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

Introduction

Deep learning is one of the hottest fields in the past decade, with applications in industry and research. However, even though it’s easy to delve into the topic, many people are confused by the terminology and end up only implementing neural network models that do not match their expectations. In this article, I will go over what recurrent neural networks (RNNs) and word embeddings are and a step-by-step guide to building your first RNN model for text classification tasks and challenges.

                                                             Source: Photo by Markus Spiske on Unsplash

RNNs are one of the most important concepts in machine learning. They’re used across a wide range of problems, including text classification, language detection, translation tasks, author identification, and question answering, to name a few.

Let’s deep dive into RNNs and a step-by-step guide to building your first RNN model for text data.

Deep Learning for Text Data

Deep learning for natural-language processing is pattern recognition applied to text, words, and paragraphs in much similar way that computer vision is pattern recognition applied to pixels. In a true sense, deep learning models map the statistical structure of text data which is sufficient to solve many simple textual tasks and problems. Deep-learning models don’t take input as text like other models they only work with numeric tensors.

Three techniques are used to vectorize the text data:

Segment text into words and convert word into a vector

Segment text into characters and transform each character into a vector

Extract n-grams of words, and transform each n-gram into a vector.

If you want to build a text model, the first thing you need to do is convert the text into a vector. There are many ways one can convert text to vector depending on what models one uses along with time or resource utilization.

Keras has a built-in method for converting text into vectors(Word embedding layer) which we will use in this article.

Here is a visual depiction of the deep neural network model for NLP tasks

 Understanding Word-embeddings

A word embedding is a learned representation for text where words that have the same meaning and save similar representation

This approach to representing words and documents may be considered one of the key breakthroughs of deep learning on challenging NLP problems

Word embeddings are alternative to one-hot encoding along with dimensionality reduction.

One-hot word vectors — Sparse, High-dimensional and Hard-coded

Word embeddings — Dense, Lower-Dimensional and Learned from the data

Keras library has embeddings layer which does word representation of given text corpus

tf.keras.layers.Embedding( input_dim, output_dim, embeddings_initializer=’uniform’, embeddings_regularizer=None, activity_regularizer=None, embeddings_constraint=None, mask_zero=False, input_length=None, **kwargs)

Key Arguments:

input_dim — the size of vocabulary or length of the word index

output_dim — Output dimension of word representation

input_length — max input sequence length of the document

Here is the visual depiction of word embedding or also known as word2vec representation

Recurrent Neural Network (RNN): Demystified

A major difference between densely connected neural networks and recurrent neural network is that fully connected networks have no memory in units of each layer. At the same time, recurrent neural networks store the state of the previous timestep or sequence while assigning weights to the current input.

In RNNs, we process inputs word by word or eye saccade but eye saccade – while keeping memories of what came before in each cell. This gives a fluid representation of sequences and allows the neural networks to capture the context of the sequence rather than an absolute representation of words.

“Recurrent neural network processes sequences by iterating through the sequence elements and maintaining a state containing information relative to what it has seen so far. In effect, an RNN is a type of neural network that has an internal loop.”

Courtesy: 6.2 Understanding recurrent neural networks, deep learning using python by Chollet

Below is the visual depiction of how recurrent neural networks learn the context of words about the target word

Here is a simple depiction of RNN architecture with rolled and unrolled RNN.

Building your First RNN Model for Text Classification Tasks

Now we will look at the step-by-step guide to building your first RNN model for the text classification task of the news descriptions classification project

So let’s get started:

Step 1: load the dataset using pandas ‘read_json()’ method as the dataset is in json file format

df = pd.read_json('../input/news-category-dataset/News_Category_Dataset_v2.json', lines=True)

Python Code:



 the output of the above code block

Step 3: Clean the text data to move forward with tokenization and vectorization of text inputs before we feed vectorized text data to the RNN model.

