Trending February 2024 # A Beginner’s Guide To Boosting Your Holiday Sales Online # Suggested March 2024 # Top 3 Popular

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15. Create Urgency 14. Partner With a Non-Profit

Besides finding gifts for our friends and family, this is also the season of giving. Team up with a non-profit and donate a percentage of sales to that charity. It will make people feel better about purchasing something from your site since you’re giving back.

13. Bundle Products

Think of what Amazon does here. When you search for one product, Amazon will offer a couple of other suggestions that can be bundled together at a discount. You could also offer gift sets to stand out from other retailers. A nice move if you want to increase sales.

12. Email Previous Customers

11. Offer Holiday Bonuses/Coupons/Upsells

People enjoy being rewarded for being a customer. Offer a complimentary gift when you purchase a certain amount, coupons or something extra like free gift-wrapping to attract repeat customers.

10. Great Customer Service

People want to know that if there is a problem with their order or if they have a question regarding a product that they can reach a real, live person. Place your customer service number prominently throughout your site and have a live person on the other end of the phone. This is how Zappos became such a juggernaut.

9. Free Shipping

We all love freebies. And, does it get any better when you notice that a site offers free shipping? An essential move for people who are most-likely on a budget throughout the holidays.

8. Personal Suggestions/Experiences

Customers are constantly searching for gift recommendations or what the must-have item is this year. Offer consumers gift ideas through a list that was personally written by you or a ghost writer. Go the extra mile and share your own personal experiences with that product as well. Besides providing some personality, people may be more inclined to purchase a product if they know it’s been recommended by a real person.

7. Offer One Day Sale or Discounts

Pretty much every online retailer will be offering some sort of sale or discount during the holidays. So, this should be a no-brainer. If you don’t offer a one-day sale or end of year discounts, people will move on to a site that has the better deal.

6. Host Contests

Contests are a proven technique to gain attention. Whether it’s a wish list or caption contest, they are a simple and effective way to draw visitors to your site, which will hopefully result in more sales.

5. Use Social Media

There’s so much more you can do with social media than just informing customers that you have a website where they can buy stuff. Have a holiday flash sale via Facebook. Post hashtags on Twitter that link to discounts or coupons. Conduct a Pinterest holiday board contest. There a number of ways that you can use social media networks to bring people to your site for the final sale.

4. Make Sure You’re Smartphone Ready

The Google survey we mentioned earlier also discovered that three-quarters of smart phone owners will browse on their phones this season. Common sense, right here. You need to make sure that your site is compatible with smartphones. Whether that’s by having the correct size images or making sure that your checkout works properly, your site must be effective on these devices.

3. Offer Gift Cards/Certificates

Gift cards and gift certificates are big business, which is why your site must absolutely offer one. Be certain that your site has a section devoted to gift cards that is prominently displayed on the homepage. It also wouldn’t hurt for the card to be enclosed in a decorated box or envelope.

2. Feature Holiday Themes

Since everyone is in the holiday spirit, make sure that the graphics on your site are just as festive – just to remind people it is indeed the holidays! This could also be good chance to highlight some of your products, if you sell goods, right on the homepage. Also, make sure that all your social networks have holiday themed content. Tis’ the season.

1. Follow Social Media

Keep up with trends via social media. We’re not just talking about only Facebook and Twitter, but also Instagram, Tumblr, and Pinterest. By following trends on social networks you’ll be aware of what items shoppers are searching for this season so you know what to push.

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What Is Sales Automation: A Guide To Boosting Your Revenue

Learn how to use the top 12 sales automation tools to complete manual tasks, save your team time and boost sales.

If you’re not using sales automation software yet, you’re wasting valuable time and money.

Imagine a tireless fleet of employees taking care of all the mundane, repetitive tasks that keep your business running. Meanwhile, your other team members are focusing on important projects, like closing sales. Working together, these teams ensure your business operations are coordinated and effective.

Don’t have the budget to hire a whole new team of dedicated assistants who can work 24/7? That’s where sales automation comes in.

Bonus: Learn how to sell more products on social media with our free Social Commerce 101 guide. Delight your customers and improve conversion rates.

What is sales automation?

Sales automation is the use of sales automation tools to complete manual tasks that are predictable and routine.

Think sending invoices and follow-up emails, or answering customer questions. These administrative tasks can take up a huge amount of valuable employee time. And they often need to be done monthly, weekly, or even daily.

Outsourcing these tasks to sales automation software increases the productivity of your team. And it costs a lot less than hiring a new assistant who happens to love repetitive labor. You can automate up to one-third of all sales tasks!

What are the benefits of using sales automation software?

In short, sales automation software increases your productivity and revenues. Businesses that use sales automation have reported a 10 to 15% increase in efficiency, and up to 10% higher sales.

Despite these huge benefits, only one in four companies has automated sales tasks. That means three in four companies spend more time than they need to!

