation.

Customer Segmentation

We covered this earlier - one of the most common applications of clustering is customer segmentation. And it isn't just limited to banking. This strategy is across functions, including telecom, e-commerce, sports, advertising, sales, etc.

Document Clustering

This is another common application of clustering. Let's say you have multiple documents and you need to cluster similar documents together. Clustering helps us group these documents such that similar documents are in the same clusters.

Image Segmentation

We can also use clustering to perform image segmentation. Here, we try to club similar pixels in the image together. We can apply clustering to create clusters having similar pixels in the same group.

You can refer to this article to see how we can make use of clustering for image segmentation tasks.

Recommendation Engines

Clustering can also be used in recommendation engines. Let's say you want to recommend songs to your friends. You can look at the songs liked by that person and then use clustering to find similar songs and finally recommend the most similar songs.

There are many more applications which I'm sure you have already thought of. You can share these applications in the comments section below. Next, let's look at how we can evaluate our clusters.

Understanding the Different Evaluation Metrics for Clustering

The primary aim of clustering is not just to make clusters, but to make good and meaningful ones. We saw this in the below example:

Here, we used only two features and hence it was easy for us to visualize and decide which of these clusters is better.

Unfortunately, that's not how real-world scenarios work. We will have a ton of features to work with. Let's take the customer segmentation example again - we will have features like customer's income, occupation, gender, age, and many more. Visualizing all these features together and deciding better and meaningful clusters would not be possible for us.

This is where we can make use of evaluation metrics. Let's discuss a few of them and understand how we can use them to evaluate the quality of our clusters.

Inertia

Recall the first property of clusters we covered above. This is what inertia evaluates. It tells us how far the points within a cluster are. So, inertia actually calculates the sum of distances of all the points within a cluster from the centroid of that cluster.

We calculate this for all the clusters and the final inertial value is the sum of all these distances. This distance within the clusters is known as intracluster distance. So, inertia gives us the sum of intracluster distances:

Now, what do you think should be the value of inertia for a good cluster? Is a small inertial value good or do we need a larger value? We want the points within the same cluster to be similar to each other, right? Hence, the distance between them should be as low as possible.

Keeping this in mind, we can say that the lesser the inertia value, the better our clusters are.

Dunn Index

We now know that inertia tries to minimize the intracluster distance. It is trying to make more compact clusters.

Let me put it this way - if the distance between the centroid of a cluster and the points in that cluster is small, it means that the points are closer to each other. So, inertia makes sure that the first property of clusters is satisfied. But it does not care about the second property - that different clusters should be as different from each other as possible.

This is where Dunn index can come into action.

Along with the distance between the centroid and points, the Dunn index also takes into account the distance between two clusters. This distance between the centroids of two different clusters is known as inter-cluster distance. Let's look at the formula of the Dunn index:

Dunn index is the ratio of the minimum of inter-cluster distances and maximum of intracluster distances.

We want to maximize the Dunn index. The more the value of the Dunn index, the better will be the clusters. Let's understand the intuition behind Dunn index:

Also, the denominator should be minimum to maximize the Dunn index. Here, we are taking the maximum of intracluster distances. Again, the intuition is the same here. The maximum distance between the cluster centroids and the points should be minimum which will eventually make sure that the clusters are compact.

Introduction to K-Means Clustering

We have finally arrived at the meat of this article!

Recall the first property of clusters - it states that the points within a cluster should be similar to each other. So, our aim here is to minimize the distance between the points within a cluster.

There is an algorithm that tries to minimize the distance of the points in a cluster with their centroid - the k-means clustering technique.

K-means is a centroid-based algorithm, or a distance-based algorithm, where we calculate the distances to assign a point to a cluster. In K-Means, each cluster is associated with a centroid.

The main objective of the K-Means algorithm is to minimize the sum of distances between the points and their respective cluster centroid.

Let's now take an example to understand how K-Means actually works:

Step 1: Choose the number of clusters k

The first step in k-means is to pick the number of clusters, k.

Step 2: Select k random points from the data as centroids

Next, we randomly select the centroid for each cluster. Let's say we want to have 2 clusters, so k is equal to 2 here. We then randomly select the centroid:

Here, the red and green circles represent the centroid for these clusters.