# clean the text data using regex and data cleaning function def datacleaning(text): text = whitespace.sub(' ', text) text = user.sub('', text) text = re.sub(r"[[^()]*]","", text) text = re.sub("d+", "", text) text = re.sub(r'[^ws]','',text) text = text.lower() # removing stop-words text = [word for word in text.split() if word not in list(STOPWORDS)] # word lemmatization sentence = [] for word in text: lemmatizer = WordNetLemmatizer() sentence.append(lemmatizer.lemmatize(word,'v')) return ' '.join(sentence)

Step 4: Tokenization and vectorization of text data to create a word index of the sentences and split the dataset into train and test datasets.

# one hot encoding using keras tokenizer and pad sequencing X = final_df2['length_of_news'] encoder = LabelEncoder() y = encoder.fit_transform(final_df2['category']) print("shape of input data: ", X.shape) print("shape of target variable: ", y.shape)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)

tokenizer = Tokenizer(num_words=100000, oov_token='') tokenizer.fit_on_texts(X_train) # build the word index # padding X_train text input data train_seq = tokenizer.texts_to_sequences(X_train) # converts strinfs into integer lists # padding X_test text input data test_seq = tokenizer.texts_to_sequences(X_test) word_index = tokenizer.word_index max_words = 150000 # total number of words to consider in embedding layer total_words = len(word_index) maxlen = 130 # max length of sequence y_train = to_categorical(y_train, num_classes=41) y_test = to_categorical(y_test, num_classes=41) print("Length of word index:", total_words) shape of input data: (184853,) shape of target variable: (184853,) Length of word index: 174991

Step 5: Now as we have ‘train’ and ‘test’ data prepared, we can build an RNN model using the ‘Embedding()’ and ‘SimpleRNN()’ layers of Kera’s library.

# basline model using embedding layers and simpleRNN model = Sequential() model.add(Embedding(total_words, 70, input_length=maxlen)) model.add(Bidirectional(SimpleRNN(64, dropout=0.1, recurrent_dropout=0.20, activation='tanh', return_sequences=True))) model.add(Bidirectional(SimpleRNN(64, dropout=0.1, recurrent_dropout=0.30, activation='tanh', return_sequences=True))) model.add(SimpleRNN(32, activation='tanh')) model.add(Dropout(0.2)) model.add(Dense(41, activation='softmax')) model.summary() Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding (Embedding) (None, 130, 70) 12249370 _________________________________________________________________ bidirectional (Bidirectional (None, 130, 128) 17280 _________________________________________________________________ bidirectional_1 (Bidirection (None, 130, 128) 24704 _________________________________________________________________ simple_rnn_2 (SimpleRNN) (None, 32) 5152 _________________________________________________________________ dropout (Dropout) (None, 32) 0 _________________________________________________________________ dense (Dense) (None, 41) 1353 ================================================================= Total params: 12,297,859 Trainable params: 12,297,859 Non-trainable params: 0 _________________________________________________________________

Step 6: Compile the model with the ‘rmsprop’ optimizer and ‘accuracy’ as validation metrics followed by fitting the model to the ‘X_train’ and ‘y_train’ data. you can evaluate the model using the ‘model.evaluate()’ method on test data. Congrats! you have just built your first model using word embedding and RNN layers.

loss=’categorical_crossentropy’, metrics=[‘accuracy’] ) # SETUP A EARLY STOPPING CALL and model check point API earlystopping = keras.callbacks.EarlyStopping(monitor=’accuracy’, patience=5, verbose=1, mode=’min’ ) checkpointer = ModelCheckpoint(filepath=’bestvalue’,moniter=’val_loss’, verbose=0, save_best_only=True) callback_list = [checkpointer, earlystopping]

# fit model to the data batch_size=128, epochs=15, validation_split=0.2 ) # evalute the model print("test loss and accuracy:", test_loss, test_acc) Conclusion

Key takeaways:

Word embeddings are representations of word tokens that eventually can be trained along with a model to find optimal weights that fit the task at hand.

Recurrent neural networks are widely used in text data classification tasks and can be implemented using the Keras library of python.

