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abhinav3045 · 7 months ago

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K-Means Clustering in Python: Step-by-Step Example

by Zach BobbittPosted on August 31, 2022

One of the most common clustering algorithms in machine learning is known as k-means clustering.

K-means clustering is a technique in which we place each observation in a dataset into one of K clusters.

The end goal is to have K clusters in which the observations within each cluster are quite similar to each other while the observations in different clusters are quite different from each other.

In practice, we use the following steps to perform K-means clustering:

1. Choose a value for K.

First, we must decide how many clusters we’d like to identify in the data. Often we have to simply test several different values for K and analyze the results to see which number of clusters seems to make the most sense for a given problem.

2. Randomly assign each observation to an initial cluster, from 1 to K.

3. Perform the following procedure until the cluster assignments stop changing.

For each of the K clusters, compute the cluster centroid. This is simply the vector of the p feature means for the observations in the kth cluster.

Assign each observation to the cluster whose centroid is closest. Here, closest is defined using Euclidean distance.

The following step-by-step example shows how to perform k-means clustering in Python by using the KMeans function from the sklearn module.

Step 1: Import Necessary Modules

First, we’ll import all of the modules that we will need to perform k-means clustering:import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler

Step 2: Create the DataFrame

Next, we’ll create a DataFrame that contains the following three variables for 20 different basketball players:

points

assists

rebounds

The following code shows how to create this pandas DataFrame:#create DataFrame df = pd.DataFrame({'points': [18, np.nan, 19, 14, 14, 11, 20, 28, 30, 31, 35, 33, 29, 25, 25, 27, 29, 30, 19, 23], 'assists': [3, 3, 4, 5, 4, 7, 8, 7, 6, 9, 12, 14, np.nan, 9, 4, 3, 4, 12, 15, 11], 'rebounds': [15, 14, 14, 10, 8, 14, 13, 9, 5, 4, 11, 6, 5, 5, 3, 8, 12, 7, 6, 5]}) #view first five rows of DataFrame print(df.head()) points assists rebounds 0 18.0 3.0 15 1 NaN 3.0 14 2 19.0 4.0 14 3 14.0 5.0 10 4 14.0 4.0 8

We will use k-means clustering to group together players that are similar based on these three metrics.

Step 3: Clean & Prep the DataFrame

Next, we’ll perform the following steps:

Use dropna() to drop rows with NaN values in any column

Use StandardScaler() to scale each variable to have a mean of 0 and a standard deviation of 1

The following code shows how to do so:#drop rows with NA values in any columns df = df.dropna() #create scaled DataFrame where each variable has mean of 0 and standard dev of 1 scaled_df = StandardScaler().fit_transform(df) #view first five rows of scaled DataFrame print(scaled_df[:5]) [[-0.86660275 -1.22683918 1.72722524] [-0.72081911 -0.96077767 1.45687694] [-1.44973731 -0.69471616 0.37548375] [-1.44973731 -0.96077767 -0.16521285] [-1.88708823 -0.16259314 1.45687694]]

Note: We use scaling so that each variable has equal importance when fitting the k-means algorithm. Otherwise, the variables with the widest ranges would have too much influence.

Step 4: Find the Optimal Number of Clusters

To perform k-means clustering in Python, we can use the KMeans function from the sklearn module.

This function uses the following basic syntax:

KMeans(init=’random’, n_clusters=8, n_init=10, random_state=None)

where:

init: Controls the initialization technique.

n_clusters: The number of clusters to place observations in.

n_init: The number of initializations to perform. The default is to run the k-means algorithm 10 times and return the one with the lowest SSE.

random_state: An integer value you can pick to make the results of the algorithm reproducible.

The most important argument in this function is n_clusters, which specifies how many clusters to place the observations in.

However, we don’t know beforehand how many clusters is optimal so we must create a plot that displays the number of clusters along with the SSE (sum of squared errors) of the model.

Typically when we create this type of plot we look for an “elbow” where the sum of squares begins to “bend” or level off. This is typically the optimal number of clusters.

The following code shows how to create this type of plot that displays the number of clusters on the x-axis and the SSE on the y-axis:#initialize kmeans parameters kmeans_kwargs = { "init": "random", "n_init": 10, "random_state": 1, } #create list to hold SSE values for each k sse = [] for k in range(1, 11): kmeans = KMeans(n_clusters=k, **kmeans_kwargs) kmeans.fit(scaled_df) sse.append(kmeans.inertia_) #visualize results plt.plot(range(1, 11), sse) plt.xticks(range(1, 11)) plt.xlabel("Number of Clusters") plt.ylabel("SSE") plt.show()

In this plot it appears that there is an elbow or “bend” at k = 3 clusters.

Thus, we will use 3 clusters when fitting our k-means clustering model in the next step.

Note: In the real-world, it’s recommended to use a combination of this plot along with domain expertise to pick how many clusters to use.

Step 5: Perform K-Means Clustering with Optimal K

The following code shows how to perform k-means clustering on the dataset using the optimal value for k of 3:#instantiate the k-means class, using optimal number of clusters kmeans = KMeans(init="random", n_clusters=3, n_init=10, random_state=1) #fit k-means algorithm to data kmeans.fit(scaled_df) #view cluster assignments for each observation kmeans.labels_ array([1, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0])

The resulting array shows the cluster assignments for each observation in the DataFrame.

To make these results easier to interpret, we can add a column to the DataFrame that shows the cluster assignment of each player:#append cluster assingments to original DataFrame df['cluster'] = kmeans.labels_ #view updated DataFrame print(df) points assists rebounds cluster 0 18.0 3.0 15 1 2 19.0 4.0 14 1 3 14.0 5.0 10 1 4 14.0 4.0 8 1 5 11.0 7.0 14 1 6 20.0 8.0 13 1 7 28.0 7.0 9 2 8 30.0 6.0 5 2 9 31.0 9.0 4 0 10 35.0 12.0 11 0 11 33.0 14.0 6 0 13 25.0 9.0 5 0 14 25.0 4.0 3 2 15 27.0 3.0 8 2 16 29.0 4.0 12 2 17 30.0 12.0 7 0 18 19.0 15.0 6 0 19 23.0 11.0 5 0

The cluster column contains a cluster number (0, 1, or 2) that each player was assigned to.

Players that belong to the same cluster have roughly similar values for the points, assists, and rebounds columns.

Note: You can find the complete documentation for the KMeans function from sklearn here.