Step 3: Assign all the points to the closest cluster centroid

Once we have initialized the centroids, we assign each point to the closest cluster centroid:

Here you can see that the points which are closer to the red point are assigned to the red cluster whereas the points which are closer to the green point are assigned to the green cluster.

Step 4: Recompute the centroids of newly formed clusters

Now, once we have assigned all of the points to either cluster, the next step is to compute the centroids of newly formed clusters:

Here, the red and green crosses are the new centroids.

Step 5: Repeat steps 3 and 4

We then repeat steps 3 and 4:

The step of computing the centroid and assigning all the points to the cluster based on their distance from the centroid is a single iteration. But wait - when should we stop this process? It can't run till eternity, right?

Stopping Criteria for K-Means Clustering

There are essentially three stopping criteria that can be adopted to stop the K-means algorithm:

1. Centroids of newly formed clusters do not change

2. Points remain in the same cluster

3. Maximum number of iterations are reached

We can stop the algorithm if the centroids of newly formed clusters are not changing. Even after multiple iterations, if we are getting the same centroids for all the clusters, we can say that the algorithm is not learning any new pattern and it is a sign to stop the training.

Another clear sign that we should stop the training process if the points remain in the same cluster even after training the algorithm for multiple iterations.

Finally, we can stop the training if the maximum number of iterations is reached. Suppose if we have set the number of iterations as 100. The process will repeat for 100 iterations before stopping.

Implementing K-Means Clustering in Python from Scratch

Time to fire up our Jupyter notebooks (or whichever IDE you use) and get our hands dirty in Python!

We will be working on the loan prediction dataset that you can download here. I encourage you to read more about the dataset and the problem statement here. This will help you visualize what we are working on (and why we are doing this). Two pretty important questions in any data science project.

First, import all the required libraries:

View the code on Gist.

Now, we will read the CSV file and look at the first five rows of the data:

View the code on Gist.

For this article, we will be taking only two variables from the data - "LoanAmount" and "ApplicantIncome". This will make it easy to visualize the steps as well. Let's pick these two variables and visualize the data points:

View the code on Gist.

View the code on Gist.

Here, the red dots represent the 3 centroids for each cluster. Note that we have chosen these points randomly and hence every time you run this code, you might get different centroids.

Next, we will define some conditions to implement the K-Means Clustering algorithm. Let's first look at the code:

View the code on Gist.

These values might vary every time we run this. Here, we are stopping the training when the centroids are not changing after two iterations. We have initially defined the diff as 1 and inside the while loop, we are calculating this diff as the difference between the centroids in the previous iteration and the current iteration.

When this difference is 0, we are stopping the training. Let's now visualize the clusters we have got:

View the code on Gist.

Awesome! Here, we can clearly visualize three clusters. The red dots represent the centroid of each cluster. I hope you now have a clear understanding of how K-Means work.

Here is a LIVE CODING window for you to play around with the code and see the results for yourself - without leaving this article! Go ahead and start working on it:

However, there are certain situations where this algorithm might not perform as well. Let's look at some challenges which you can face while working with k-means.

Challenges with the K-Means Clustering Algorithm

One of the common challenges we face while working with K-Means is that the size of clusters is different. Let's say we have the below points:

Another challenge with k-means is when the densities of the original points are different. Let's say these are the original points:

One of the solutions is to use a higher number of clusters. So, in all the above scenarios, instead of using 3 clusters, we can have a bigger number. Perhaps setting k=10 might lead to more meaningful clusters.

Remember how we randomly initialize the centroids in k-means clustering? Well, this is also potentially problematic because we might get different clusters every time. So, to solve this problem of random initialization, there is an algorithm called K-Means++ that can be used to choose the initial values, or the initial cluster centroids, for K-Means.

K-Means++ to Choose Initial Cluster Centroids for K-Means Clustering

In some cases, if the initialization of clusters is not appropriate, K-Means can result in arbitrarily bad clusters. This is where K-Means++ helps. It specifies a procedure to initialize the cluster centers before moving forward with the standard k-means clustering algorithm.

Using the K-Means++ algorithm, we optimize the step where we randomly pick the cluster centroid. We are more likely to find a solution that is competitive to the optimal K-Means solution while using the K-Means++ initialization.