Using a step-by-step guide for building an RNN model to classify text data at hand, you build the model for any text classification problem.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

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Building Machine Learning Model Is Fun Using Orange

Introduction

With growing need of data science managers, we need tools which take out difficulty from doing data science and make it fun. Not everyone is willing to learn coding, even though they would want to learn / apply data science. This is where GUI based tools can come in handy.

Today, I will introduce you to another GUI based tool – Orange. This tool is great for beginners who wish to visualize patterns and understand their data without really knowing how to code.

In my previous article, I presented you with another GUI based tool KNIME. If you do not want to learn to code but still apply data science, you can try out any of these tools.

By the end of this tutorial, you’ll be able to predict which person out of a certain set of people is eligible for a loan with Orange!

Table of Contents:

Why Orange?

Setting up your System:

Creating your first Workflow

Familiarizing yourself with the basics

Problem Statement

Importing the data files

Understanding the data

How do you clean your data?

Training your first model

1. Why Orange?

Orange is a platform built for mining and analysis on a GUI based workflow. This signifies that you do not have to know how to code to be able to work using Orange and mine data, crunch numbers and derive insights.

You can perform tasks ranging from basic visuals to data manipulations, transformations, and data mining. It consolidates all the functions of the entire process into a single workflow.

The best part and the differentiator about Orange is that it has some wonderful visuals. You can try silhouettes, heat-maps, geo-maps and all sorts of visualizations available.

2. Setting up your System

Orange comes built-in with the Anaconda tool if you’ve previously installed it. If not, follow these steps to download Orange.

Step 2: Install the platform and set the working directory for Orange to store its files.

This is what the start-up page of Orange looks like. You have options that allow you to create new projects, open recent ones or view examples and get started.

Before we delve into how Orange works, let’s define a few key terms to help us in our understanding:

A widget is the basic processing point of any data manipulation. It can do a number of actions based on what you choose in your widget selector on the left of the screen.

A workflow is the sequence of steps or actions that you take in your platform to accomplish a particular task.

You can also go to “Example Workflows” on your start-up screen to check out more workflows once you have created your first one.

3. Creating Your First Workflow

This is your blank Workflow on Orange. Now, you’re ready to explore and solve any problem by dragging any widget from the widget menu to your workflow.

4. Familiarising yourself with the basics

Orange is a platform that can help us solve most problems in Data Science today. Topics that range from the most basic visualizations to training models. You can even evaluate and perform unsupervised learning on datasets:

4.1 Problem

The problem we’re looking to solve in this tutorial is the practice problem Loan Prediction that can be accessed via this link on Datahack.

4.2 Importing the data files

We begin with the first and the necessary step to understand our data and make predictions: importing our data

Step 3: Once you can see the structure of your dataset using the widget, go back by closing this menu.

Neat! Isn’t it?

Let’s now visualize some columns to find interesting patterns in our data.

4.3 Understanding our Data

The plot I’ve explored is a Gender by Income plot, with the colors set to the education levels. As we can see in males, the higher income group naturally belongs to the Graduates!

Although in females, we see that a lot of the graduate females are earning low or almost nothing at all. Any specific reason? Let’s find out using the scatterplot.

One possible reason I found was marriage. A huge number graduates who were married were found to be in lower income groups; this may be due to family responsibilities or added efforts. Makes perfect sense, right?

4.3.2 Distribution

What we see is a very interesting distribution. We have in our dataset, more number of married males than females.

4.3.3 Sieve diagram

Let’s visualize using a sieve diagram.

This plot divides the sections of distribution into 4 bins. The sections can be investigated by hovering the mouse over it.

Let’s now look at how to clean our data to start building our model.

5. How do you clean your data?

Here for cleaning purpose, we will impute missing values. Imputation is a very important step in understanding and making the best use of our data.

Here, I have selected the default method to be Average for numerical values and Most Frequent for text based values (categorical).

You can select from a variety of imputations like:

Distinct Value

Random Values

Remove the rows with missing values

Model-Based

6. Training your First Model

Beginning with the basics, we will first train a linear model encompassing all the features just to understand how to select and build models.