Additional Resources

The following tutorials explain how to perform other common tasks in Python:

How to Perform Linear Regression in Python How to Perform Logistic Regression in Python How to Perform K-Fold Cross Validation in Python

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studio45creation · 1 year ago

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Python Developer in Chicago Unlocking Opportunities with Studio45Creations

Python has emerged as one of the most popular programming languages in the world due to its versatility, ease of learning, and vast ecosystem. In Chicago, the demand for skilled Python developers is on the rise, driven by the city's thriving tech scene, diverse industries, and a robust job market. This blog explores the landscape for Python developers in Chicago, highlighting the opportunities, key industries, required skills, and the role of Studio45Creations in this dynamic environment.

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The Demand for Python Developers

Python's popularity as a programming language is reflected in its widespread adoption across different industries. Its versatility allows it to be used in web development, data science, artificial intelligence (AI), machine learning (ML), automation, and more. In Chicago, Python developers are in high demand across several sectors:

Finance: Chicago is home to major financial institutions, trading firms, and fintech startups. Python is extensively used for algorithmic trading, risk management, and financial data analysis.

Healthcare: The healthcare industry leverages Python for data analysis, predictive modeling, and the development of healthcare applications. Chicago's medical institutions and healthtech companies are keen on hiring Python developers.

E-commerce: With the growth of e-commerce platforms, businesses in Chicago are seeking Python developers to build and maintain scalable web applications, implement recommendation systems, and analyze customer data.

Manufacturing: Chicago's manufacturing sector is adopting Industry 4.0 technologies, including automation and IoT, where Python plays a crucial role in scripting and data processing.

Startups: The city's vibrant startup scene offers numerous opportunities for Python developers to work on innovative projects, ranging from AI-driven solutions to cutting-edge web applications.

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Proficiency in Python: A deep understanding of Python programming, including core concepts, libraries, and frameworks, is fundamental.

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The demand for Python developers in Chicago continues to grow, driven by the city's vibrant tech scene and diverse industries. Studio45Creations a leading software development company, plays a crucial role in this dynamic environment, offering innovative solutions and exciting career opportunities for Python developers. With a focus on web development, data science, AI, and automation, Studio45Creations leverages the power of Python to deliver exceptional results for its clients.

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popyseo · 2 years ago

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shanto4 · 2 years ago

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abbsee · 4 years ago

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aiplindore · 8 years ago

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Panda, Penguin, Hummingbird, Pigeon are among the most popular Google updates on the internet with Penguin 4.0 being the latest one among the versions rolled out. There have been some other technical updates too which, together with the previous ones, have changed the way internet search results work. Let us find out more about them in this infographic.

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nummero123 · 4 years ago

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Google Penalties That Destroy Traffic and How to Avoid Them

Google takes its ranking system seriously, regularly changing it to give consumers the greatest search experiences possible.

This includes penalizing pages or sites that do not follow Google's Webmaster Guidelines.

A Google penalty can be incurred intentionally through black hat SEO,

Accidentally through poor site upkeep, or simply as a result of an algorithm update.

Google penalties, in any case, have a detrimental impact on your search rankings,

and in some situations, your pages or entire website may be removed from results.

So, in this post, we'll go through what NOT to do (and what to do instead) to avoid a Google penalty and save your site from suffering a traffic decline. We'll go over:

Manual vs. algorithmic What to expect from Google penalties.

How to detect and repair a manual Google penalty.

Seven of the most common Google penalties and how to prevent them

By the end, you'll be able to maintain your ranks and continue to develop your website traffic.

What is a Google penalty?

When Google discovers that a website violates its Webmaster Guidelines, it imposes a penalty.

There are two forms of penalties, but both result in a decrease in ranking and traffic.

Algorithmic penalties

Google updates its algorithm every year to continue giving the best results to its searchers.

Panda, Penguin, Pigeon, and Hummingbird are some of the most notable Google upgrades.

Some algorithm updates, such as Panda (keyword stuffing, grammatical errors, and low-quality content)

and Penguin (black hat linking tactics), is intended to lower the rank of guideline-violating pages, whereas others,

such as Pigeon (solid local signals) and Hummingbird (newly prioritized ranking factors),

are intended to favour pages with newly prioritized ranking factors (mobile responsiveness).

In that vein, are you aware of the page experience and mobile-first indexing updates?

A website's score may drop after an algorithm upgrade,

either because it broke guidelines or because other sites better aligned with specific ranking parameters.

Here's an example of traffic data from a site following the BERT update.

How to fix an algorithmic Google penalty

You can't check for algorithmic penalties because they aren't expressly specified anywhere.

The best thing you can do is determine if your traffic decline coincides with the release of an algorithm change,

and then learn everything you can about it so you can identify any modifications.

You need to make coming forward or any fixes you need to make to existing material.

Depending on the upgrade and the degree to which your website was out of sync with it,

your repairs may or may not result in a restoration of ranking and traffic.

While algorithmic improvements occur regularly, they all serve to reward sites for EAT and good technical performance,

thus they should be your primary emphasis.

Manual penalties

Manual penalties imposed by Google personnel for pages that have possibly unintentional flaws such as content quality and security,

or for actively manipulating Google's algorithm using black hat SEO.

Manual penalties, as opposed to algorithm penalties, are simple to discover and correct.

How to fix a manual Google penalty

There are various Google penalty checker tools accessible, but Google Search Console is also an option.

In your dashboard, navigate to the Security & Manual Actions tab and select Manual Actions.

There, you can discover which policy you violated, which pages were affected, and how to repair it.

You can submit it for review once it has got rectified.

Employees at Google will review the request and, if it is correctly corrected, approve it and re-index your page.

What are the consequences of a Google penalty?

Any penalty results in a decrease in rank, however, the degree of the drop varies depending on the sort of penalty issued.

Penalties at the keyword level: Your ranking will decline for a certain term.

Penalties at the URL or directory level: A certain URL's ranking will suffer.

Domain-wide or site-wide penalties: Your site's ranking will decline for some URLs and keywords.

Delisting or de-indexing: This is Google's most severe penalty, in which they remove your domain from the Google index.

As a result, none of the material on your website will displayed on Google.

How long do Google penalties last?

Google sanctions are in effect until they are removed.

If a penalty remains unfixed for an extended length of time,

the alert will removed from your Search Console, but the penalty's repercussions will stay in force.

In other words, you've blown your chance to make things right with Google.

However, after the penalty is lifted, your site's traffic and rankings may or may not recover.

Check out this page on Google penalty recovery timelines for more information.