If you’re one of them, here’s how sales automation can support your success.

Streamline and boost your sales pipeline

Automation tools can tackle important (but time-consuming) elements of the sales pipeline. Collecting customer data and email addresses? No problem. Sending personalized emails? A breeze.

Automation software can even make product recommendations and guide customers through check-out.

Ensure no prospects fall through the cracks

First impressions count. Forgetting to follow up with new prospects can cost you their business. However, if you’re sending all those follow-up emails yourself, it’s bound to happen.

Increase customer satisfaction

A human touch matters to your customers. Some business owners worry that they’ll lose that essential element if they rely on automation. But the right automation strategy can have the opposite effect. With more time, your team can provide faster, better support to your customers when it counts.

Your whole organization has the same data

Sales automation tools integrate with your customer service software to keep all the important details in one place. Centralizing sales data ensures your team members can work in harmony. That way you can build on one another’s efforts instead of stepping on one another’s toes.

Benchmark your performance

Below are just a few of the most essential tasks that sales automation can tackle for sales reps. At the end of this post, we’ve rounded up a selection of tools that can do all of these and more.

Data collection Prospecting

Fortunately, you can automate a personalized email for each prospect with the data you’ve collected. You can also customize triggers, like reaching out to prospects who RSVP to an event. This ensures that each communication from your brand arrives right when your prospect is most interested and engaged.

Lead scoring Scheduling

Scheduling a simple call can often feel as complex as scheduling a rocket launch. You need to consider calendars, commitments, time zones, statutory holidays, moon phases… the list goes on. Automating the meeting scheduling process is the way to go. You can send your prospect a single link, and they choose a time that works for both of you. Or let your customers schedule their own in-store appointments, using a tool like Heyday.

Email templates and automation

Email marketing offers the best bang for your buck, generating $42 for every $1 spent. But 47% of sales teams are still sending emails manually. Typing out each email and contact details to schedule a sales call is a huge waste of time. Copy-and-pasting is faster but sloppy. The best solution is an email template, which can be populated by individual customer data for a personal touch.

Order management

If you use an eCommerce platform like Shopify, automating order management is easy. There are a ton of order management apps that integrate directly into the platform. These can generate invoices, shipping info, and delivery updates.

And when the order is done, you can also automate a customer satisfaction survey!

Customer service FAQs

Automating answers to frequently asked questions is a huge time-saver. It also benefits your customers! They can get support 24/7, and receive answers faster. One company was able to automate 88% of all customer questions using Heyday’s chatbot! That also meant faster support for the 12% of customers who needed a human to take over.

Get a free Heyday demo

Social media scheduling

More than half of Instagram users log on daily. So do 70% of Facebook users and almost half of Twitter users. Your brand needs to stay active on social media to keep up. Fortunately, you don’t need to log in to each platform every day to post your updates. You can use a social media management tool like Hootsuite.

With Hootsuite, you can schedule posts at the best time for each platform, without spending all day on TikTok for work. (Instead, you can spend all day on TikTok for fun.)

This is a good time to remind you that any automation requires human oversight. That’s a lesson Drag Race learned after Queen Elizabeth II passed shortly before this tweet was sent:

As always, automation works in harmony with your team. You want to be monitoring your channels and engaging with audiences. And remember to delete any awkward pre-scheduled posts.

Proposals and contracts

Automation can even help you close a deal. Rather than typing out each proposal, automation software can pull key details from your CRM and use them to populate a template. It also minimizes the risk of human error.

These tools can also monitor the documents. You’ll get a notification when your customer has viewed and signed. Save even more time by automating reminders.

Reports

Regular reports are important for tracking your performance, but producing them can be a drag. Instead, use software tools with integrated analytics to measure your business activities. These can include your social media reports, chatbot analytics, or sales data.

The 12 best sales automation software for 2023

There are a ton of tools out there that promise to transform your business. Here are our picks for the most indispensable options.

Heyday also provides powerful built-in analytics to sharpen your business strategy. Learn more about your customers with each interaction, and direct your efforts for the greatest impact.

Get a free Heyday demo

Social media has never been more important— or more time-consuming if you’re posting manually. Hootsuite can do the heavy lifting of scheduling and posting to each platform. Plus, it provides you with the most important social media analytics. It also centralizes all your social media profiles into one clear, organized dashboard.

And as social commerce grows even more important, you can use Hootsuite to sell products right on Instagram!

Try Hootsuite free for 30 days!

LeadGenius helps sales and marketing teams connect with valuable prospects. With LeadGenius, you can automate data acquisition tasks using their Flow browser extension. This allows you to quickly find new potential customers, and update your existing contacts.

And with DataGenius, you can search the web for accounts and contacts that align with your target audience. That means less time spent searching for new customers, and more high-quality prospects. You know the phrase “Work smarter, not harder?” This is exactly what it means.