The steps to initialize the centroids using K-Means++ are:

1. The first cluster is chosen uniformly at random from the data points that we want to cluster. This is similar to what we do in K-Means, but instead of randomly picking all the centroids, we just pick one centroid here

2. Next, we compute the distance (D(x)) of each data point (x) from the cluster center that has already been chosen

3. Then, choose the new cluster center from the data points with the probability of x being proportional to (D(x))2

4. We then repeat steps 2 and 3 until k clusters have been chosen

Let's take an example to understand this more clearly. Let's say we have the following points and we want to make 3 clusters here:

Let's say we pick the green point as the initial centroid. Now, we will calculate the distance (D(x)) of each data point with this centroid:

I'm sure there's one question which you've been wondering about since the start of this article - how many clusters should we make? Aka, what should be the optimum number of clusters to have while performing K-Means?

How to Choose the Right Number of Clusters in K-Means Clustering?

One of the most common doubts everyone has while working with K-Means is selecting the right number of clusters.

So, let's look at a technique that will help us choose the right value of clusters for the K-Means algorithm. Let's take the customer segmentation example which we saw earlier. To recap, the bank wants to segment its customers based on their income and amount of debt:

The maximum possible number of clusters will be equal to the number of observations in the dataset.

But then how can we decide the optimum number of clusters? One thing we can do is plot a graph, also known as an elbow curve, where the x-axis will represent the number of clusters and the y-axis will be an evaluation metric. Let's say inertia for now.

You can choose any other evaluation metric like the Dunn index as well:

Next, we will start with a small cluster value, let's say 2. Train the model using 2 clusters, calculate the inertia for that model, and finally plot it in the above graph. Let's say we got an inertia value of around 1000:

So,

the cluster value where this decrease in inertia value becomes constant can be chosen as the right cluster value for our data.

Here, we can choose any number of clusters between 6 and 10. We can have 7, 8, or even 9 clusters. You must also look at the computation cost while deciding the number of clusters. If we increase the number of clusters, the computation cost will also increase. So, if you do not have high computational resources, my advice is to choose a lesser number of clusters.

Let's now implement the K-Means Clustering algorithm in Python. We will also see how to use K-Means++ to initialize the centroids and will also plot this elbow curve to decide what should be the right number of clusters for our dataset.

Implementing K-Means Clustering in Python

We will be working on a wholesale customer segmentation problem. You can download the dataset using this link. The data is hosted on the UCI Machine Learning repository.

The aim of this problem is to segment the clients of a wholesale distributor based on their annual spending on diverse product categories, like milk, grocery, region, etc. So, let's start coding!

We will first import the required libraries:

View the code on Gist.

Next, let's read the data and look at the first five rows:

View the code on Gist.

View the code on Gist.

Since K-Means is a distance-based algorithm, this difference of magnitude can create a problem. So let's first bring all the variables to the same magnitude:

View the code on Gist.

The magnitude looks similar now. Next, let's create a kmeans function and fit it on the data:

View the code on Gist.

We have initialized two clusters and pay attention - the initialization is not random here. We have used the k-means++ initialization which generally produces better results as we have discussed in the previous section as well.

Let's evaluate how well the formed clusters are. To do that, we will calculate the inertia of the clusters:

View the code on Gist.

Output: 2599.38555935614

We got an inertia value of almost 2600. Now, let's see how we can use the elbow curve to determine the optimum number of clusters in Python.

We will first fit multiple k-means models and in each successive model, we will increase the number of clusters. We will store the inertia value of each model and then plot it to visualize the result:

View the code on Gist.

Can you tell the optimum cluster value from this plot? Looking at the above elbow curve, we can choose any number of clusters between 5 to 8. Let's set the number of clusters as 6 and fit the model:

View the code on Gist.

Finally, let's look at the value count of points in each of the above-formed clusters:

View the code on Gist.

End Notes

In this article, we discussed one of the most famous clustering algorithms - K-Means. We implemented it from scratch and looked at its step-by-step implementation. We looked at the challenges which we might face while working with K-Means and also saw how K-Means++ can be helpful when initializing the cluster centroids.

Finally, we implemented k-means and looked at the elbow curve which helps to find the optimum number of clusters in the K-Means algorithm.

If you have any doubts or feedback, feel free to share them in the comments section below. And make sure you check out the comprehensive 'Applied Machine Learning' course that takes you from the basics of machine learning to advanced algorithms (including an entire module on deploying your machine learning models!).