The top reasons for Google penalties and how to prevent them

Every company aims to be on the first page of Google to improve traffic to their website and, as a result, gain more clients.

To accomplish this (through SEO), a lengthy amount of effort and patience is required.

This is why many people, particularly those who have recently established a blog or website, are tempted to take shortcuts to boost their ranking.

However, these strategies simply result in Google penalties.

The most common penalties are listed below, as well as what you may do to avoid and/or correct them.

1. Thin content and doorway pages

This occurs when website owners prioritize quantity above quality SEO material, believing that more content equals more visitors.

They may employ content generation software, produce short-form pieces, or scrape content from other sources.

Not only are these bad SEO methods detectable by Google, but low-quality material also reflects negatively on your company.

The Panda 4.0 algorithm upgrade in 2016 was designed to lower the exposure of low-quality material and gateway sites in search results.

How to prevent the thin content penalty:

Don't completely outsource or try to mass-produce your material. Mass-produced material is never of high quality, and outsourcing can result in off-brand and incoherent content.

If you need assistance growing your quality content, contact freelancers with whom you can collaborate closely and who specialize in your sector to create pages that add value to your readers.

Conduct thorough keyword research to ensure that you target the correct keywords and that your content corresponds to the query's intent.

Instead of doorway pages, create pillar pages or cornerstone content.

Combine short pages optimized for comparable keywords into a single long page with more information on a single topic.

2. Hidden text and links

Hide any material or links for the sake of SEO rather than user experience violates Google's Webmaster Rules.

Text and links can concealed in a variety of ways, including:

Increasing the text size to 0

Using white text or background connecting

Putting text behind an image

Using CSS to move text off-screen

Using the same colour as the backdrop for the links

How to prevent the hidden content penalty

To begin with, never do it on purpose.

If you have to hide something, it definitely shouldn't be on your page in the first place.

If you did not do this on purpose,

Go to your Search Console's URL inspection tab, type the affected pages into the search box, and then click "see crawled page."

You can look for any hidden links or CSS there.

3. User-generated spam

If you host a forum, allow guest articles, or allow comments on your blog, you may inundated with spam-bots or bad actors.

Spam links may lead to low-quality or inappropriate pages, undermining the T in EAT.

Alternatively, actual humans will leave a comment on your blog with one or more irrelevant links solely to obtain a back-link from your site to boost their domain authority.

How to prevent the user-generated spam penalty

Here are a few methods for preventing user-generated spam on your website and forum.

Tools for comment moderation

Disqus used by us to filter, remove, and block spammy comments.

You can also approve comments before they are made public on your site.

If you can't keep up with moderation, even with a plugin or tool, turn off commenting entirely.

Anti-spam tools

Spammers swarm your comment section with automated scripts.

Integrate Google re-CAPTCHA into your website to prevent spam comments.

No-follow and UGC attributes

If a guest poster or commentator adds an appropriate link but one with which you do not want to be linked, you can tag it to make it a no-follow link.

This stops Google from following those links off your page and passing link juice from your site to the linked site.

No-index meta tag

If you allow users to write articles on your site, you can mark such pages with a noindex meta tag.

This manner, the page will be accessible via your website but will not appear in search results or be factored into Google's ranking system.

4. Unnatural or poor links to your site

Google's Penguin algorithm upgrade in 2016 intended to detect artificial link creation.

Back-link building is an extremely efficient SEO approach for increasing page authority—

but only if the links originate naturally from high-quality sources.

How to prevent the unnatural link penalty

Of course, adopt a link-building approach that does not include:

Purchasing or selling links

Exchanges of links (Link my website and I will link to you)

Links to your forum profile and signature

Links to blog comments

Links to article directories

Building an excessive number of links in a short time

PBN hyperlinks

Perform regular back-link audits

Unintentionally, you may also receive spammy links to your website.

Analyze your back-link profile using Google Analytics, Search Console.

5. Keyword stuffing

On-page SEO, such as including keywords in the title, headings, body, meta description, and alt text, assists

Googlebot in determining the purpose of your website.

However, keyword stuffing on purpose is a black hat SEO strategy that will result in a Google penalty.

You're probably aware of what keyword stuffing in the body looks like, but you can also punished for keyword stuffing in the alt text.

6. Hacked website

If hackers obtain access to your website,

they can not only threaten the confidentiality, but also inject dangerous code, add irrelevant material, or redirect your site to malicious or spammy domains.

Your site's rating will plummet on all search queries, and Google may remove your entire website from search results as a result of this punishment.

How to prevent the hacked website penalty

There are various ways to improve the security of your website:

Maintain the integrity of your content management system.

Use strong passwords that you update regularly.

Use an SSL certificate.

Invest in good hosting.

To detect hacks, use a malware scanner tool.

Back up your website regularly.

To prevent brute force attacks, hide the login URL and limit the number of login attempts.

7. Abusing structured data mark-up

Structured data mark-up is a sort of coding that allows Google to present your site more beautifully in search results,

such as by displaying star ratings and the number of reviews.

However, if Google discovers that you are using structured data that is irrelevant to the content or users, you may be penalized manually.

How to prevent the structured data penalty

This is yet another penalty for black hat SEO. Here's what you should do:

Don't use phony reviews to boost your CTR. Follow these recommended methods for obtaining genuine Google evaluations.

Use structured data only when it makes sense for the information you're marking up.

Make certain that your mark-up material is visible to readers.

Add no schema mark-up relating to unlawful activity, violence, or any other forbidden content.

Conclusion

Google penalties, whether algorithmic or human, harm your ranking and traffic.

You can apply remedies to remove the penalty, however you may or may not recover your traffic and ranking.

That is why it is critical to do all possible to avoid them from happening in the first place.

You can contact Nummero Digital Marketing Agency in Bangalore.

#Google Penalties That Destroy Traffic and How to Avoid Them #digital marketing companies in bangalore

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ggonecrane-ml4daprojects · 4 years ago

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Week 3 Assignment - Running a Lasso Regression Analysis

Introduction

In this assignment I am going to perform a Lasso Regression analysis using k-fold cross validation to identify a subset of predictors from 24 or so of predictor variables that best predicts my response variable. My data set is from Add Health Survey, Wave 1.

My response variable is students’ academic performance in the measurement of students grade point average, labelled as GPA1, in which 4.0 is the maximum value.