Overloop (formerly chúng tôi is a sales automation tool for outbound campaigns. It allows your sales team to scale up their prospecting efforts across multiple channels, and analyze their results. From there, you can create custom flows to maximize your results.

Your team can also use Overloop to automate recruitment and business development activities. Plus, it integrates with other automation tools for a unified workflow.

Where can you find new prospects? Well, the world’s largest professional network is a start.

Why do some interactions lead to a deal, and others to a dead end? With Gong, you can stop wondering. It captures and analyzes your customer interactions, yielding data on the most effective tactics and strategies. In short, it turns the art of customer engagement into a science.

Gong can help every member of your sales team become a star performer, by creating data-driven workflows to follow. Identify weaknesses in your sales pipeline and address them with clear, actionable steps.

Founded in 2013, Calendly exploded during the pandemic. (The sudden proliferation of virtual meetings might have something to do with it.) In 2023 alone, the user base grew by an incredible 1,180%!

It integrates directly with your calendar, so you can determine your windows of availability. You can also collect contact data and send follow-ups automatically.

84% of customers value experience as much as product quality. To stay competitive, you need to provide a top-tier customer experience. That’s why you need a CRM.

A CRM helps all your departments work together, by centralizing customer data. That means everyone has the same information, and can see what actions have been taken. From the customer perspective, it’s smoother, more coordinated support at every step.

Another superpowered CRM option, perfect for teams of all sizes. Hubspot Sales Hub coordinates each stage of your sales pipeline, allowing you to track and measure your team’s activities.

You can nurture customers and prospects automatically, using customized workflows. Spend less time enrolling prospects and sending emails, and increase your revenues and response rates at the same time.

For smaller businesses, Sales Hub has free and affordable monthly plans. You can scale up as you grow while spending your finite resources wisely.

ClientPoint lets you streamline the process of creating and sharing documents. These include contracts, proposals, and information packages.

With ClientPoint, you can also get analytics on each document and set up automated alerts and reminders to close the deal.

Odds are, your sales team does a lot of email outreach. Yesware helps you streamline and focus your efforts, by tracking the results of your communications. It integrates directly with your email client, so it doesn’t feel like an extra step in your process. In fact, you don’t need to change anything at all: Yesware gathers information for you, then makes it easy to share insights with your team.

Yesware also lets you save your best emails as templates, so you can duplicate your success. It also includes features like scheduling and email sending.

Zapier is an app for apps. It allows you to link one app to another, creating a continuous automated workflow. For example, you can automate personalized emails to new customers by creating a “Zap” between Shopify and Gmail. Or send weekly social media reports to your team by using Zapier to connect Hootsuite and Slack. With over 5,000 apps, the possibilities are almost endless.

Ready to add sales automation to your operations? Start with a Heyday demo to learn how conversational AI can boost your sales and customer satisfaction!

Get a free a Heyday Demo

Turn customer service conversations into sales with Heyday. Improve response times and sell more products. See it in action.

Using Slicers In Excel Pivot Table – A Beginner’s Guide

A Pivot Table Slicer enables you to filter the data when you select one or more than one options in the Slicer box (as shown below).

Let’s get started.

Suppose you have a dataset as shown below:

This is a dummy data set (US retail sales) and spans across 1000 rows. Using this data, we have created a Pivot Table that shows the total sales for the four regions.

Read More: How to Create a Pivot Table from Scratch.

Once you have the Pivot Table in place, you can insert Slicers.

One may ask – Why do I need Slicers? 

You may need slicers when you don’t want the entire Pivot Table, but only a part of it. For example, if you don’t want to see the sales for all the regions, but only for South, or South and West, then you can insert the slicer and quickly select the desired region(s) for which you want to get the sales data.

Slicers are a more visual way that allows you to filter the Pivot Table data based on the selection.

Here are the steps to insert a Slicer for this Pivot Table:

Select any cell in the Pivot Table.

In the Insert Slicers dialog box, select the dimension for which you the ability to filter the data. The Slicer Box would list all the available dimensions and you can select one or more than one dimensions at once. For example, if I only select Region, it will insert the Region Slicer box only, and if I select Region and Retailer Type both, then it’ll insert two Slicers.

Note that Slicer would automatically identify all the unique items of the selected dimension and list it in the slicer box.

You can also insert multiple slicers by selecting more than one dimension in the Insert Slicers dialog box.

To insert multiple slicers:

Select any cell in the Pivot Table.

In the Insert Slicers dialog box, select all the dimensions for which you want to get the Slicers.

This will insert all the selected Slicers in the worksheet.

Note that these slicers are linked to each other. For example, If I select ‘Mid West’ in the Region filter and ‘Multiline’ in the Retailer Type filter, then it will show the sales for all the Multiline retailers in Mid West region only.

