Marco zamarco online dating

Marco zamarco online dating

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Sign in. Mar 8, · 7 min read. D ating is rough for the single person. Dating apps can be even rougher. The algorithms dating apps use are largely kept private by the various companies that use them. Today, we will try to shed some light on these algorithms by building a dating algorithm using AI and Machine Learning.

More specifically, we will be utilizing unsupervised machine learning in the form of clustering. Hopefully, we could improve the proc e ss of dating profile matching by pairing users together by using machine learning. If dating companies such as Tinder or Hinge already take advantage of these techniques, then we will at least learn marco zamarco online dating little bit more about their profile matching process and some unsupervised machine learning concepts.

However, if they do not use machine learning, then maybe we could surely improve the matchmaking process ourselves, marco zamarco online dating. The idea behind the use of machine learning for dating apps and algorithms has been explored and detailed in the previous article below:, marco zamarco online dating.

This article dealt with the application of AI and dating apps. It laid out the outline of the project, which we will be finalizing here in this article, marco zamarco online dating. The overall concept and application is simple. We will be using K-Means Clustering or Hierarchical Agglomerative Clustering to cluster the dating profiles with one another. By doing so, we hope to provide these hypothetical users with more matches like themselves instead of profiles unlike their own.

Now that we have an outline to begin creating this machine learning dating algorithm, we can begin coding it all out in Python! Since publicly available dating profiles are rare or impossible to come by, which is understandable due to security and privacy risks, we will have to resort to fake dating profiles to test out our machine learning algorithm.

The process of gathering these fake dating profiles is outlined in the article below:. Once we have our forged dating profiles, we can begin the practice of using Natural Marco zamarco online dating Processing NLP to explore and analyze our data, specifically the user bios.

We have another article which details this entire procedure:. With the data gathered and analyzed, we will be able to move on with the next exciting part of the project — Clustering! To begin, we must first import all the necessary libraries we will need in order for this clustering marco zamarco online dating to run properly. We will also load in the Pandas DataFrame, which we created when we forged the fake dating profiles.

With our dataset good to go, we can begin the next step for our clustering algorithm. This will potentially decrease the time it takes to fit and transform our clustering algorithm to the dataset.

Next, we will have to vectorize the bios we have from the fake profiles. With vectorization we will implementing two different approaches to see if they have significant effect on the clustering algorithm.

Those two vectorization approaches are: Count Vectorization and TFIDF Vectorization. Marco zamarco online dating will be experimenting with both approaches to find the optimum vectorization method. Here we have the option of either using CountVectorizer or TfidfVectorizer marco zamarco online dating vectorizing the dating profile bios. When the Bios have been vectorized and placed into their own DataFrame, marco zamarco online dating, we will concatenate them with the scaled dating categories to create a new DataFrame with all the features we need.

Based on this final DF, we have more than features. Because of this, we will have to reduce the dimensionality of our dataset by using Principal Component Analysis PCA. In order marco zamarco online dating us to reduce this large feature set, we will have to implement Principal Component Analysis PCA. This technique will reduce the dimensionality of our dataset but still retain much of the variability or valuable statistical information.

What we are doing here is fitting and transforming our last DF, then plotting the variance and the number of features. This plot will visually tell us how marco zamarco online dating features account for the variance.

With that number in mind, we can apply it to our PCA function to reduce the number of Principal Components or Features in our last DF to 74 from These features will now be used instead of the original DF to fit to our clustering algorithm.

In order to cluster our profiles together, we must first find the optimum number of clusters to create. The optimum number of clusters will be determined based marco zamarco online dating specific evaluation metrics which will quantify the performance of the clustering marco zamarco online dating. Since there is no definite set number of clusters to create, we will be using a couple of different evaluation metrics to determine the optimum number of clusters.

These metrics are the Silhouette Coefficient and the Davies-Bouldin Score. These metrics each have their own advantages and disadvantages. The choice to use either one is purely subjective and you are free to use another metric if you choose. Below, we will be running some code that will run our clustering algorithm with differing amounts of clusters, marco zamarco online dating.