The predictor variables I have used for this analysis are as follows:

Data Management

Since my original raw dataset contains quite a few missing values in some columns and rows, I have created a new dataset by removing any rows with missing data via Pandas’ dropna() function. Also, I have managed BIO_SEX variable by re-coding its female value to 0 and male value to 1, and called it a new variable MALE.

Program

My Python source for this analysis is appended at the bottom of this report.

Analysis

In this analysis, I have programmed to randomly split my dataset into a training dataset consisting of 70% of the total observations and a test dataset consisting of the other 30% of the observations. In terms of model selection algorithm, I have used Least Angle Regression (LAR) algorithm.

First, I have run k-fold cross-validation with k =10, meaning 10 random folds from the training dataset to choose the final statistical model. The following is the list of coefficient values obtained:

-------------------------------------------------------------------------------------

Coefficient Table (Sorted by Absolute Value)

-------------------------------------------------------------------------------------

{'WHITE': 0.0,

'INHEVER1': 0.0002990082208926463,

'PARPRES': -0.002527493803234668,

'FAMCONCT': -0.009013886132829411,

'EXPEL1': -0.009800613179381865,

'NAMERICAN': -0.011971527818286374,

'DEP1': -0.013819592163450321,

'COCEVER1': -0.016004694549008488,

'ASIAN': 0.019825632747609248,

'ALCPROBS1': 0.026109998722006623,

'ALCEVR1': -0.030053393438026644,

'DEVIANT1': -0.03398238436410221,

'ESTEEM1': 0.03791720034719747,

'PASSIST': -0.041636965715495806,

'AGE': -0.04199151644577515,

'CIGAVAIL': -0.04530490276829347,

'HISPANIC': -0.04638996070701919,

'MAREVER1': -0.06950247134179253,

'VIOL1': -0.07079177062520403,

'BLACK': -0.08033742139890393,

'PARACTV': 0.08546097579217665,

'MALE': -0.108287101884112,

'SCHCONN1': 0.11836667293459634}

As the result shows, there was one predictor at the top, WHITE ethnicity, of that regression coefficient shrunk to zero after applying the LASSO regression penalty. Hence, WHITE predictor would not be made to the list of predictors in the final selection model. Among the predictors made to the list though, the last five predictors turned out to be the most influential ones: SCHCONN1, MALE, PARACTV, BLACK, and VIOL1.

SCHCONN1 (School connectedness) and PARACTV (Activities with parents family) show the largest regression coefficients, 0.118 and 0.085, respectively. They apparently are strongly positively associated with students GPA. MALE (Gender), BLACK (Ethnicity), and VIOL1 (Violence) however, coefficients of -0.108, -0.080, and -0.070, respectively, are the ones strongly negatively associated with students GPA.

The following Lasso Path plot depicts such observations graphically:

The plot above shows the relative importance of the predictor selected at any step of the selection process, how the regression coefficients changed with the addition of a new predictor at each step, as well as the steps at which each variable entered the model. As we already saw in the list of the regression coefficients table above, two of positive strongest predictors are paths, started from low x-axis, with green and blue color, SCHCONN1 and PARACTV, respectively. Three of negative strongest predictors are the ones, started from low x-axis, drawn downward as the alpha value on the x-axis increases, with brown (MALE), grey (BLACK), and cyan (VIOL1) colors, respectively.

The following plot shows mean square error on each fold:

We can see that there is variability across the individual cross-validation folds in the training data set, but the change in the mean square error as variables are added to the model follows the same pattern for each fold. Initially it decreases rapidly and then levels off to a point at which adding more predictors doesn't lead to much reduction in the mean square error.

The following is the average mean square error on the training and test dataset.

-------------------------------------------------------------------------------------

Training Data Mean Square Error

-------------------------------------------------------------------------------------

0.4785435409557714

-------------------------------------------------------------------------------------

Test Data Mean Square Error

-------------------------------------------------------------------------------------

0.44960217328334645

As expected, the selected model was less accurate in predicting students GPA in the test data, but the test mean square error was pretty close to the training mean square error. This suggests that prediction accuracy was pretty stable across the two data sets.

The following is the R square for the proportion of variance in students GPA:

-------------------------------------------------------------------------------------

Training Data R-Square

-------------------------------------------------------------------------------------

0.20331942870725228

-------------------------------------------------------------------------------------

Test Data R-Square

-------------------------------------------------------------------------------------

0.2183030945000226

The R-square values were 0.20 and 0.21, indicating that the selected model explained 20 and 21% of the variance in students GPA for the training and test sets, respectively.

======== Program Source Begin =======

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Feb 5 06:54:43 2021

@author: ggonecrane """

#from pandas import Series, DataFrame import pandas as pd import numpy as np import matplotlib.pylab as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LassoLarsCV import pprint

def printTableLabel(label): print('\n') print('-------------------------------------------------------------------------------------') print(f'\t\t\t\t{label}') print('-------------------------------------------------------------------------------------')

#Load the dataset data = pd.read_csv("tree_addhealth.csv")

#upper-case all DataFrame column names data.columns = map(str.upper, data.columns)

# Data Management recode1 = {1:1, 2:0} data['MALE']= data['BIO_SEX'].map(recode1) data_clean = data.dropna()

resp_var = 'GPA1' # 'SCHCONN1' # exp_vars = ['MALE','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN', 'AGE','ALCEVR1','ALCPROBS1','MAREVER1','COCEVER1','INHEVER1','CIGAVAIL','DEP1', 'ESTEEM1','VIOL1','PASSIST','DEVIANT1','GPA1','EXPEL1','FAMCONCT','PARACTV', 'PARPRES','SCHCONN1'] exp_vars.remove(resp_var)

#select predictor variables and target variable as separate data sets predvar= data_clean[exp_vars]

target = data_clean[resp_var]

# standardize predictors to have mean=0 and sd=1* predictors=predvar.copy() from sklearn import preprocessing

for key in exp_vars: predictors[key]=preprocessing.scale(predictors[key].astype('float64'))

# split data into train and test sets pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, target, test_size=.3, random_state=123)

# specify the lasso regression model model=LassoLarsCV(cv=10, precompute=False).fit(pred_train,tar_train)

# print variable names and regression coefficients res_dict = dict(zip(predictors.columns, model.coef_)) pred_dict = dict(sorted(res_dict.items(), key=lambda x: abs(x[1]))) printTableLabel('Coefficient Table (Sorted by Absolute Value)') pprint.pp(pred_dict)