Also, if I select Mid West, note that the Specialty option in the second filter gets a lighter shade of blue (as shown below). This indicates that there is no data for Specialty retailer in the Mid West region.

What’s the difference between Slicers and Report Filters?

Here are some key differences between Slicers and Report Filters:

Slicers don’t occupy a fixed cell in the worksheet. You can move these like any other object or shape. Report Filters are tied to a cell.

Report filters are linked to a specific Pivot Table. Slicers, on the other hand, can be linked to multiple Pivot Tables (as we will see later in this tutorial).

Since a report filter occupies a fixed cell, it’s easier to automate it via VBA. On the other hand, a slicer is an object and would need a more complex code.

A Slicer comes with a lot of flexibility when it comes to formatting.

Here are the things that you can customize in a slicer.

If you don’t like the default colors of a slicer, you can easily modify it.

Select the slicer.

If you don’t like the default styles, you can create you own. To do this, select the New Slicer Style option and specify your own formatting.

By default, a Slicer has one column and all the items of the selected dimension are listed in it. In case you have many items, Slicer shows a scroll bar that you can use to go through all the items.

You may want to have all the items visible without the hassle of scrolling. You can do that by creating multiple column Slicer.

To do this:

Select the Slicer.

Change the Columns value to 2.

This will instantly split the items in the Slicer into two column. However, you may get something looking as awful as shown below:

This looks cluttered and the full names are not displayed. To make it look better, you change the size of the slicer and even the buttons within it.

To do this:

Select the Slicer.

Change Height and Width of the Buttons and the Slicer. (Note that you can also change the size of the slicer by simply selecting it and using the mouse to adjust the edges. However, to change the button size, you need to make the changes in the Options only).

By default, a Slicer picks the field name from the data. For example, if I create a slicer for Regions, the header would automatically be ‘Region’.

You may want to change the header or completely remove it.

Here are the steps:

In the Slicer Settings dialog box, change the header caption to what you want.

This would change the header in the slicer.

If you don’t want to see the header, uncheck the Display Header option in the dialog box.

By default, the items in a Slicer are sorted in an ascending order in case of text and Older to Newer in the case of numbers/dates.

You can change the default setting and even use your own custom sort criteria.

Here is how to do this:

In the Slicer Settings dialog box, you can change the sorting criteria, or use your own custom sorting criteria.

Read More: How to create custom lists in Excel (to create your own sorting criteria)

It may happen that some of the items in the Pivot Table have no data in it. In such cases, you can make the Slicers hide that item.

In such cases, you can choose not display it at all.

Here are the steps to do this:

In the Slicer Settings dialog box, with the ‘Item Sorting and Filtering’ options, check the option ‘Hide items with no data’.

A slicer can be connected to multiple Pivot Tables. Once connected, you can use a single Slicer to filter all the connected Pivot Tables simultaneously.

Remember, to connect different Pivot Tables to a Slicer, the Pivot Tables need to share the same Pivot Cache. This means that these are either created using the same data, or one of the Pivot Table has been copied and pasted as a separate Pivot Table.

Read More: What is Pivot Table Cache and how to use it?

Below is an example of two different Pivot tables. Note that the Slicer in this case only works for the Pivot Table on the left (and has no effect on the one on the right).

To connect this Slicer to both the Pivot  Tables:

In the Report Connections dialog box, you will see all the Pivot Table names that share the same Pivot Cache. Select the ones you want to connect to the Slicer. In this case, I only have two Pivot Tables and I’ve connected both with the Slicer.

Now your Slicer is connected to both the Pivot Tables. When you make a selection in the Slicer, the filtering would happen in both the Pivot Tables (as shown below).

Just as you use a Slicer with a Pivot Table, you can also use it with Pivot Charts.

Something as shown below:

Here is how you can create this dynamic chart:

Make the fields selections (or drag and drop fields into the area section) to get the Pivot chart you want. In this example, we have the chart that shows sales by region for four quarters. (Read here on how to group dates as quarters).

Select the Slicer dimension you want with the Chart. In this case, I want the retailer types so I check that dimension.

Format the Chart and the Slicer and you’re done.

Note that you can connect multiple Slicers to the same Pivot Chart and you can also connect multiple charts to the same Slicer (the same way we connected multiple Pivot Tables to the same Slicer).

You May Also Like the Following Pivot Table Tutorials:

Machine Learning Using C++: A Beginner’s Guide To Linear And Logistic Regression

Why C++ for Machine Learning?

The applications of machine learning transcend boundaries and industries so why should we let tools and languages hold us back? Yes, Python is the language of choice in the industry right now but a lot of us come from a background where Python isn’t taught!

The computer science faculty in universities are still teaching programming in C++ – so that’s what most of us end up learning first. I understand why you should learn Python – it’s the primary language in the industry and it has all the libraries you need to get started with machine learning.