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K Means Clustering | K Means Clustering Algorithm in Python

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K-Means Clustering is a simple yet powerful algorithm in data science There are a plethora of real-world applications of K-Means Clustering (a few of which we will cover here) This comprehensive guide will introduce you to the world of clustering and K-Means Clustering along with an implementation in Python on a real-world dataset I love working on recommendation engines.

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Linear Programming | Applications Of Linear Programming

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Linear Programming | Applications Of Linear Programming

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Introduction

Optimization is the way of life. We all have finite resources and time and we want to make the most of them. From using your time productively to solving supply chain problems for your company - everything uses optimization. It's a especially interesting and relevant topic in data science.

It is also a very interesting topic - it starts with simple problems, but can get very complex. For example, sharing a chocolate between siblings is a simple optimization problem. We don't think in mathematical term while solving it. On the other hand devising inventory and warehousing strategy for an e-tailer can be very complex. Millions of SKUs with different popularity in different regions to be delivered in defined time and resources - you see what I mean!

Linear programming (LP) is one of the simplest ways to perform optimization. It helps you solve some very complex optimization problems by making a few simplifying assumptions. As an analyst you are bound to come across applications and problems to be solved by Linear Programming.

For some reason, LP doesn't get as much attention as it deserves while learning data science. So, I thought let me do justice to this awesome technique. I decided to write an article which explains Linear programming in simple English. I have kept the content as simple as possible. The idea is to get you started and excited about Linear Programming.

Table of Content

1. What is Linear Programming?

Basic Terminologies

Process to define a LP problem

2. Solve Linear Program by Graphical Method

3. Solve Linear Program using OpenSolver

4. Simplex Method

5. Northwest Corner Method and Least Cost Method

6. Applications of Linear programming

1.What is Linear Programming?

Now, what is linear programming? Linear programming is a simple technique where we depict complex relationships through linear functions and then find the optimum points. The important word in previous sentence is depict. The real relationships might be much more complex - but we can simplify them to linear relationships.

Applications of linear programming are every where around you. You use linear programming at personal and professional fronts. You are using linear programming when you are driving from home to work and want to take the shortest route. Or when you have a project delivery you make strategies to make your team work efficiently for on time delivery.

Example of a linear programming problem

Let's say a FedEx delivery man has 6 packages to deliver in a day. The warehouse is located at point A. The 6 delivery destinations are given by U, V, W, X, Y and Z. The numbers on the lines indicate the distance between the cities. To save on fuel and time the delivery person wants to take the shortest route.

So, the delivery person will calculate different routes for going to all the 6 destinations and then come up with the shortest route. This technique of choosing the shortest route is called linear programming.

In this case, the objective of the delivery person is to deliver the parcel on time at all 6 destinations. The process of choosing the best route is called Operation Research. Operation research is an approach to decision-making, which involves a set of methods to operate a system. In the above example, my system was the Delivery model.

Linear programming is used for obtaining the most optimal solution for a problem with given constraints. In linear programming, we formulate our real life problem into a mathematical model. It involves an objective function, linear inequalities with subject to constraints.

Is the linear representation of the 6 points above representative of real world? Yes and No. It is oversimplification as the real route would not be a straight line. It would likely have multiple turns, U turns, signals and traffic jams. But with a simple assumption, we have reduced the complexity of the problem drastically and are creating a solution which should work in most scenarios.

Formulating a problem - Let's manufacture some chocolates

Example: Consider a chocolate manufacturing company which produces only two types of chocolate - A and B. Both the chocolates require Milk and Choco only. To manufacture each unit of A and B, following quantities are required:

Each unit of A requires 1 unit of Milk and 3 units of Choco

Each unit of B requires 1 unit of Milk and 2 units of Choco

The company kitchen has a total of 5 units of Milk and 12 units of Choco. On each sale, the company makes a profit of

Rs 6 per unit A sold

Rs 5 per unit B sold.

Now, the company wishes to maximize its profit. How many units of A and B should it produce respectively?

Solution: The first thing I'm gonna do is represent the problem in a tabular form for better understanding.