# plot coefficient progression m_log_alphas = -np.log10(model.alphas_) ax = plt.gca() plt.plot(m_log_alphas, model.coef_path_.T) plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k', label='alpha CV') plt.ylabel('Regression Coefficients') plt.xlabel('-log(alpha)') plt.title('Regression Coefficients Progression for Lasso Paths')

# plot mean square error for each fold m_log_alphascv = -np.log10(model.cv_alphas_) plt.figure() plt.plot(m_log_alphascv, model.mse_path_, ':') plt.plot(m_log_alphascv, model.mse_path_.mean(axis=-1), 'k', label='Average across the folds', linewidth=2) plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k', label='alpha CV') plt.legend() plt.xlabel('-log(alpha)') plt.ylabel('Mean squared error') plt.title('Mean squared error on each fold')

# MSE from training and test data from sklearn.metrics import mean_squared_error train_error = mean_squared_error(tar_train, model.predict(pred_train)) test_error = mean_squared_error(tar_test, model.predict(pred_test)) printTableLabel('Training Data Mean Square Error') print(train_error) printTableLabel('Test Data Mean Square Error') print(test_error)

# R-square from training and test data rsquared_train=model.score(pred_train,tar_train) rsquared_test=model.score(pred_test,tar_test) printTableLabel('Training Data R-Square ') print(rsquared_train) printTableLabel('Test Data R-Square ') print(rsquared_test)

======== Program Source End =======

0 notes

milescpareview · 4 years ago

Text

All You Need to Know About AI-ML and Its Applications

“The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast - it is growing at a pace close to exponential" states Elon Musk, CEO, SpaceX.

A close look at the AI market showcases that automation with AI is trending with all major corporations. The global market value of AI was pegged at USD 40 billion in 2019 and starting 2020, it is slated to grow at a CAGR of 42.2% for 7 years. The introduction of AI will boost corporate incomes by 38%. It is the efficiency of AI-systems which allows for low-cost installations with high productivity.

In 2020, Covid-19 has gravely impacted global economic health. Yet economists view it as an opportunity for greater reform, stating that grave recession leads to greater automation.

How did digital devices vest AI-automation?

From switch operations to coded commands, the transformation of computational powers has been phenomenal. Computers are increasingly smarter whilst processing data from varied sources, self-learning and improving its capacity to retain and read Big Data in microseconds. This gave birth to machine learning or ML. Programmers coded inherent capabilities to computers to react and recreate, and today we call it artificial intelligence or AI. Now, It has branched into subcontexts like Natural Language Processing (NLP), Speech Recognition, and Machine Vision. These are intertwined in various disciplines as applications of AI-ML. What are the principal subtypes of Machine Learning in use?

What differentiates AI from normal programming is the ML algorithm. It helps computers to analyse and learn from data to perform its task, and it gets better with more experiences and data. Based on AI applications, machines can learn in three principal ways:-

1) Supervised Learning-

It aims to train the machine to observe patterns from a controlled set of input and react as instructed to deliver a desirable output. The way social media recognizes faces and tags is a common application of supervised learning.

2) Unsupervised Learning-

It is not limited to learn from restricted data sets. Rather appointed to learn the pattern from continuous incoming feed and responds to slightest change in usual patterns.. The system works perfectly for cybersecurity inspectors to detect abnormal behavior in server-centric systems.

3) Reinforcement Learning-

An offset of unsupervised learning where the system decides on how to react to a certain sequence of events. It allows the machine to learn from an uncertain environment and capture the concern and basis the pattern, deciding on the response. It enables AI to make business plans, marketing strategies, search recommendations, and others.

What are the levels of AI?

The more advanced machine learning becomes, the better is the impact of AI. Measuring AI's success in mimicking human intelligence, it can be -

1) Reactive - AI that can respond to stimuli

2) Limited Memory - AI that uses memory to learn and respond

3) Theory of Mind - AI that understands

4) Self Aware - AI that is aware of its existence and needs

The potential that AI can achieve has 3 stratification's -

1) Artificial Narrow Intelligence (ANI) - Represents existing AI devices

2) Artificial General Intelligence (AGI) - Systems that have near-human abilities

3) Artificial Super Intelligence (ASI) - The most capable form of AI to achieve

Within a narrow timeframe, AI has sneaked into our lives in every possible way. Your music application playing your favorite song or your favorite search engine displays accurate predictions before you complete typing the query is not pure luck. The software we use today takes advantage of AI to provide more human-like responses than behaving like mere machines.

What are the trending AI applications in the market? The thriving AI-digital age is augmenting new technological disruptions with applications like -

AI IoT (AIoT) - For near-perfect CRM and personal assistance

AI for NLP -The speech-to-text tool for Speech API Adaptors

Edge Computing - A cloud storage system to manage data for IoT systems

Recommender Systems - Offering customized buying choices for visitors

Computer Vision (CV) - with multiple object detection ability

Behavioral Artificial Intelligence (BAI) - asserting brand improvement and predicting customer trends

How Industry 4.0 is preparing to be AI-ready? In our digital frontier, AI has no intention to sabotage human life. Let’s not get carried away by over-exaggeration of science-fiction movies that robots are going to take over. Scientists say that it is AI that will bring back humanity in the mechanized world. Looking at the bright side, there will be 29% growth in AI job listing and India ranks among the top five countries to globally drive AI growth.

The future is AI. Learning AI will give you an advantage and leverage your career in how you want your future to be. If you aspire to boost your career prospects and learn AI-ML applications, IIT Mandi and Wiley offer PG Certification in Applied AI and ML. This course will walk you through Deep AI processing and industry-specific tools for the entire AI lifecycle. You can learn modeling user-friendly APIs with tools like Python, Keras, Tensorflow, NLTK, NumPy, Scikit-learn, Pandas, Jupyter, and Matplotlib.

Plus you will learn to 'Apply AI' in real-world scenarios under the guidance of the top industry experts organized by the Wiley Innovation Advisory Council.

For over 200 years, Wiley has been helping people and organizations develop the skills and knowledge they need to succeed. They are dedicated to developing efficient learning products, digital transformation education, learning, assessment, and certification solutions to help universities, businesses, and individuals move between education and employment and achieve their ambitions. In 2020, WileyNXT has been recognized by Fast Company for its outstanding innovations in education.

Miles Education, always committed to your career success brings to you new-age certifications in Finance and Emerging Technologies. We have partnered with IIT Roorkee, IIT Mandi, IIM Lucknow, IIM Kozhikode and WileyNXT to present PG certifications in AI, ML, Deep Learning and Analytics.

To know more visit Miles Education today.