But what if your university doesn’t teach it? Well – that’s what inspired me to dig deeper and use C++ for building machine learning algorithms. So if you’re a college student, a fresher in the industry, or someone who’s just curious about picking up a different language for machine learning – this tutorial is for you!

In this first article of my series on machine learning using C++, we will start with the basics. We’ll understand how to implement linear regression and logistic regression using C++!

Let’s begin!

Note: If you’re a beginner in machine learning, I recommend taking the comprehensive Applied Machine Learning course.

Linear Regression using C++

Let’s first get a brief idea about what linear regression is and how it works before we implement it using C++.

Linear regression models are used to predict the value of one factor based on the value of another factor. The value being predicted is called the dependent variable and the value that is used to predict the dependent variable is called an independent variable. The mathematical equation of linear regression is:

                                                 Y=B0+B1 X

Here,

X: Independent variable

Y: Dependent variable

B0: Represents the value of Y when X=0

B1: Regression Coefficient (this represents the change in the dependent variable based on the unit change in the independent variable)

For example, we can use linear regression to understand whether cigarette consumption can be predicted based on smoking duration. Here, your dependent variable would be “cigarette consumption”, measured in terms of the number of cigarettes consumed daily, and your independent variable would be “smoking duration”, measured in days.

Loss Function

The loss is the error in our predicted value of B0 and B1. Our goal is to minimize this error to obtain the most accurate value of B0 and B1 so that we can get the best fit line for future predictions.

For simplicity, we will use the below loss function:

e(i) = p(i) - y(i)

Here,

e(i) : error of ith training example

p(i) : predicted value of ith training example

y(i): actual value of ith training example

Overview of the Gradient Descent Algorithm

Gradient descent is an iterative optimization algorithm to find the minimum of a function. In our case here, that function is our Loss Function.

Here, our goal is to find the minimum value of the loss function (that is quite close to zero in our case). Gradient descent is an effective algorithm to achieve this. We start with random initial values of our coefficients B0 and B1 and based on the error on each instance, we’ll update their values.

Here’s how it works:

Initially, let B1 = 0 and B0 = 0. Let L be our learning rate. This controls how much the value of B1 changes with each step. L could be a small value like 0.01 for good accuracy

We calculate the error for the first point: e(1) = p(1) – y(1)

We’ll update B0 and B1 according to the following equation:

   b0(t+1) = b0(t) - L * error    b1(t+1) = b1(t) - L * error

We’ll do this for each instance of a training set. This completes one epoch. We’ll repeat this for more epochs to get more accurate predictions.

You can refer to these comprehensive guides to get a more in-depth intuition of linear regression and gradient descent:

Implementing Linear Regression in C++ Initialization phase:

We’ll start by defining our dataset. For the scope of this tutorial, we’ll use this dataset:

We’ll train our dataset on the first 5 values and test on the last value:

View the code on Gist.

Next, we’ll define our variables:

View the code on Gist.

Training Phase

Our next step is the gradient descent algorithm:

View the code on Gist.

Since there are 5 values and we are running the whole algorithm for 4 epochs, hence 20 times our iterative function works. The variable p calculates the predicted value of each instance. The variable err is used for calculating the error of each instance. We then update the values of b0 and b1 as explained above in the gradient descent section above. We finally push the error in the error vector.

As you will notice, B0 does not have any input. This coefficient is often called the bias or the intercept and we can assume it always has an input value of 1.0. This assumption can help when implementing the algorithm using vectors or arrays.

Finally, we’ll sort the error vector to get the minimum value of error and corresponding values of b0 and b1. At last, we’ll print the values:

View the code on Gist.

Testing Phase:

View the code on Gist.

We’ll enter the test value which is 6. The answer we get is 4.9753 which is quite close to 5. Congratulations! We just completed building a linear regression model with C++, and that too with good parameters.

Full Code for final implementation:

View the code on Gist.

Logistic Regression with C++

Logistic Regression is one of the most famous machine learning algorithms for binary classification. This is because it is a simple algorithm that performs very well on a wide range of problems.

The name of this algorithm is logistic regression because of the logistic function that we use in this algorithm. This logistic function is defined as:

predicted = 1 / (1 + e^-x)

Gradient Descent for Logistic Regression

We can apply stochastic gradient descent to the problem of finding the coefficients for the logistic regression model as follows:

Let us suppose for the example dataset, the logistic regression has three coefficients just like linear regression:

output = b0 + b1*x1 + b2*x2

The job of the learning algorithm will be to discover the best values for the coefficients (b0, b1, and b2) based on the training data.

Given each training instance:

Calculate a prediction using the current values of the coefficients.                            prediction = 1 / (1 + e^(-(b0 + b1*x1 + b2*x2)).