Let the total number of units produced of A be = X

Let the total number of units produced of B be = Y

Now, the total profit is represented by Z

The total profit the company makes is given by the total number of units of A and B produced multiplied by its per unit profit Rs 6 and Rs 5 respectively.
Profit: Max Z = 6X+5Y
which means we have to maximize Z.

The company will try to produce as many units of A and B to maximize the profit. But the resources Milk and Choco are available in limited amount.

As per the above table, each unit of A and B requires 1 unit of Milk. The total amount of Milk available is 5 units. To represent this mathematically,
X+Y ≤ 5
Also, each unit of A and B requires 3 units & 2 units of Choco respectively. The total amount of Choco available is 12 units. To represent this mathematically,
3X+2Y ≤ 12
Also, the values for units of A can only be integers.

So we have two more constraints, X ≥ 0 & Y ≥ 0

For the company to make maximum profit, the above inequalities have to be satisfied.
This is called formulating a real-world problem into a mathematical model.
Common terminologies used in Linear Programming

Let us define some terminologies used in Linear Programming using the above example.

Decision Variables: The decision variables are the variables which will decide my output. They represent my ultimate solution. To solve any problem, we first need to identify the decision variables. For the above example, the total number of units for A and B denoted by X & Y respectively are my decision variables.

Objective Function: It is defined as the objective of making decisions. In the above example, the company wishes to increase the total profit represented by Z. So, profit is my objective function.

Constraints: The constraints are the restrictions or limitations on the decision variables. They usually limit the value of the decision variables. In the above example, the limit on the availability of resources Milk and Choco are my constraints.

Non-negativity restriction: For all linear programs, the decision variables should always take non-negative values. Which means the values for decision variables should be greater than or equal to 0.

Process to formulate a Linear Programming problem

Let us look at the steps of defining a Linear Programming problem generically:

1. Identify the decision variables

2. Write the objective function

3. Mention the constraints

4. Explicitly state the non-negativity restriction

For a problem to be a linear programming problem, the decision variables, objective function and constraints all have to be linear functions.

If the all the three conditions are satisfied, it is called a Linear Programming Problem.

2. Solve Linear Programs by Graphical Method

A linear program can be solved by multiple methods. In this section, we are going to look at the Graphical method for solving a linear program. This method is used to solve a two variable linear program. If you have only two decision variables, you should use the graphical method to find the optimal solution.

A graphical method involves formulating a set of linear inequalities subject to the constraints. Then the inequalities are plotted on a X-Y plane. Once we have plotted all the inequalities on a graph the intersecting region gives us a feasible region. The feasible region explains what all values our model can take. And it also gives us the optimal solution.

Let's understand this with the help of an example.

Example: A farmer has recently acquired an 110 hectares piece of land. He has decided to grow Wheat and barley on that land. Due to the quality of the sun and the region's excellent climate, the entire production of Wheat and Barley can be sold. He wants to know how to plant each variety in the 110 hectares, given the costs, net profits and labor requirements according to the data shown below:

The farmer has a budget of US$10,000 and an availability of 1,200 man-days during the planning horizon. Find the optimal solution and the optimal value.

Solution: To solve this problem, first we gonna formulate our linear program.

Formulation of Linear Problem
Step 1: Identify the decision variables
The total area for growing Wheat = X (in hectares)

The total area for growing Barley = Y (in hectares)

X and Y are my decision variables.
Step 2: Write the objective function
Since the production from the entire land can be sold in the market. The farmer would want to maximize the profit for his total produce. We are given net profit for both Wheat and Barley. The farmer earns a net profit of US$50 for each hectare of Wheat and US$120 for each Barley.

Our objective function (given by Z) is, Max Z = 50X + 120Y
Step 3: Writing the constraints
1. It is given that the farmer has a total budget of US$10,000. The cost of producing Wheat and Barley per hectare is also given to us. We have an upper cap on the total cost spent by the farmer. So our equation becomes:
100X + 200Y ≤ 10,000
2. The next constraint is, the upper cap on the availability on the total number of man-days for planning horizon. The total number of man-days available are 1200. As per the table, we are given the man-days per hectare for Wheat and Barley.
10X + 30Y ≤ 1200
3. The third constraint is the total area present for plantation. The total available area is 110 hectares. So the equation becomes,
X + Y ≤ 110 Step 4: The non-negativity restriction
The values of X and Y will be greater than or equal to 0. This goes without saying.
X ≥ 0, Y ≥ 0
We have formulated our linear program. It's time to solve it.