By running this code, we will be going through several steps:. Also, there is an option to run both types of clustering algorithms in the loop: Hierarchical Agglomerative Clustering and KMeans Clustering. There is an option to uncomment out the desired clustering algorithm. To evaluate the clustering algorithms, we will create an evaluation function to run on our list of scores.

With this function we can evaluate the list of scores acquired and plot out the values to determine the optimum number of clusters. Based on both of these charts and evaluation metrics, the optimum number of clusters seem to be For our final run of the algorithm, we will be using:, marco zamarco online dating.

With these parameters or functions, we will be clustering our dating profiles marco zamarco online dating assigning each profile a number to determine which cluster they belong to. With everything ready, we can finally discover the clustering assignments for each dating profile. Once we have run the code, we can create a new column containing the cluster marco zamarco online dating. This new DataFrame now shows the assignments for each dating profile.

We have successfully clustered our dating profiles! We can now filter our selection in the DataFrame by selecting only specific Cluster numbers. By utilizing an unsupervised machine learning technique such as Hierarchical Agglomerative Clusteringwe were successfully able to cluster marco zamarco online dating over 5, different dating profiles.

Feel free to change and experiment with the code to see if you could potentially improve the overall result. Hopefully, by the end of this article, you were able to learn more about NLP and unsupervised machine learning.

There are other potential improvements to be made to this project such as implementing a way to include new user input data to see who they might potentially match or cluster with. Perhaps create a dashboard to fully realize this clustering algorithm as a prototype dating app. Link to the Web Application. Connect with me: linkedin. Your home for data science. A Medium publication sharing concepts, ideas and codes. Get started. Open in app. Sign in Get started. Editors' Picks Features Deep Dives Grow Contribute.

Get started Open in app. How to Use Machine Learning and AI to Make a Dating App. Utilize Unsupervised Learning as a Dating Algorithm. Marco Santos.

The idea behind the use of machine learning for dating apps and algorithms has been explored and detailed in the previous article below: Applying Machine Learning to Find Love The First Steps in Developing an AI Matchmaker.

Getting the Dating Profile Data Since publicly available dating profiles are rare or impossible to come by, which is understandable due to security and privacy risks, we will have to resort to fake dating profiles to test out our machine learning algorithm. The process of gathering these fake dating profiles marco zamarco online dating outlined in the article below: Generating Fake Dating Profiles for Data Science Forging Dating Profiles for Data Analysis by Webscraping.

Using NLP Machine Learning on Dating Profiles Applying Natural Language Processing for User Bios. Preparing the Profile Data To begin, we must first import all the necessary libraries we will need in order for this clustering algorithm to run properly. Evaluation Metrics for Clustering The optimum number of clusters will be determined based on specific evaluation metrics which will quantify the performance of the marco zamarco online dating algorithms.

Finding the Right Number of Clusters Below, we will be running some code that will run our clustering algorithm with differing amounts of clusters. Running the Final Clustering Algorithm With everything ready, we can finally discover the clustering assignments for each dating profile. Closing Thoughts By utilizing an unsupervised machine learning technique such as Hierarchical Agglomerative Clusteringwe were successfully able to cluster together over 5, marco zamarco online dating, different dating profiles.

Check out the following article to see how we created a web application for this dating app: How to Use Streamlit to Create Web Applications Using Streamlit to Display a Data Science Based Dating Application Utilizing Machine Learning. Generating Fake Dating Profiles for Data Science Forging Dating Profiles for Data Analysis by Webscraping.

Applying Machine Learning to Find Love The First Steps in Developing an AI Matchmaker. Dating Machine Learning Python Data Science Artificial Intelligence. More from Towards Data Science. Read more from Towards Data Science, marco zamarco online dating. More From Medium. Ismael Araujo in Towards Data Science.

Statistics: Are you Bayesian or Frequentist? Cassie Kozyrkov in Towards Data Science. Take your SQL from Good to Great: Part 3, marco zamarco online dating. Taylor Brownlow in Towards Data Science. Adam Hughes in Towards Data Science.

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