Step 1: First, we need to set a target variable to apply Logistic Regression on it.

Step 4: Once we have set our target variable, find the clean data from the “Impute” widget as follows and place the “Logistic Regression” widget.

Ridge Regression:

Performs L2 regularization, i.e. adds penalty equivalent to square of the magnitude of coefficients

Minimization objective = LS Obj + α * (sum of square of coefficients)

Lasso Regression:

Performs L1 regularization, i.e. adds penalty equivalent to absolute value of the magnitude of coefficients

Minimization objective = LS Obj + α * (sum of absolute value of coefficients)

I have chosen Ridge for my analysis, you are free to choose between the two.

Step 8: To visualize the results better, drag and drop from the “Test and Score” widget to fin d “Confusion Matrix”.

This way, you can test out different models and see how accurately they perform.

Let’s try to evaluate, how a Random Forest would do? Change the modeling method to Random Forest and look at the confusion matrix.

Looks decent, but the Logistic Regression performed better.

We can try again with a Support Vector Machine.

Better than the Random Forest, but still not as good as the Logistic Regression model.

Sometimes the simpler methods are the better ones, isn’t it?

This is how your final workflow would look after you are done with the complete process.

For people who wish to work in groups, you can also export your workflows and send it to friends who can work alongside you!

The resulting file is of the (.ows) extension and can be opened in any other Orange setup.

End Notes

Orange is a platform that can be used for almost any kind of analysis but most importantly, for beautiful and easy visuals. In this article, we explored how to visualize a dataset. Predictive modeling was undertaken as well, using a logistic regression predictor, SVM, and a random forest predictor to find loan statuses for each person accordingly.

Hope this tutorial has helped you figure out aspects of the problem that you might not have understood or missed out on before. It is very important to understand the data science pipeline and the steps we take to train a model, and this should surely help you build better predictive models soon!

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Building A Total Cost Of Ownership Model For Outdoor Digital Signage

It’s readily apparent that using digital screens to inform, educate and brand is valuable, but building a solid total cost of ownership or ROI business case for using digital signage involves taking a much deeper look into what the technology mix will deliver in settings like QSR and retail. Most of the information available about the financial return on digital signage networks has focused solely on the economics of marketing impacts — such as sales lift or recalls of brand messaging. There’s very little out there that takes an analytical deep dive into the real investment versus the benefits of a signage project.

Identifying the Right Criteria

A recent Forrester Research report titled “Build a Business Case for Digital Signage” lays out a case and methodology for what it calls the “Total Economic Impact (TEI) of Digital Signage.” The report was designed to provide infrastructure and operations professionals with a toolset they can use to build a valid business case before making the investment in a screen network.

The report notes that there are four critical business scenarios in which digital signage drives value:

Brand perception and brand storytelling

Improved information distribution to customers of all types

Explicit or subtle interactivity

Effective workplace communications

The TEI model uses three categories of analysis: costs, benefits and flexibility. By focusing on these respective categories, businesses can effectively combine hardware, software, services and resources; examine raised sales or productivity rates; and monetize the value of introducing technology for future use cases.

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These TEI elements then get run through a filter of risk — tempering the initial benefits like sales against uncertain real-world factors like economic change and technology problems.

Setting the Model

Forrester, in its report, worked up a representative scenario for how a TEI, or ROI, model could be developed. They settled on a large convenience store chain that sells a lot of what’s termed “high-velocity” food and beverage items — soft drinks and snacks that sell in big numbers every day. The stores in this dreamed-up chain use screens both inside and outside the store, and the technology is there to take over from printed materials.

The TEI model broke costs down to four elements and thought in terms of total costs over the span of three years:

Hardware like screens, media players, mounts and any cabling and distribution gear.

Software license fees, maintenance fees and upfront costs.

Installation costs, from putting the equipment in place to the initial costs for creative and other content running on network launch day.

Ongoing content costs for designing fresh creative for new promotions and content subscriptions, and the resource costs (internally or subcontracted) to manage the flow, scheduling and distribution of content.