Solving a LP through Graphical method

Since we know that X, Y ≥ 0. We will consider only the first quadrant.

To plot for the graph for the above equations, first I will simplify all the equations.

100X + 200Y ≤ 10,000 can be simplified to X + 2Y ≤ 100 by dividing by 100.

10X + 30Y ≤ 1200 can be simplified to X + 3Y ≤ 120 by dividing by 10.

The third equation is in its simplified form, X + Y ≤ 110.

Plot the first 2 lines on a graph in first quadrant (like shown below)

The optimal feasible solution is achieved at the point of intersection where the budget & man-days constraints are active. This means the point at which the equations X + 2Y ≤ 100 and X + 3Y ≤ 120 intersect gives us the optimal solution.

The values for X and Y which gives the optimal solution is at (60,20).

To maximize profit the farmer should produce Wheat and Barley in 60 hectares and 20 hectares of land respectively.

The maximum profit the company will gain is,

Max Z = 50 * (60) + 120 * (20)

= US$5400

3. Solve Linear Program using OpenSolver

In reality, a linear program can contain 30 to 1000 variables and solving it either Graphically or Algebraically is next to impossible. Companies generally use OpenSolver to tackle these real-world problems. Here I am gonna take you through steps to solve a linear program using OpenSolver.

OpenSolver is an open source linear and optimizer for Microsoft Excel. It is an advanced version of built-in excel Solver. You can download OpenSolver here and follow the installation manual.

I want you to get a hands-on knowledge on using OpenSolver. So, for clear understanding, I will explain it using an example.

Example: Below there is a diet chart which gives me calories, protien, carbohydrate and fat content for 4 food items. Sara wants a diet with minimum cost. The diet chart is as follows:

The chart gives the nutrient content as well as the per-unit cost of each food item. The diet has to be planned in such a way that it should contain at least 500 calories, 6 grams of protien, 10 grams of carbohydrates and 8 grams of fat.

Solution: First, I'm gonna formulate my linear program in a spreadsheet.

Step 1: Identify the decision variables. Here my decision variables are the food items. Add the headers. For trial purpose, we are entering arbitrary values. Let's say, Sara consumes 3 units of Food Item 1, 0 unit of Food Item 2, 1 unit of Food Item 3 and 0 unit of Food Item 4. These are called variable cells.

Step 2: Now we will write our objective function. For the diet to be optimal we must have minimum cost along with required calories, protein, carbohydrate and fat.

In cell B7:E7 we take the reference the number of units. And in cell B8:E8 we put the per-unit cost of each food items.

In cell B10, we want the total cost for the diet. The total cost is given by the sumproduct of number of units eaten and per unit cost. Sumproduct is given by = B7*B8+C7*C8+D7*D8+E7*E8. Let's see this in a spreadsheet.

Step 3: Now, we will enter the constraints. Column F contains the total of calories, protien, carbohydrate and fat. The total number of calorie intake in given by sumproduct the number of food items eaten and the calorie consumed per food item. For cell F13= Sumprodcut($B$7:$F$7, B13:F13). Similarly for others. Column G gives the inequality, since the problem demands Calories, Protien, Carbohydrate and Fat to be atleast 500, 6, 10 and 8 respectively. Column H gives the required nutrient content.

Step 4: Now, we will enter the Linear program into the solver. Now, once you have installed OpenSolver. When you click on the Data tab, on the right you will see Model. Click on model, then enter the values one by one. First, we will enter the objective function,$B10 i.e in objective cell. Select minimise because we want to minimize the diet cost.

Step 5: Now enter the decision variables in the variable cells.

Step 6: Now, we will add the constraints. The first constraint is F13 ≥ H13. Add all the constraints one by one.

Step 7: Now, you have to enter one important constraint. The non-negativity restriction. All the decision variables will be greater than 0.

Step 8: Now, click on save model to finish the modeling process. Once you save the model, it will look something like this.

Step 9: Once the model is saved click on Data tab then click solve. The optimal solution and values are displayed in the corresponding cells. The optimal minimum cost is US$0.90. Sara should consume 3 units of Food Item 2 and 1 unit of Food Item 3 for the required nutrient content at the minimum cost. This is solves our linear program.