#machine learning #ai #artificial intelligence #ml #course

0 notes

philipfloyd · 5 years ago

Text

The Unique SEO Content Strategy to Rank #1 with Zero Links

We keep hearing that content is king. Write great content, content here, content there. But how true is that statement? Is there such a thing as an SEO Content Strategy that you can use to rank #1 with zero backlinks?

Like most answers in SEO, this one is also ‘it depends’.

Keep reading and I’ll explain exactly why.

While you might not be able to get to position one without any backlinks for a highly competitive keyword, you’ll definitely be able to increase your rankings in Google just by optimizing your content and having in place a great SEO content strategy.

By the end of this blog post, you’ll have a better understanding of how Google treats links and content and you’ll get the SEO Content Strategy that might help rank #1 on Google with zero backlinks.

Can I Really Rank #1 with Zero Links?

Why Is Google Pushing More Weight to the Content Ranking Factor?

Step-by-step SEO Content Strategy to Rank #1 with Zero Links

Identify Your Primary Audience and Their Pain Points

Perform an In-depth Keyword Research & Audit

Make a List of the Keywords You Can Realistically Dominate

Analyze the Search Intent of Your Keywords

Optimize Landing Pages / Per Keyword & Per Search Intent

Write Content That Stands Out and Is Share Worthy

Launch & Share Your Content with Key Influencers

Monitor How Your Content Is Performing

Update Your Content Once in a While

Conclusion

1. Can I Really Rank #1 with Zero Links?

Not long ago we performed a lot of searches for pharma, gambling and skincare keywords, in a nutshell on highly competitive keywords and niches.

Our bet was to figure out if there are pages within these niches ranking based on high quality content only.

As you can see in the screenshot below taken from cognitiveSEO’s Content Optimizer, you can see that there are many pages with no or just a few links but highly optimized content, that rank really well.

Of course, we cannot answer to this question with a simple yes or no, yet, with Google giving so much weight to content lately, we might say that a good SEO content strategy can help you out big time.

Of course, it depends based on what you mean by rankings and it depends on what you mean by links.

For example, someone might claim they rank without any links, but what are they ranking for?

Are they ranking for a high competition, high search volume keyword, or a long tail, low competition and low search volume one?

Some time ago, Steven Kang started a contest in the SEO Signals Facebook Group, with a prize pool of $1000, to whoever could find or pinpoint a website that ranked #1 on Google with no backlinks.

However, the rules for this contest were very specific:

There can be no external backlinks present. Any single external backlink to the domain discovered will disqualify the site.

The site can have internal links since some claim internal linking shouldn’t count for such claim.

The site must be written in English.

One year after, the contest is still available. Nobody has won it yet.

By the looks of it… it seems that it’s difficult to rank on Google without backlinks.

However, in one case a website was eliminated and it had backlinks only from Social Media platforms such as Facebook, Twitter and Pinterest.

While those are indeed backlinks, almost every website has them and they don’t pass as much value as a contextual link from a high quality, relevant website.

Other contestants have said that the contest is not fair, as they did not buy or build the links, but they simply came naturally, just as Google wants.

However… they’re still links, aren’t they?

The contest therefore excludes all links, not only purchased, built or bought ones. Considering this, Social Media is also excluded and probably any other form of online promotion.

So when it comes to ranking with zero links… Are you talking about links to that specific web page, or links to the entire domain?

Are you talking about a made-up keyword with no search volume whatsoever, or about a profitable, competitive, highly searched for keyword with a great market value?

If you’re trying to rank for a very competitive keyword, where the top-ranking domains have thousands of backlinks pointing to them… tough luck!

The truth is that beside a solid SEO content strategy, links still matter and if your competition is building, buying or obtaining backlinks naturally, you’ll have a difficult time ranking #1 with zero backlinks.

But if the competition doesn’t have many backlinks either, it is possible to outrank them, even without links, in some cases.

There are many examples of websites which outrank their competition with fewer backlinks.

For example, we’re outranking Moz for the keyword “does meta description affect seo” although we only have 23 referring domains, while Moz’s page has over 1500.

For another similar phrase, “does meta description matter”, another website is ranking very well, with only 1 referring domain.

However, in both cases the results are an answer box. If you remember, some time ago you could have the answer box position and another spot.

Luckily, when Google changed this and only let you keep one or the other, they let us have the answer box.

These are pretty specific keywords and for the most competitive phrase, “meta description”, Moz still dominates the top spot.

Ranking with just a few backlinks to a web page is possible, if you have the right content.

But it’s probably necessary to have at least some backlinks pointing to your domain.

The internet doesn’t really work well without links. Search engines use links to crawl the entire web.

It’s also true that without any backlinks pointing to your domain, Google will have a hard time crawling and indexing your site.

However, keep in mind that backlinks on their own won’t do very much if the content doesn’t satisfy the users. They can even do harm.

After the Penguin updates, it was pretty clear Google was set to fight back against link spam.

Maybe the top spot isn’t achievable with absolutely zero links, but you don’t need the top spot to increase your search traffic.

You can greatly improve rankings in Google simply by optimizing your content even for competitive, high search volume keywords.

Many other SEOs and content marketers have improved their rankings following this OnPage SEO technique.

If you stick around, I’ll show you exactly how you can do it, too.

But first, let’s see why Google is pushing away from backlinks and more towards content.

2. Why Is Google Pushing More Weight to the Content Ranking Factor?

Back in 2017, during our numerous tests, we found a big correlation between the Content Performance score we had developed and rankings.

We repeated the test after 2 years, in 2019 and it seemed that content is even more important now.

The algorithm we use is complex, using a mix of AI technologies and things such as LSI and Flesch–Kincaid to determine what makes a piece of content rank high.

Attempts to remove backlinks from the algorithms have been made before.

Why? Mostly because of spam. As long as backlinks are a ranking signal, people will try to abuse them.

In 2013, Yandex, the Russian search engine, removed backlinks from their algorithm in an attempt to stop link spam.

Although it only took effect in the Moscow local search results and only for commercial queries, the change was short lived, as they brought links back only a year after.

Officials at Yandex stated that the change was “quite successful technologically”, but people still bought links and spammed the web regardless, so they decided to bring them back.

However, they brought them back with a substantial change: The links would not only be a positive ranking signal, but also a negative one, just like with Google’s Penguin update.

Could it be that the officials at Yandex are not willing to admit their failure? Maybe, who knows.

But if it was a success, couldn’t they just use spammy links as a negative signal only? Without using other links as a positive one?