The benefits, meanwhile, boiled down to how the screens might improve sales performance in the store. While there are many industry stories talking up double- and even triple-digit percentage boosts in sales for items promoted on digital screens, those cases tend to be outliers.

Setting Expectations

Putting those benefits against the costs shows a clear and healthy return, but halve the expectations using that filter of risk. What if, for example, local economic conditions weaken sales, the sightlines to the screens are obscured by something else put up in stores, or the creative content is ineffective?

The study outlines a disciplined framework and a strategy that almost any company deploying digital signage could use to build a TCO or ROI model. It forces the people pushing the project to fully investigate the obvious capital expenditures, and the less obvious ongoing operating expenditures to manage networks and keep content fresh and relevant. It also makes a good case for working with conservative expectations of the benefits; few CFOs are happy with projects that are oversold and then under-deliver on the benefits.

Companies should build their model not on what they hope might happen, but what’s realistic. In the hyper-competitive retail business, even a 1 percent bump in sales can be a big deal. If businesses work to develop a plan to accurately calculate the total cost of ownership of a digital signage solution, they’ll be able to fully reap the benefits of this exciting technology.

Samsung’s outdoor digital signage solutions and integration capabilities can help businesses bring in new business and increase upsells.

Evaluate Your Model – Metrics For Image Classification And Detection

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

Deep learning techniques like image classification, segmentation, object detection are used very commonly. Choosing the right evaluation metrics is very crucial to decide which model to use, how to tune the hyperparameters, the need for regularization techniques, and so on. I have included the metrics I have used to date.

Classification Metrics

Let’s first consider Classification metrics for image classification. Image classification problems can be binary or multi-classification. Example for binary classification includes detection of cancer, cat/dog, etc. Some examples for Multi-label classification include MNIST, CIFAR, and so on.

The first metric that you think of usually is Accuracy. It’s a simple metric that calculates the ratio of Correct predictions to wrong predictions. But is it always valid?

Let’s take a case of an imbalanced dataset of cancer patients. Here, the majority of the data points will belong to the negative class and very few in the positive class. So, just by classifying all the patients as “Negative”, the model would have achieved great accuracy!

Confusion Matrix

The next step usually is to plot the confusion Matrix.  It has 4 categories: True positives, True negatives, false positives, and false negatives. Using this matrix, we can calculate various useful metrics!

Accuracy =  (TP + TN) / ( TP + TN + FP + FN)

You can find this using just a few lines of code with sklearn metrics library.

from sklearn.metrics import confusion_matrix, accuracy_score # Threshold can be optimized for each problem threshold=0.5 tn, fp, fn, tp = confusion_matrix(labels_list, preds_list).ravel() accuracy = accuracy_score(labels_list, preds_list

You would have probably heard terms like recall or sensitivity. They are the same!

Sensitivity/ True Positive Rate:

TPR/Sensitivity denotes the percentage/fraction of the positive class that was correctly predicted and classified! It’s also called Recall.

Sensitivity = True Positives/ (True Positives + True Negatives)

An example: What percent of actual cancer-infected patients were detected by the model?

Specificity / True Negative Rate:

While it’s essential to correctly predict positive class, imagine what would happen if a cancer-negative patient has been told incorrectly that he’s in danger! (False positive)

Specificity is a metric to calculate what portion of the negative class has been correctly predicted and classified.

Specificity = True Negatives/ (False Positives + True Negatives)

This is also called as True Negative Rate (TPR)

Specificity and Sensitivity are the most commonly used metrics. But, we need to understand FPR also to get ROC.

False Positive Rate

This calculates how many negative class samples were incorrectly classified as positive.

FPR = 1 – Specificity

For a good classification model, what is that we desire?

A higher TPR and lower FPR!

Another useful method is to get the AUC ROC curve for your confusion matrix. Let’s look into it!

AUC ROC Curve

ROC stands for Receiver Operator Characteristic (ROC). AUC just means Area under the curve. Here, we plot the True Positive Rate against False Positive Rate for various thresholds.