4. Simplex Method

Simplex Method is one of the most powerful & popular methods for linear programming. Simplex method is an iterative procedure for getting the most feasible solution. In this method, we keep transforming the value of basic variables to get maximum value for the objective function.

A linear programming function is in its standard form if it seeks to maximize the objective function. subject to constraints,

. . . . . .

. . . . . .

where, and . After adding slack variables, the corresponding system of constraint equation is,

. . . .

where,

The variables, ................... are called slack variables. They are non-negative numbers which are added to remove the inequalities from an equation.

The above explanation gives the theoretical explanation of simplex method. Now, I am gonna explain how to use simplex method in real life using Excel.

Example: The advertising alternatives for a company include television, newspaper and radio advertisements. The cost for each medium with their audience coverage is given below.

The local newspaper limits the number of advertisements from a single company to ten. Moreover, in order to balance the advertising among the three types of media, no more than half of the total number of advertisements should occur on the radio. And at least 10% should occur on television. The weekly advertising budget is $18,200. How many advertisements should be run in each of the three types of media to maximize the total audience?

Solution: First I am going to formulate my problem for a clear understanding.

Step 1: Identify Decision Variables

Let , , represent the total number of ads for television, newspaper, and radio respectively.

Step 2: Objective Function

The objective of the company is to maximize the audience. The objective function is given by:

Step 3: Write down the constraints

Now, I will mention each constraint one by one.

It is clearly given that we have a budget constraint. The total budget which can be allocated is $18,200. And the individual costs per television, newspaper and radio advertisement is $2000, $600 and $300 respectively. This can be represented by the equation,

Since for a newspaper advertisement, there is an upper cap on the number of advertisements to 10. My first constraints is,

The next constraint is the number of advertisements on television. The company wants at least 10% of the total advertisements to be on television. So, it can be represented as:

The last constraint is the number of advertisements on the radio cannot be more than half of the total number of advertisements. It can be represented as

Now, I have formulated my linear programming problem. We are using simplex method to solve this. I will take you through simplex method one by one.

To reiterate all the constraints are as follows. I have simplified the last two equations to bring them in standard form.

We have a total of 4 equations. To balance out each equation, I am introducing 4 slack variables, , and .

So our equations are as follows:

I hope now you are available to make sense of the entire advertising problem. All the above equations, are only for your better understanding. Now if you solve these equations, you will get the values for X1= 4, X2= 10 and X3= 14.

On solving the objective function you will get the maximum weekly audience as 1,052,000. You can follow the tutorial here to solve the equation. To solve linear program in excel, follow this tutorial.

5. Northwest Corner Method and Least Cost Method

5.1 Northwest Corner Method

Northwest corner method is a special type method used for transportation problems in linear programming. It is used to calculate the feasible solution for transporting commodities from one place to another. Whenever you are given a real-world problem, which involves supply and demand from one source of different source. The data model includes the following:

The level of supply and demand at each source is given

The unit transportation of a commodity from each source to each destination

The model assumes that there is only one commodity. The demand for which can come from different sources. The objective is to fulfill the total demand with minimum transportation cost. The model is based on the hypothesis that the total demand is equal to the total supply, i.e the model is balanced. Let's understand this with the help of an example.

Example: Consider there are 3 silos which are required to satisfy the demand from 4 mills. (A silo is a storage area of farm used to store grain and Mill is a grinding factory for grains).

Solution: Let's understand what the above table explains.

The cost of transportation from Silo i to Mill j is given by the cost in each cell corresponding to the supply from each silo 1 and the demand at each Mill. For example: The cost of transporting from Silo 1 to Mill 1 is $10, from Silo 3 to Mill 5 is $18. It is also given the total demand & supply for mill and silos. The objective is to find the minimal transportation cost such that the demand for all the mills is satisfied.

As the name suggests Northwest corner method is a method of allocating the units starting from the topleft cell. The demand for Mill 1 is 5 and Silo 1 has a total supply of 15. So, 5 units can be allocated to Mill1 at a cost of $10 per unit.The demand for Mill1 is met. then we move to top left cell of Mill 2. The demand for Mill 2 is 15 units, which it can get 10 units from Silo 1 at a cost of $2 per unit and 5 units from Silo 2 at a cost of $7 per unit. Then we move onto Mill 3, the northwest cell is S2M3. The demand for Mill 3 is 15 units, which it can get from Silo 2 at a cost of $9 per unit. Moving on to the last Mill, Mill 4 has a demand of 15 units. It will get 5 units from a Silo 2 at a cost of $20 per unit and 10 units from Silo 3 at a cost of $18 per unit.