Calculate new coefficient values based on the error in the prediction. The values are updated according to the below equation:               b = b + alpha * (y – prediction) * prediction * (1 – prediction) * x

Where b is the coefficient we are updating and prediction is the output of making a prediction using the model. Alpha is a parameter that you must specify at the beginning of the training run. This is the learning rate and controls how much the coefficients (and therefore the model) changes or learns each time it is updated.

Like we saw earlier when talking about linear regression, B0 does not have any input. This coefficient is called the bias or the intercept and we can assume it always has an input value of 1.0. So while updating, we’ll multiply with 1.0.

The process is repeated until the model is accurate enough (e.g. error drops to some desirable level) or for a fixed number of iterations.

For a beginner’s guide to logistic regression, check this out – Simple Guide to Logistic Regression.

Implementing Logistic Regression in C++ Initialization phase

We’ll start by defining the dataset:

We’ll train on the first 10 values and test on the last value:

View the code on Gist.

Next, we’ll initialize the variables:

View the code on Gist.

Training Phase

View the code on Gist.

Since there are 10 values, we’ll run one epoch that takes 10 steps. We’ll calculate the predicted value according to the equation as described above in the gradient descent section:

prediction = 1 / (1 + e^(-(b0 + b1*x1 + b2*x2)))

Next, we’ll update the variables according to the similar equation described above:

b = b + alpha * (y – prediction) * prediction * (1 – prediction) * x

Finally, we’ll sort the error vector to get the minimum value of error and corresponding values of b0, b1, and b2. And finally, we’ll print the values:

View the code on Gist.

Testing phase:

View the code on Gist.

When we enter x1=7.673756466 and x2= 3.508563011, we get pred = 0.59985. So finally we’ll print the class:

View the code on Gist.

So the class printed by the model is 1. Yes! We got the prediction right!

Final Code for full implementation

View the code on Gist.

One of the more important steps, in order to learn machine learning, is to implement algorithms from scratch. The simple truth is that if we are not familiar with the basics of the algorithm, we can’t implement that in C++.

Related

Beginner’s Guide To Object Detection For Computer Vision Project

Object detection can be defined as a computer vision technique which aims to identify and locate objects on an image or a video. Computers might be able to process information way faster than humans, however, it is still difficult for computers to detect various objects on an image or video. The reason for this is that the computer interprets the majority of the outputs in the binary language only. This article aims to briefly discuss:

The basics of object detection

The object detection models

The benefits of object detection

The challenges and solutions

Before we get to the points above, we need to understand the difference between image classification and object detection. Beginners tend to confuse these two.

Difference between Object Detection and Image Classification

Let us break down these techniques, to know the difference between them. When you look at a picture of a dog you can instantly say it’s an image of an animal i.e. tell what the image is about. This is what image classification is all about. As long as there is only one object,

Object Detection Models

Now that we are clear with the definition of Object Detection, let’s have a look at some popular Object Detection models.

 R-CNN, Faster R-CNN, Mask R-CNN

The most popular object detection models belong to the family of regional based CNN models. This model has revolutionized the way the world of Object Detection used to work. In the past few years, they’ve not only become more accurate but more efficient too.

SSD and YOLO

There are a plethora of models belonging to the single shot detector family which were published in 2024. Although SSDs are faster than CNN models, their accuracy rate is much lower than that of the CNNs. YOLO or you only look once, is quite different from region-based algorithms. Just like SDDs, yolo is faster than R-CNNs but lags behind because of low accuracy. For mobile or embedded devices, SDDs are the perfect choice.

CenterNet

In recent years, these object detection models are gaining more popularity. CentreNet follows a key point-based approach for object detection. When compared with SSD or R-CNN approaches, this model proves to be more efficient and as well as more accurate. The only drawback of this method is slow training process.

Benefits of Object detection to Real-world

Object detection is completely inter-linked with other similar computer vision techniques such as image segmentation and image recognition that assist us to understand and analyze the scenes in videos and images. Nowadays, several real-world use cases are implemented in the market of object detection which make a tremendous impact on different industries. Here we’ll specifically examine how object detection applications have impacted in the following areas.

Self-driving cars

The primary reason behind the success of autonomous vehicles is real-time object detection artificial intelligence based models. These systems allow us to locate, identify and track the objects around them, for the purpose of safety and efficiency.

Video Surveillance

Real-time object detection and tracking the movements of objects allow video surveillance cameras to track the record of scenes of a particular location such as an airport. This state-of-the-art technique accurately recognizes and locates several instances of a given object in the video. In real-time, as the object moves through a given scene or across the particular frame, the system stores the information with real-time tracking feeds.

Crowd Counting

For heavily populated areas such as shopping malls, airports, city squares and theme parks, this application performs unbelievably well. Generally, this object detection application proves to be helpful to large enterprises and municipalities for tracking road traffic, violation of laws and number of vehicles passing in a particular time frame.

