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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)

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++.

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## 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:

## 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.

Related

## Ai With Python – Supervised Learning: Regression

AI with Python – Supervised Learning: Regression

Regression is one of the most important statistical and machine learning tools. We would not be wrong to say that the journey of machine learning starts from regression. It may be defined as the parametric technique that allows us to make decisions based upon data or in other words allows us to make predictions based upon data by learning the relationship between input and output variables. Here, the output variables dependent on the input variables, are continuous-valued real numbers. In regression, the relationship between input and output variables matters and it helps us in understanding how the value of the output variable changes with the change of input variable. Regression is frequently used for prediction of prices, economics, variations, and so on.

Building Regressors in Python

In this section, we will learn how to build single as well as multivariable regressor.

Linear Regressor/Single Variable Regressor

Let us important a few required packages −

import numpy as np from sklearn import linear_model import sklearn.metrics as sm import matplotlib.pyplot as plt

Now, we need to provide the input data and we have saved our data in the file named linear.txt.

input = 'D:/ProgramData/linear.txt'

input_data = np.loadtxt(input, delimiter=',') X, y = input_data[:, :-1], input_data[:, -1]

The next step would be to train the model. Let us give training and testing samples.

training_samples = int(0.6 * len(X)) testing_samples = len(X) - num_training X_train, y_train = X[:training_samples], y[:training_samples] X_test, y_test = X[training_samples:], y[training_samples:]

Now, we need to create a linear regressor object.

reg_linear = linear_model.LinearRegression()

Train the object with the training samples.

reg_linear.fit(X_train, y_train)

We need to do the prediction with the testing data.

y_test_pred = reg_linear.predict(X_test)

Now plot and visualize the data.

plt.scatter(X_test, y_test, color = 'red') plt.plot(X_test, y_test_pred, color = 'black', linewidth = 2) plt.xticks(()) plt.yticks(()) plt.show() Output

Now, we can compute the performance of our linear regression as follows −

print("Performance of Linear regressor:") print("Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred), 2)) print("Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred), 2)) print("Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2)) print("Explain variance score =", round(sm.explained_variance_score(y_test, y_test_pred), 2)) print("R2 score =", round(sm.r2_score(y_test, y_test_pred), 2)) Output

Performance of Linear Regressor −

Mean absolute error = 1.78 Mean squared error = 3.89 Median absolute error = 2.01 Explain variance score = -0.09 R2 score = -0.09

In the above code, we have used this small data. If you want some big dataset then you can use sklearn.dataset to import bigger dataset.

2,4.82.9,4.72.5,53.2,5.56,57.6,43.2,0.92.9,1.92.4, 3.50.5,3.41,40.9,5.91.2,2.583.2,5.65.1,1.54.5, 1.22.3,6.32.1,2.8 Multivariable Regressor

First, let us import a few required packages −

import numpy as np from sklearn import linear_model import sklearn.metrics as sm import matplotlib.pyplot as plt from sklearn.preprocessing import PolynomialFeatures

Now, we need to provide the input data and we have saved our data in the file named linear.txt.

input = 'D:/ProgramData/Mul_linear.txt'

input_data = np.loadtxt(input, delimiter=',') X, y = input_data[:, :-1], input_data[:, -1]

The next step would be to train the model; we will give training and testing samples.

training_samples = int(0.6 * len(X)) testing_samples = len(X) - num_training X_train, y_train = X[:training_samples], y[:training_samples] X_test, y_test = X[training_samples:], y[training_samples:]

Now, we need to create a linear regressor object.

reg_linear_mul = linear_model.LinearRegression()

Train the object with the training samples.

reg_linear_mul.fit(X_train, y_train)

Now, at last we need to do the prediction with the testing data.

y_test_pred = reg_linear_mul.predict(X_test) print("Performance of Linear regressor:") print("Mean absolute error =", round(sm.mean_absolute_error(y_test, y_test_pred), 2)) print("Mean squared error =", round(sm.mean_squared_error(y_test, y_test_pred), 2)) print("Median absolute error =", round(sm.median_absolute_error(y_test, y_test_pred), 2)) print("Explain variance score =", round(sm.explained_variance_score(y_test, y_test_pred), 2)) print("R2 score =", round(sm.r2_score(y_test, y_test_pred), 2)) Output

Performance of Linear Regressor −

Mean absolute error = 0.6 Mean squared error = 0.65 Median absolute error = 0.41 Explain variance score = 0.34 R2 score = 0.33

Now, we will create a polynomial of degree 10 and train the regressor. We will provide the sample data point.

polynomial = PolynomialFeatures(degree = 10) X_train_transformed = polynomial.fit_transform(X_train) datapoint = [[2.23, 1.35, 1.12]] poly_datapoint = polynomial.fit_transform(datapoint) poly_linear_model = linear_model.LinearRegression() poly_linear_model.fit(X_train_transformed, y_train) print("nLinear regression:n", reg_linear_mul.predict(datapoint)) print("nPolynomial regression:n", poly_linear_model.predict(poly_datapoint)) Output

Linear regression −

[2.40170462]

Polynomial regression −

[1.8697225]

In the above code, we have used this small data. If you want a big dataset then, you can use sklearn.dataset to import a bigger dataset.

2,4.8,1.2,3.22.9,4.7,1.5,3.62.5,5,2.8,23.2,5.5,3.5,2.16,5, 2,3.27.6,4,1.2,3.23.2,0.9,2.3,1.42.9,1.9,2.3,1.22.4,3.5, 2.8,3.60.5,3.4,1.8,2.91,4,3,2.50.9,5.9,5.6,0.81.2,2.58, 3.45,1.233.2,5.6,2,3.25.1,1.5,1.2,1.34.5,1.2,4.1,2.32.3, 6.3,2.5,3.22.1,2.8,1.2,3.6

## A Beginner’s Guide To Boosting Your Holiday Sales Online

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.