The total cost of transportation is = 5*10+(2*10+7*5)+9*15+(20*5+18*10) = $520

5.2 Least Cost Method

Least Cost method is another method to calculate the most feasible solution for a linear programming problem. This method derives more accurate result than Northwest corner method. It is used for transportation and manufacturing problems. To keep it simple I am explaining the above transportation problem.

According to the least cost method, you start from the cell containing the least unit cost for transportation. So, for the above problem, I supply 5 units from Silo 3 at per unit cost of $4. The demand for Mill1 is met. For Mill 2, we supply 15 units from Silo 1 at per unit cost of $2. Then For Mill 3 we supply 15 units from Silo 2 at per unit cost of $9. Then for Mill 4 we supply 10 units from Silo 2 at per unit cost of $20 and 5 units from Silo 3 a $18 per unit. The total transportation costs is $475.

Well the above method explains we can optimize our costs further with the best method. Let's check this using Excel Solver. Solver is an in-built add-on in Microsoft Excel. It's an add-in plug available in Excel. Go to file->options->add-ins->select solver->click on manage->select solver->click Ok. Your solver is now added in excel. You can check it under the Data tab.

The first thing I am gonna do is enter my data in excel. After entering the data in excel, I have calculated the total of C3:F3. Similarly for others. This is done to take the total demand from Silo 1 and others.

After this, I am gonna break my model into two. The first table gives me the units supplied and the second table gives me the unit cost.

Now, I am calculating my total cost which will be given by Sumproduct of unit cost and units supplied.

Now I am gonna use Solver to compute my model. Similar to the above method. Add the objective function, variable cells, constraints.

Now your model is ready to be solved. Click on solve and you will get your optimal cost. The minimum transportation cost is $435.

7. Applications of Linear Programming

Linear programming and Optimization are used in various industries. Manufacturing and service industry uses linear programming on a regular basis. In this section, we are going to look at the various applications of Linear programming.

1. Manufacturing industries use linear programming for analyzing their supply chain operations. Their motive is to maximize efficiency with minimum operation cost. As per the recommendations from the linear programming model, the manufacturer can reconfigure their storage layout, adjust their workforce and reduce the bottlenecks. Here is a small Warehouse case study of Cequent a US base company, watch this video for a more clear understanding.

2. Linear programming is also used in organized retail for shelf space optimization. Since the number of products in the market have increased in leaps and bounds, it is important to understand what does the customer want. Optimization is aggressively used in stores like Walmart, Hypercity, Reliance, Big Bazaar, etc. The products in the store are placed strategically keeping in mind the customer shopping pattern. The objective is to make it easy for a customer to locate & select the right products. This is with subject to constraints like limited shelf space, the variety of products, etc.

3. Optimization is also used for optimizing Delivery Routes. This is an extension of the popular traveling salesman problem. Service industry uses optimization for finding the best route for multiple salesmen traveling to multiple cities. With the help of clustering and greedy algorithm the delivery routes are decided by companies like FedEx, Amazon, etc. The objective is to minimize the operation cost and time.

4. Optimizations is also used in Machine Learning. Supervised Learning works on the fundamental of linear programming. A system is trained to fit on a mathematical model of a function from the labeled input data that can predict values from an unknown test data.

Well, the applications of Linear programming don't end here. There are many more applications of linear programming in real-world like applied by Shareholders, Sports, Stock Markets, etc. Go on and explore further.

End Notes

I hope you enjoyed reading this article. I have tried to explain all the basic concepts under linear programming. If you have any doubts or questions feel free to post them in the comments section.

I have explained each concept with real life example. I want you to try them at your end and get hands-on experience. Let me know what you think!

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Introductory guide on Linear Programming for (aspiring) data scientists

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Introduction Optimization is the way of life. We all have finite resources and time and we want to make the most of them. From using your time productively to solving supply chain problems for your company - everything uses optimization. It's a especially interesting and relevant topic in data science.