Google has also tested rankings without backlinks in their algorithm.

youtube

However, they came to the conclusion that the results look much better when backlinks are a ranking signal.

Unfortunately, there are other issues with backlinks other than buying them.

Take a look at Negative SEO, for example. Google claims they can spot and ignore these types of links with Penguin 4.0, but is it really possible?

I’m not convinced. I’ve seen many cases in which websites have a drop in traffic immediately after an unnatural spike in backlinks clearly built by others.

You’d think that people engaging in Negative SEO will try to mimic link building… but that might also result in positive results for the target.

So, these attacks are usually very obvious. Pure spam. And Google still penalized the site.

So, it’s obvious they have a hard time dealing with all this link trading and spam.

This, combined with the constant effort from SEOs to obtain backlinks in unnatural ways, has pushed Google into wanting to back away from backlinks.

Backlinks are something you don’t have much control over. Yet, an SEO content strategy you can control.

With algorithm updates such as Penguin (which penalizes spammy links) and Panda (which penalizes spammy content), Google shows us it’s putting more weight on content.

youtube

But although search engines don’t like them, it seems like links are still going to stick around for a while, as Matt Cutts states in the video above.

However, with AI software that can also write content like humans getting better and better every day…

It’s hard to tell where this will go.

We might have the same issue as with links, with AI written websites constantly trying to compete to the top.

It would be a battle of the algorithms… one in which users won’t have a say.

We could bring the law into the equation, but that would spark a whole new lot of bigger and more complex issues.

However, I could see how in the future, the law would require you to specify that the content is written by a robot and not by a human.

While AI written content can be used for good, it can also be used for harm, from the relatively harmless blackhat SEOs to the highly harmful fake news outlets.

youtube

We’ve known for a long time that content was an important ranking factor, but now we also wanted to know why.

Google was moving away from backlinks (our tool and marketing strategy was mostly built around backlinks) so investing in this was in our own interest.

So let’s take a look at how exactly you can improve your rankings in Google without any link building, only by optimizing your content.

3. The Step-by-Step SEO Content Strategy to Rank #1 with No Links

OK. Now you know that it’s possible to rank without links in some situations and also why Google puts more and more weight on the content side of SEO.

You also know that backlinks are something you don’t have complete control over.

But how exactly do you rank without links? What step-by-step SEO content strategy can you apply to succeed?

Here are the steps you need to follow to have a chance on ranking without links.

3.1. Identify your Primary Audience and Their Pain Points

The first think you should do before writing anything is identifying your target audience.

You can start with what’s top of mind and build from there.

Who would be interested in your content and how can you make your content appeal to them?

Who are your clients? How old are they? Who are their friends? Where do they hang? How do they talk?

It’s easier to do this when you already have some clients or readers. Just look at your best clients and readers so far.

Who’s buying from you? Who’s reviewing on products? Who’s commenting on your blog and social media profiles?

If you’re just starting out and have no idea… then consider doing a market research.

I know it sounds complex, but you can start by simply creating a Google Form and sharing it on a couple of Facebook Groups, asking for help.

Sure, it’s not much, but it’s better than nothing.

Don’t focus on features with your product but on benefits. Don’t think in terms of needs but in terms of wants. Want is stronger than need.

And don’t seek answers from your research but feelings and emotions which you can then use to convince them to buy.

That’s what you want to ultimately address and that’s what will get you sales / engagement.

This step is probably more useful for products and commercial keywords and less for informational queries.

However, you can still address things such as the tone of voice for your audience.

You can also try to create a persona of your ideal customer / reader and try to write as if you were speaking directly to that person.

3.2. Perform an In-depth Keyword Research & Audit

The really important step when trying to optimize content is always keyword research.

What are you targeting now?

You can use the Google Search Console to easily determine the keyword you’re already ranking for.

Can you improve this existing content? Or should you target new keywords? These are questions you should ask yourself when putting together your SEO content strategy.

You can also use the CognitiveSEO Keyword Explorer to identify new topics you can target.

Once you’re done, map out these keywords into an excel file and sort them out by relevance and importance.

3.3. Make a List of the Keywords You Can Realistically Dominate

Now comes the more difficult part.

For which of those keywords can you realistically rank better?

After you’ve determined a list of keywords you’ll go after, you have to take a look at two things:

Content Performance and Domain Performance.

You can use the Ranking Analysis section of the Content Optimizer Tool.

While the Content Performance is something that we have control over and will improve in a future step, backlinks aren’t something we have that much control over.

So you want to see websites with as few referring domains as possible compared to your site (which you can check with the Site Explorer).

Of course, if you see a lower content score and/or word count, those are also good indicators that it will be easier to improve your content.

3.4. Analyze the Search Intent of Your Keywords

Search intent and user experience are very important. It’s the key metric in 2020 and onward.

If you don’t match the search intent well, users will have a bad experience on your website.

One of the best examples of search intent is transactional vs. informational.

Given the keyword “men’s running shoes”, eCommerce websites will tend to rank better.

However, for a keyword such as “best running shoes for men”, reviews and buyer’s guides will tend to rank better.

People are simply looking for different types of content on each of those keywords.

What you ultimately want to figure out is what your user is looking for when searching for a particular query and accessing your website.

For this blog post I know that there are two possible intents that are very closely related to one another: “can you rank with zero backlinks” & “how to do it”. My article simply appeals to those needs.

Knowing my audience in general, I know that I want to provide proof for the first group, and step-by-step tutorials for the second.

A good way of analyzing search intent is to look at what’s already ranking well.

You know they’re already doing something well, since they’re ranking.

If you can spot some gaps you can fill, such as questions asked in the comments that remain unanswered in the content itself, that’s even better.

Did you know?

The Content Optimizer & Keyword Tool from cognitiveSEO automatically classifies the search intent for you.

You just need to type in the keyword you are interested in optimizing for, and the tool does most of the job for you. The tool performs keyword research, it will tell you what is the user search intent, how popular is that keyword, how difficult it is to rank on it, and cream of the crop: what it takes to rank on that keyword, meaning, what are the exact keywords and links that boosted that page in the top of the search results.

3.5. Optimize Landing Pages / Per Keyword & Per Search Intent

On with the actual optimization. Don’t worry, it’s probably the easiest step.

First, consider that it’s always best to optimize for a single main keyword and a single search intent per page.

You can sometimes combine very similar keywords, but try as much as possible to stick to the same search intent.

Remember, if the search engine results are similar for these keywords, they probably have a similar search intent as well.