Anomaly detection

There are several anomaly detection applications available for different industries which use object detection. For instance, in agriculture, object detection models can accurately recognize and find the potential instances of plant disease. With the help of this, farmers will get notified and they will be able to prevent their crops from such threats. As another example, this model has been used to identify the skin infections and symptomatic lesions. Some applications are already built for skin care and acne treatment using object detection models. Keep in mind, there are some problems encountered while creating any kind of object detection model. However, solutions are also available to limit the challenges.

Challenges and Solutions of Object detection Modelling Dual Synchronization

The first challenge for object detection is to classify the image and position of the object, which is known as

Solution: Regional based Convolutional neural networks displays one class of object detection framework that consist of region generation proposals where objects are likely to be located, followed by CNN models processing to classify and rectify the object locations. Fast-R CNN model can improve the initial results with R-CNN. As its name denotes, this Fast R-CNN model provides tremendous speed, but accuracy also improves only because the localization and object classification tasks are optimized using a multi-task loss function.

Real-time detection speed 

Fast speed of object detection algorithms has always been a major problem to classify and localize the crucial objects accurately at same time to meet the real-time video processing. Over the years, several algorithms improved the test time from 0.02 frames per second to 155 fps.

Solution: Faster R-CNN and Fast R-CNN models aim to speed up the original speed of R-CNN approach. Because R-CNN uses the selective search to produce 2000 candidate regions of interest and passes through each CNN based model individually, that may cause a heavy bottleneck since the model processing gets down. Whereas, Fast R-CNN model transmits the whole image through CNN base once and then matches the ROIs created with selective search to feature map, considering 20-fold reduction in processing time.

Multiple aspects ratios and spatial scales

For several object detection applications, items of interest may appear in   huge range of aspect ratios and sizes. Researchers proved numerous methods to ensure the detection algorithms which are able to recognize different objects at different views and scales.

Solution: Rather than selective search, faster R-CNN has been updated with a region proposal network that uses a small sliding window over the picture’s convolutional feature map to produce candidate regions of interest. Several regions of Interests can be predicted at different positions and described relative to reference anchor boxes. The size and shape of these anchor boxes are selected to span a range of aspect ratios and different scales. It lets several types of objects identify with a hope that bounding box coordinates do not need to be adjusted during the localization task.

Limited data

One of the undeniable facts to be considered is the limited amount of annotated data which becomes a hurdle to build an application. These datasets are specifically containing ground truth examples for dozens to hundreds of objects, while image classification datasets include approximately 100,000 different classes.

Final thought

Beginner’s Guide To Web Scraping In Python Using Beautifulsoup

Overview

Learn web scraping in Python using the BeautifulSoup library

Web Scraping is a useful technique to convert unstructured data on the web to structured data

BeautifulSoup is an efficient library available in Python to perform web scraping other than urllib

A basic knowledge of HTML and HTML tags is necessary to do web scraping in Python

Introduction

The need and importance of extracting data from the web is becoming increasingly loud and clear. Every few weeks, I find myself in a situation where we need to extract data from the web to build a machine learning model.

For example, last week we were thinking of creating an index of hotness and sentiment about various data science courses available on the internet. This would not only require finding new courses, but also scraping the web for their reviews and then summarizing them in a few metrics!

This is one of the problems / products whose efficacy depends more on web scraping and information extraction (data collection) than the techniques used to summarize the data.

Note: We have also created a free course for this article – Introduction to Web Scraping using Python. This structured format will help you learn better.

Ways to extract information from web

There are several ways to extract information from the web. Use of APIs being probably the best way to extract data from a website. Almost all large websites like Twitter, Facebook, Google, Twitter, StackOverflow provide APIs to access their data in a more structured manner. If you can get what you need through an API, it is almost always preferred approach over web scraping. This is because if you are getting access to structured data from the provider, why would you want to create an engine to extract the same information.

Sadly, not all websites provide an API. Some do it because they do not want the readers to extract huge information in a structured way, while others don’t provide APIs due to lack of technical knowledge. What do you do in these cases? Well, we need to scrape the website to fetch the information.

There might be a few other ways like RSS feeds, but they are limited in their use and hence I am not including them in the discussion here.

What is Web Scraping?

You can perform web scraping in various ways, including use of Google Docs to almost every programming language. I would resort to Python because of its ease and rich ecosystem. It has a library known as ‘BeautifulSoup’ which assists this task. In this article, I’ll show you the easiest way to learn web scraping using python programming.

For those of you, who need a non-programming way to extract information out of web pages, you can also look at import.io . It provides a GUI driven interface to perform all basic web scraping operations. The hackers can continue to read this article!

