10 Tips to Integrate Machine Learning And AI In Dating Apps

Dating at times becomes very tough when it gets handled by a single person. The specific apps for dating makes it even more rough. The different algorithms which are used by dating apps are at large kept much private by the apps and app development companies. Dating algorithms can get created easily by using AI and Machine Learning. It becomes even more specific which helps in utilizing the least supervised machine learning in the form of clustering.

Along with this we can make improvements in the process of dating profile matching. We can do it by pairing most of the users together by making the use of machine learning.

All the dating companies and apps such as Tinder or Hinge are already reaping the benefits of such techniques. Before such dating app development one must possess a little knowledge about the profile matching process. One has to know even some unsupervised machine learning concepts.

If the app owners are not interested in implementing machine learning in the apps then it becomes the task of the developer to improve the complete procedure of match-making.

The basic idea of machine learning for dating apps and even the algorithms are as follows:

Registering Machine Learning To Find Love

The article completely deals with the application of AI and dating apps. It is laid out in the outline of the overall project.

The complete concept and its application is quite simple. Basically there are two types of algorithms which are used. It can be K-Means clustering or Hierarchical Agglomerative Clustering. These help in clustering the dating profiles with all the other profiles.

Coding in Python Language becomes much more easy to begin creating the machine learning language.

1. Getting the Dating profile Data

Dating profiles which are available publicly are rare and sometimes even it becomes impossible to come by, it becomes understandable due to all the security and privacy risks.

The fake dating profiles can be put to test in machine learning algorithms. The process of getting all the fake dating profiles. The complete procedure of gathering all the fake dating profiles is also outlined here.

2. Generating Fake Dating Profiles for Data Science

Forging all the dating profiles for data analysis can be done with the help of Web Scraping. Once we have been forging the dating profiles so that the practice of making the use of natural language processing can help in exploring and analyzing the data.

With the data which gets collected and further analyzed, after this the developer can focus on the next exciting part – Clustering.

3. Preparing the Profile Data

To start with, We must first import all the needful libraries which are needed in order for the clustering algorithm so that it can run properly. The Pandas Data-frame will get created when the fake dating profiles get forged. The next step towards clustering algorithm is:

4. Scaling the Data

The next and crucial step which will help the performance of clustering algorithm is scaling the dating categories. This will potentially decrease the time it takes to fit and even transform the clustering algorithm datasets.

5. Vectorizing the Bios

We need to vectorize the bios that we all have from such fake profiles.  The need to create one more Dataframe which includes the vectorized bios and which drops the original bio column.

With vectorization we will just be integrating two different approaches which helps us to observe all the significant effects on the clustering algorithm. The two complete vectorization approaches were : Count Vectorization and TFIDF Vectorization.

To get the best vectorization method which matches your horizon can only be known to you once you experiment with both the approaches.

Countvectorizer() or Tfidvectorizer() is the option which can be utilised for vectorizing the dating profile bios. The Bioshave which gets vectorized and even placed into the Dataframe, we need to sequence it with the scaled dating categories which helps in creating the new dataframe with all such features which are needed.

Depending upon the final DF, We will have to reduce the dimensionality of the dataset with the help of Principal Component Analysis (PCA)

6.PCA on the DataFrame

In order to diminish the large feature set, we will have to integrate Principal Component Analysis(PCA).

The technique will also diminish the dimensionality of the dataset but it will still retain much of the variability or even the statistical information which is of high value. The last DF must get fitted and transformed rather than plotting the variance and even the number of features.

The plot completely visualizes how many features are to be integrated in the account for variance. After running the code, all the number of features that account for 95% of the variance is 74. With such a number in the mind, it can be applicable to the PCA function which helps in reducing the number of Principal Components and its features in the last DF to 74 from 117.

Such features can now be utilised instead of the original DF to fit to all the clustering algorithms.

7. Clustering the Dating profiles

With our data scaled, vectorized, and PCA. We can easily begin with the cluster of dating profiles. In order to cluster the profiles together. We need to first identify the optimum number of clusters which are being created.

8. Evaluation Metrics for Clustering

The optimum numbers of clusters which will get determined depending on the specific evaluation metrics and which will quantify the performance of clustering algorithms.

There are no definite set number of clusters which are to be created and we will be using the couple of all the different evaluation metrics which helps in determining the optimum number of clusters. Such metrics are the Silhouette Coefficient and the Davies-Bouldin Score.

These metrics have their own pros and cons even the preferences which helps them in choosing either one is highly subjective and one can choose the metric of his choice.

9. Finding the Right Number of Clusters

The code which is included in the clusters varies which makes the complete clustering algorithm.

The user also gets an option to make a choice between the clustering algorithms which are in the loop: Hierarchical Agglomerative Clustering and KMeans Clustering. There is also an option to comment out with the chosen clustering algorithm.

10. Evaluating the Clusters

Further to evaluate the clustering algorithms there is a different evaluation function which runs on the list of our scores.

With such functions we can easily evaluate the list of scores which are acquired and plot out the different values which helps in determining the optimum number of all the clusters.

Depending upon the charts and evaluation metrics, the optimum number of all the clusters which seems around 12 and for the final run of the algorithm.

The last Sentence

By making the use of an unsupervised machine learning technique which has Hierarchical Agglomerative Clustering and we can auspiciously cluster together over 5000 different profiles which are related to dating.

Feeling free to change and even experiment with all the codes which can be seen with the potentiality and can improve the overall result.