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.

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.

## Ai Vs. Machine Learning Vs. Deep Learning

Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality.

Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future.

Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market. Among vendors selling big data analytics and data science tools, two types of artificial intelligence have become particularly popular: machine learning and deep learning.

While many solutions carry the “AI,” “machine learning,” and/or “deep learning” labels, confusion about what these terms really mean persists in the market place. The diagram below provides a visual representation of the relationships among these different technologies:

As the graphic makes clear, machine learning is a subset of artificial intelligence. In other words, all machine learning is AI, but not all AI is machine learning.

Similarly, deep learning is a subset of machine learning. And again, all deep learning is machine learning, but not all machine learning is deep learning.

Also see: Top Machine Learning Companies

AI, machine learning and deep learning are each interrelated, with deep learning nested within ML, which in turn is part of the larger discipline of AI.

Computers excel at mathematics and logical reasoning, but they struggle to master other tasks that humans can perform quite naturally.

For example, human babies learn to recognize and name objects when they are only a few months old, but until recently, machines have found it very difficult to identify items in pictures. While any toddler can easily tell a cat from a dog from a goat, computers find that task much more difficult. In fact, captcha services sometimes use exactly that type of question to make sure that a particular user is a human and not a bot.

In the 1950s, scientists began discussing ways to give machines the ability to “think” like humans. The phrase “artificial intelligence” entered the lexicon in 1956, when John McCarthy organized a conference on the topic. Those who attended called for more study of “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Critics rightly point out that there is a big difference between an AI system that can tell the difference between cats and dogs and a computer that is truly intelligent in the same way as a human being. Most researchers believe that we are years or even decades away from creating an artificial general intelligence (also called strong AI) that seems to be conscious in the same way that humans beings are — if it will ever be possible to create such a system at all.

If artificial general intelligence does one day become a reality, it seems certain that machine learning will play a major role in the system’s capabilities.

Machine learning is the particular branch of AI concerned with teaching computers to “improve themselves,” as the attendees at that first artificial intelligence conference put it. Another 1950s computer scientist named Arthur Samuel defined machine learning as “the ability to learn without being explicitly programmed.”

In traditional computer programming, a developer tells a computer exactly what to do. Given a set of inputs, the system will return a set of outputs — just as its human programmers told it to.

Machine learning is different because no one tells the machine exactly what to do. Instead, they feed the machine data and allow it to learn on its own.

In general, machine learning takes three different forms:

Reinforcement learning is one of the oldest types of machine learning, and it is very useful in teaching a computer how to play a game.

For example, Arthur Samuel created one of the first programs that used reinforcement learning. It played checkers against human opponents and learned from its successes and mistakes. Over time, the software became much better at playing checkers.

Reinforcement learning is also useful for applications like autonomous vehicles, where the system can receive feedback about whether it has performed well or poorly and use that data to improve over time.

Supervised learning is particularly useful in classification applications such as teaching a system to tell the difference between pictures of dogs and pictures of cats.

In this case, you would feed the application a whole lot of images that had been previously tagged as either dogs or cats. From that training data, the computer would draw its own conclusions about what distinguishes the two types of animals, and it would be able to apply what it learned to new pictures.

By contrast, unsupervised learning does not rely on human beings to label training data for the system. Instead, the computer uses clustering algorithms or other mathematical techniques to find similarities among groups of data.

Unsupervised machine learning is particularly useful for the type of big data analytics that interests many enterprise leaders. For example, you could use unsupervised learning to spot similarities among groups of customers and better target your marketing or tailor your pricing.

Some recommendation engines rely on unsupervised learning to tell people who like one movie or book what other movies or books they might enjoy. Unsupervised learning can also help identify characteristics that might indicate a person’s credit worthiness or likelihood of filing an insurance claim.

Various AI applications, such as computer vision, natural language processing, facial recognition, text-to-speech, speech-to-text, knowledge engines, emotion recognition, and other types of systems, often make use of machine learning capabilities. Some combine two or more of the main types of machine learning, and in some cases, are said to be “semi-supervised” because they incorporate some of the techniques of supervised learning and some of the techniques of unsupervised learning. And some machine learning techniques — such as deep learning — can be supervised, unsupervised, or both.

The phrase “deep learning” first came into use in the 1980s, making it a much newer idea than either machine learning or artificial intelligence.

Deep learning describes a particular type of architecture that both supervised and unsupervised machine learning systems sometimes use. Specifically, it is a layered architecture where one layer takes an input and generates an output. It then passes that output on to the next layer in the architecture, which uses it to create another output. That output can then become the input for the next layer in the system, and so on. The architecture is said to be “deep” because it has many layers.

To create these layered systems, many researchers have designed computing systems modeled after the human brain. In broad terms, they call these deep learning systems artificial neural networks (ANNs). ANNs come in several different varieties, including deep neural networks, convolutional neural networks, recurrent neural networks and others. These neural networks use nodes that are similar to the neurons in a human brain.

However, those GPUs also excel at the type of calculations necessary for deep learning. As GPU performance has improved and costs have decreased, people have been able to create high-performance systems that can complete deep learning tasks in much less time and for much less cost than would have been the case in the past.

Today, anyone can easily access deep learning capabilities through cloud services like Amazon Web Services, Microsoft Azure, Google Cloud and IBM Cloud.

If you are interested in learning more about AI vs machine learning vs deep learning, Datamation has several resources that can help, including the following:

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