Libraries required for web scraping

As we know, Python is an open source programming language. You may find many libraries to perform one function. Hence, it is necessary to find the best to use library. I prefer BeautifulSoup (Python library), since it is easy and intuitive to work on. Precisely, I’ll use two Python modules for scraping data:

Urllib2: It is a Python module which can be used for fetching URLs. It defines functions and classes to help with URL actions (basic and digest authentication, redirections, cookies, etc). For more detail refer to the documentation page. Note: urllib2 is the name of the library included in Python 2. You can use the urllib.request library included with Python 3, instead. The urllib.request library works the same way urllib.request works in Python 2. Because it is already included you don’t need to install it.

BeautifulSoup: It is an incredible tool for pulling out information from a webpage. You can use it to extract tables, lists, paragraph and you can also put filters to extract information from web pages. In this article, we will use latest version BeautifulSoup 4. You can look at the installation instruction in its documentation page.

BeautifulSoup does not fetch the web page for us. That’s why, I use urllib2 in combination with the BeautifulSoup library.

Python has several other options for HTML scraping in addition to BeatifulSoup. Here are some others:

Basics – Get familiar with HTML (Tags)

While performing web scarping, we deal with html tags. Thus, we must have good understanding of them. If you already know basics of HTML, you can skip this section. Below is the basic syntax of HTML:This syntax has various tags as elaborated below:

Other useful HTML tags are:

If you are new to this HTML tags, I would also recommend you to refer HTML tutorial from W3schools. This will give you a clear understanding about HTML tags.

Scraping a web page using BeautifulSoup

Here, I am scraping data from a Wikipedia page. Our final goal is to extract list of state, union territory capitals in India. And some basic detail like establishment, former capital and others form this wikipedia page. Let’s learn with doing this project step wise step:

#import the library used to query a website import urllib2 #if you are using python3+ version, import urllib.request #specify the url #Query the website and return the html to the variable 'page' page = urllib2.urlopen(wiki) #For python 3 use urllib.request.urlopen(wiki) #import the Beautiful soup functions to parse the data returned from the website from bs4 import BeautifulSoup #Parse the html in the 'page' variable, and store it in Beautiful Soup format soup = BeautifulSoup(page) Above, you can see that structure of the HTML tags. This will help you to know about different available tags and how can you play with these to extract information.

Work with HTML tags

In[30]:soup.title

In [38]:

soup

.

title

.

string

Out[38]:u'List of state and union territory capitals in India - Wikipedia, the free encyclopedia'

In [40]:

soup

.

a

 

Above, it is showing all links including titles, links and other information.  Now to show only links, we need to iterate over each a tag and then return the link using attribute “href” with get.



Find the right table: As we are seeking a table to extract information about state capitals, we should identify the right table first. Let’s write the command to extract information within all table tags. all_tables=soup.find_all('table') right_table=soup.find('table', class_='wikitable sortable plainrowheaders') right_table Above, we are able to identify right table.

#Generate lists A=[] B=[] C=[] D=[] E=[] F=[] G=[] for row in right_table.findAll("tr"): cells = row.findAll('td') states=row.findAll('th') #To store second column data if len(cells)==6: #Only extract table body not heading A.append(cells[0].find(text=True)) B.append(states[0].find(text=True)) C.append(cells[1].find(text=True)) D.append(cells[2].find(text=True)) E.append(cells[3].find(text=True)) F.append(cells[4].find(text=True)) G.append(cells[5].find(text=True)) #import pandas to convert list to data frame import pandas as pd df=pd.DataFrame(A,columns=['Number']) df['State/UT']=B df['Admin_Capital']=C df['Legislative_Capital']=D df['Judiciary_Capital']=E df['Year_Capital']=F df['Former_Capital']=G df

Similarly, you can perform various other types of web scraping using “BeautifulSoup“. This will reduce your manual efforts to collect data from web pages. You can also look at the other attributes like .parent, .contents, .descendants and .next_sibling, .prev_sibling and various attributes to navigate using tag name. These will help you to scrap the web pages effectively.-

But, why can’t I just use Regular Expressions?

Now, if you know regular expressions, you might be thinking that you can write code using regular expression which can do the same thing for you. I definitely had this question. In my experience with BeautifulSoup and Regular expressions to do same thing I found out:

Code written in BeautifulSoup is usually more robust than the one written using regular expressions. Codes written with regular expressions need to be altered with any changes in pages. Even BeautifulSoup needs that in some cases, it is just that BeautifulSoup is relatively better.

Regular expressions are much faster than BeautifulSoup, usually by a factor of 100 in giving the same outcome.

So, it boils down to speed vs. robustness of the code and there is no universal winner here. If the information you are looking for can be extracted with simple regex statements, you should go ahead and use them. For almost any complex work, I usually recommend BeautifulSoup more than regex.

End Note

In this article, we looked at web scraping methods using “BeautifulSoup” and “urllib2” in Python. We also looked at the basics of HTML and perform the web scraping step by step while solving a challenge. I’d recommend you to practice this and use it for collecting data from web pages.

Note: We have also created a free course for this article – Introduction to Web Scraping using Python. This structured format will help you learn better.

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