Generally, if a prediction has a value above 0.5, we classify it into positive class, else, negative class. Here, this deciding boundary 0.5 is denoted as the threshold. It’s not always necessary to use 0.5 as the threshold, sometimes other values might give the best results. To find out this, we plot TPR  vs  FPR against a range of threshold values. Usually, the thresholds are varied from 0.1, 0.2, 0.3, 0.4, and so on to 1.

Image: Source

For a particular threshold, if you want to calculate a ROC AUC Score, sklearn provides a function. You can use it as shown.

from sklearn.metrics import roc_auc_score   roc_auc = roc_auc_score(labels, predictions)

The top left corner of the graph is where you should look for your optimal threshold!

If you want to plot the ROC AUC graph, you can use blow snippets

fpr, tpr, thresholds = metrics.roc_curve(y, scores, pos_label=2) import matplotlib.pyplot as plt plt.plot(fpr,tpr) plt.show()

Here, the fpr and tpr is given by the function will be a list/array containing the respective values for each threshold value in the list.

You can also plot sensitivity and specificity against thresholds to get more information.

Object detection Metrics

Object detection has many applications including face detection, Lane detection in Auto-driver systems, and so on. Here, we need to use a different set of metrics to evaluate. The most popular one is IOU. Let’s begin!

IOU (Intersection over Union)

So in object detecting or segmentation problems, the ground truth labels are masks of a portion or a bounding box where the object is present. The IOU metric finds the difference between the prediction bounding box and the ground truth bounding box.

IOU = Area of Intersection of the two bounding boxes / Area of Union

Source: Image

IOU will be a value between 0-1. For perfectly overlapping boxes, it will be 1 and 0 for non-overlapping prediction. Generally, IOU should be above 0.5 for a decent object detection model.

Mean Average Precision (mAP)

Using the IOU, precision, and recall can be calculated.

How?

You have to set an IOU Threshold value. For example, let’s say I keep the IOU threshold as 0.5. Then for a prediction of IOU as 0.8, I can classify it as True positive. If it’s 0.4 (less than 0.5) then it is a False Positive.  Also note that if we change the threshold to 0.4, then this prediction would classify as True Positive. So, varying thresholds can give different metrics.

Next, Average Precision (AP) is obtained by finding the area under the precision-recall curve. The mAP for object detection is the average of the AP calculated for all the classes to determine the accuracy of a set of object detections from a model when compared to ground-truth object annotations of a dataset.

The mean Average Precision is calculated by taking the mean of AP over all classes and/or overall IoU thresholds.

Many object detection algorithms including Faster R-CNN, MobileNet use this metric. This metric provides numerical value making it easier to compare with other models.

Thanks for reading! You can connect with me at [email protected]

The media shown in this article on Metrics for Image Classification are not owned by Analytics Vidhya and are used at the Author’s discretion.

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Understanding Pipes And Redirection For The Linux Command Line

On all Unix-like operating systems, like Linux and FreeBSD, the output from a command line program automatically goes to a place known as standard output (stdout). By default, standard out is the screen (the console) but that can be changed using pipes and redirection. Likewise the keyboard is considered the standard input (stdin) and as with standard out, it can be changed.

Pipes

Pipes allow you to funnel the output from one command into another where it will be used as the input. In other words, the standard output from one program becomes the standard input for another.

The “more” command takes the standard input and paginates it on the standard output (the screen). This means that if a command displays more information than can be shown on one screen, the “more” program will pause after the first screen full (page) and wait for the user to press SPACE to see the next page or RETURN to see the next line.

Here is an example which will list all the files, with details (-la) in the /dev directory and pipe the output to more. The /dev directory should have dozens of files and hence ensure that more needs to paginate.

Notice the --More-- prompt at the bottom of the screen. Press SPACE to see the next page and keep pressing SPACE until the output is finished.

Here is another pipe example, this time using the “wc” (word count) tool.