You should keep in mind that writing naturally is the best way to go.

Open the Content Assistant section and hit Start Optimizing.

It’s easier to work on something you’ve already written, but you can also start from scratch. However, you’ll have to write at least a title for the tool to work.

You can also import a URL if your content is already live.

Then, just click Check Score, the tool will analyze your content, tell you the score and recommend you some keyword ideas.

The idea is to add those keywords into your blog post. However, don’t just throw them in, or else you’ll end up with a Keyword Stuffing warning.

The keywords with dots on their left are the most important ones. You should be using them multiple times throughout your content.

The idea is to think of these keywords as subtopics or areas of interest that you should cover in your content.

If a keyword that the tool recommends doesn’t seem to fit or has grammar mistakes in it, don’t force it in.

You don’t need a 100% score. The tool will pinpoint how much you need to reach.

However, you can also try doing it in phases. First, optimize for a few points above the average, then update your content after a few weeks to the recommended score.

3.6. Write Content That Stands Out and Is Share Worthy

A great way to do this is to write content about complex questions that are very often asked in your industry, or case studies.

For example, this article about meta descriptions affect SEO or not has been linked to multiple times by other SEO blogs trying to make a quick point without getting into too much detail.

Also, make sure you have great Titles, Headlines and Descriptions.

Optimize your titles with keywords for SEO, but also keep them catchy for Social Media.

You can use the Open Graph property to set different titles for SEO and Social Media

Long content tends to perform better in organic search results.

When people search for something, you already know they are interested in it. That’s why SEM has such a high conversion rate and people spend time reading in depth articles.

You can look at the top ranking search results for your keyword in the Ranking Analysis section of our Content Optimizer Tool.

For this particular keyword, you can see that the top ranking content is between 2500 and 5000 words on average, although we can even see articles with only 1800 words in top 10.

Usually, it’s a good idea to go for at least the top 10 average plus a few hundred words, just to be sure. But it’s not really about quantity, it’s about quality.

However, you’ll probably not be able to fit all the depth required for this particular topic in just 200 words, at least compared to the competitors.

Nevertheless, take this content length thing with a grain of salt. While long form content performs better in search, shorter content might perform better on social media.

On Social Media, people are browsing different things and not really looking for a particular topic, so they might prefer things straight and to the point.

3.7. Launch & Share Your Content with Key Influencers

Once you’ve written and published your content, it’s time to promote it. This should be an important part of your SEO content strategy.

We made a study a few years ago on weather social signals influence rankings and it seems that a strong presence on social networks is correlated with better rankings.

Once you have your content live, it’s time to share it with the appropriate influencers.

Who are these influencers? Well, people who have a following which matches your target audience.

If they don’t hear about your content… they can’t share it or talk about it.

It’s also a good idea that the influencers themselves are interested or resonate in the topic and the way you present it.

One of the even more advanced SEO strategies is to plan your content ahead.

And by this I don’t mean having an editorial calendar, but actually targeting influencers beforehand, analyzing them and writing something that you already know they will be willing to share.

You’ll have to be persuasive and it’s also a good idea to build trust with them beforehand, by sharing their content and commenting on it or engaging with them.

I know it sounds evil and manipulative and you can be if your sole interest is only to get what you want, but being genuine and truly offering value in exchange usually works best.

I can recall the easiest response I have ever got. It was from a person named Reiner, which reminded me of a character in the anime show Attack on Titan.

Luckily, he did see the anime, so we how now had a personal connection and he was willing to give me a hand.

Another easy-to-implement technique is to ask for quotes from experts in your industry or take interviews.

Two examples are my article about SEO mistakes and my colleague Andreea’s article about SEO tips from 22 experts.

While we offered a lot of value, it’s true that we also used the guests’ reputation to leverage our content’s reach.

Of course, it’s not guaranteed to work, but most of them will gladly share the content on their social profiles or blogs.

To find influencers, you can use a tool such as Brand Mentions.

You can spy on your competitors and see which influencers have mentioned their content.

Then, engage with them and try to build relationships.

3.8. Monitor How Your Content Is Performing

You should make sure you’re able to track your progress if you want to know how successful your modifications were.

The Google Search Console is a great way of monitoring organic search traffic and positions.

However, the data there might sometimes be a little confusing and hard to filter.

You might also be interested in a third party tool, such as our Rank Tracker.

Moreover, not all traffic comes from Google, so you might also want a tool such as Google Analytics to monitor other traffic sources.

You can also try a tool such as Brand Mentions to monitor mentions about your brand and content on Social Media and the internet.

To precisely monitor the effectiveness of this OnPage SEO technique, make sure you don’t make any other major changes to the website.

3.9. Update Your Content Once in a While

Google loves fresh content. That’s why updating it from time to time should be part of your SEO content strategy.

Our method works really well for old content as well, so don’t just focus on new content.

We’ve improved many of our old blog posts using the Content Optimizer Tool.

In fact, we did it on a mass scale, with pretty good results.

Although some of the articles did lose a couple of rankings instead of gaining them, the overall process was a success, both in terms of rankings and traffic.

One of the articles we’ve constantly updated over the time is our Google Easter Eggs article, which is one of our top performing piece of content, although not directly related to SEO.

It’s a good idea to check the content’s score from time to time to see if it’s still in good shape.

The Content Performance score is relative. This means that as other articles improve, your score might drop over time.

However, even if the score is still really good, don’t try to trick Google by simply changing the date to make your content look fresh.

It’s best if you actually review the content and make a few improvements / modifications.

Conclusion

To get high ranks, the focus shouldn’t be on backlinks, but on a solid SEO content strategy. Get your content under the right people’s eyes, and you’ll see the links pouring in.

Applying this SEO content strategy can help you easily rank without going through the hassle of obtaining links. Attack the content creation and promotion from the right angle and backlinks will come naturally.

The truth is, you don’t need backlinks for every article. You just need a handful of articles that bring in the links to grow the authority of your domain.

Then you can use those links to pass that authority to all your articles, making it easier to rank everywhere.

So, let us know in the comments section below: Do you consider it’s possible to rank a page without any backlinks? Have you every managed to rank one on a decent search volume keyword? Do you know any content marketers at all able to rank websites on Google without backlinks? What other marketing strategy have you used to rank your content except building links?

The post The Unique SEO Content Strategy to Rank #1 with Zero Links appeared first on SEO Blog | cognitiveSEO Blog on SEO Tactics & Strategies.

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wjwilliams29 · 5 years ago