The In Depth Look at Linux’s Archiving and Compression Commands tutorial has an example using tar and 7-Zip together:

Redirection

The cat command can be used to create a file using redirection, for example:

Conclusion

Many of Linux command line programs are designed to work with redirection and pipes, try experimenting with them and see how they interact. For example the output of the ps command, which lists the current processes, can be piped into grep. See if you can work out how to list the processes owned by root.

Gary Sims

Gary has been a technical writer, author and blogger since 2003. He is an expert in open source systems (including Linux), system administration, system security and networking protocols. He also knows several programming languages, as he was previously a software engineer for 10 years. He has a Bachelor of Science in business information systems from a UK University.

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Understanding Online Dating Apps And Playing With Their Algorithms

One of the highest ranged searches on the internet is ‘best dating apps for relationships’. To many people that can be sad but today, online dating has done away with the stigma around it.

Online dating apps & their algorithms

Studies show that 3 out of 5 people in the USA are not opposed to meeting someone online. But, how much do we know about this online dating matchmaking process?

Online dating websites all have their apps now, and even big sharks like OkCupid and Tinder all are powered by machine learning AI algorithms that match people around their geography.

So, the chances are if you have met your soul mate online, you have to thank algorithms rather than just Cupid, the God of love.

Even though Tinder, OkCupid, eHarmony have managed to keep the secret behind their matchmaking process a secret, researchers at Cornell University have cracked that can wide open.

These days most online dating apps use their AI algorithm to match new users on the following factors initially –

The agreeableness level

Closeness preference

Romantic passion range

Extroversion or Introversion level

Importance of spirituality

The level of optimism or happiness

In addition to these criteria, the algorithm then adds on the new user’s location, height, religion information to draw matches for users.

So, you can see that the algorithm polls in all this information and draws in matches that are closest to the new user’s preference. Hence, you can thank math for that lovely date you had last Saturday.

How do Dating Apps eliminate guesswork

Simply put, the dating app algorithm learns from user data. The AI is designed to not just gather user data but learn from your preferences and actions. That helps the AI to push the matched profiles closest to your preferences.

For example, if you don’t like people with piercings, then the algorithm will quit showing your profiles who have piercings.

This means that all the guesswork in the dating game is out of the window for you. You would not have met all these people had you tried dating offline. Dating app algorithms bring you closer to likeminded individuals and also learning your actions all the way.

Can you play with the Dating App Algorithm

For this, I have to go back to an episode from Parks and Recreation. Tom Haverford, a character in that comedy sit-com opened 26 different Tinder profiles. He did that to ensure his chances of meeting a woman of almost all preferences was higher.

So, yes tacking such preference alterations through multiple profiles can be a big issue for online dating apps like Tinder, Bumble, eHarmony, etc. There are some smart apps that detect an unused profile long enough then they also call for the deletion of it.

To add to it, most dating app companies use computer-human intuitions to detect misbehavior. For example, they will read through your activity and see if you have been opening multiple profiles. It can also detect whether a user is pretending to be in a foreign country. You can also get banned for explicit language and behavior.

Do not swipe right on every profile. Rather, keep it at a healthy 30% to 50%.

Work on your profile. Use a catchy caption — Link Instagram, etc.

Be genuine — no fake pictures or Photoshop.

Try replying to every message. This matters. So if you believe you would be inactive for a while, better deactivate the app temporarily.

Be yourself. Don’t try to spoof anything.

Remember, just like Facebook and Google, the algorithms of dating apps expect their users to use their applications genuinely. Don’t try shortcuts.

Are Online Dating Apps worth it?

Yes! Online dating boils down to strict math but, as true as that statement is; so is the fact that you get to meet different people. These dating app algorithms are designed to bring you closer to someone who shares your beliefs and your ideas.

Making the user in the centerfold of the whole math, you can ensure that you would at the very least end up meeting someone interesting.

EndNote

There is no doubt that the future of online dating is mobile-based for the industry. With more and more people getting on the online dating wagon, users are changing in how they use the internet to find their mate.

So definitely, knowing the algorithm and its preferential math system is going to help you refine your activities in a way, so you are matched with someone closer to your tastes.

Read next: Mistakes to avoid in Online Dating.

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