Customer Segmentation can be a powerful means to identify unsatisfied customer needs. This technique can be used by companies to outperform the competition by developing uniquely appealing products and services. Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics.

Using the above data companies can then outperform the competition by developing uniquely appealing products and services. You are owing a supermarket mall and through membership cards, you have some basic data about your customers like Customer ID, age, gender, annual income and spending score.

You want to understand the customers like who are the target customers so that the sense can be given to marketing team and plan the strategy accordingly. I started with loading all the libraries and dependencies. The columns in the dataset are customer id, gender, age, income and spending score. I dropped the id column as that does not seem relevant to the context. Also I plotted the age frequency of customers. Next I made a box plot of spending score and annual income to better visualize the distribution range.

The range of spending score is clearly more than the annual income range. I made a bar plot to check the distribution of male and female population in the dataset.

The female population clearly outweighs the male counterpart. Next I made a bar plot to check the distribution of number of customers in each age group. Clearly the 26—35 age group outweighs every other age group. I continued with making a bar plot to visualize the number of customers according to their spending scores.

The majority of the customers have spending score in the range 41— Also I made a bar plot to visualize the number of customers according to their annual income. The majority of the customers have annual income in the range and WCSS measures sum of distances of observations from their cluster centroids which is given by the below formula.

The main goal is to maximize number of clusters and in limiting case each data point becomes its own cluster centroid. In the plot of WSS-versus k, this is visible as an elbow. Finally I made a 3D plot to visualize the spending score of the customers with their annual income. The data points are separated into 5 classes which are represented in different colours as shown in the 3D plot.

K means clustering is one of the most popular clustering algorithms and usually the first thing practitioners apply when solving clustering tasks to get an idea of the structure of the dataset. The goal of K means is to group data points into distinct non-overlapping subgroups. One of the major application of K means clustering is segmentation of customers to get a better understanding of them which in turn could be used to increase the revenue of the company.

Automatic segmentation of microscopy images is an important task in medical These are some of my contacts details:. He is interested in data science, machine learning and their applications to real-world problems.Common scenarios for using unsupervised learning algorithms include: - Data Exploration - Outlier Detection - Pattern Recognition.

The most common and simplest clustering algorithm out there is the K-Means clustering. This algorithms involve you telling the algorithms how many possible cluster or K there are in the dataset.

The algorithm then iteratively moves the k-centers and selects the datapoints that are closest to that centroid in the cluster. One obvious question that may come to mind is the methodology for picking the K value. This is done using an elbow curve, where the x-axis is the K-value and the y axis is some objective function.

A common objective function is the average distance between the datapoints and the nearest centroid. After this point, it is generally established that adding more clusters will not add significant value to your analysis. Below is an example script for K-Means using Scikit-Learn on the iris dataset:. One issue with K-means, as see in the 3D diagram above, is that it does hard labels. However, you can see that datapoints at the boundary of the purple and yellow clusters can be either one.

For such circumstances, a different approach may be necessary. However, certain data points that exist at the boundary of clusters may simply have similar probabilities of being on either clusters. In such circumstances, we look at all the probabilities instead of the max probability. For the above Gaussian Mixure Model, the colors of the datapoints are based on the Gaussian probability of being near the cluster.

The RGB values are based on the nearness to each of the red, blue and green clusters. If you look at the datapoints near the boundary of the blue and red cluster, you shall see purple, indicating the datapoints are close to either clusters. One such application is text analytics. Common approach for such problems is topic modelling, where documents or words in a document are categorized into topics.

This is determined by how frequent are they in specific documents e.The data set that we are going to analyze in this post is a result of a chemical analysis of wines grown in a particular region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. The data set has observations and no missing values.

It can be downloaded here. Our goal is to try to group similar observations together and determine the number of possible clusters it may differ from 3.

This would help us make predictions and reduce dimensionality. The function sample. A summary of the data set is given below. It is extremely important to notice that the attributes are not on the same scale; therefore we are going to scale the data later.

First, we are going to deal with raw, unscaled, unpolished data. This is not the best approach, but we are interested in the results. We create a list named L1 with possible ways of clustering, using different seeds. This takes care of reproducibility. We see that the algorithm has done a decent job on the training set We notice that the SSE is quite high since we are dealing with unscaled data. This time we use the Manhattan distance in the k-means algorithm, which may be more useful in situations where different dimensions are not comparable.

Below are the results for raw data — we chose the clustering with minimal total WCSS. The jitter plots for the training and test sets are given below.

Note that the test set is being scaled with the same parameters used for the scaling the training set. We got the following results:. Obviously, the accuracy has improved when compared to unscaled data.These are just some of the real world applications of clustering. There are many other use cases for this algorithm but today we are going to apply K-means to text data. In particular, we are going to implement the algorithm from scratch and apply it to the Enron email data set and show how this technique can be a very useful way of summarizing large amounts of text and uncovering useful insights that might otherwise not be feasible.

So what exactly is K-means? Well, it is an unsupervised learning algorithm meaning there are no target labels that allows you to identify similar groups or clusters of data points within your data.

To see why it might be useful, imagine one of the use cases mentioned above, Customer Segmentation. A company using this algorithm would be able to partition their customers into different groups depending on their characteristics.

This can be a very useful way to engage in targeted advertising or to offer things like personalized discounts or promotions which is likely to drive revenue growth. For our use case, it can help give us quick insights and interpret text data.

### Most popular kaggle competition solutions

This is especially useful when we have huge amounts of data and it isn't really practical for someone to manually go through it. While working as an Economist I was able to use this technique to analyze a public consultation where a lot of the responses were qualitative in nature. Making use of my machine learning knowledge I was able to create useful insights and get a feel for the data while avoiding quite a bit of manual work for my colleagues which went down quite well.

Again the problem of K means can be thought of as grouping the data into K clusters where assignment to the clusters is based on some similarity or distance measure to a centroid more on this later.

So how do we do this? Now you may be wondering what we are optimizing for and the answer is usually Euclidean distance or squared Euclidean distance to be more precise. Data points are assigned to the cluster closest to them or in other words the cluster which minimizes this squared distance. We can write this more formally as:. This is a pretty simple algorithm, right?

Once we visualize and code it up it should be easier to follow. I have always been a fan of using visual aids to explain topics and it has usually helped me gain a deeper intuition of what is actually happening with various algorithms. As you can see the figure above shows K means at work.In Machine Learning, the types of Learning can broadly be classified into three types: 1.

Supervised Learning, 2. Unsupervised Learning and 3. Semi-supervised Learning. Algorithms belonging to the family of Unsupervised Learning have no variable to predict tied to the data. Instead of having an output, the data only has an input which would be multiple variables that describe the data.

This is where clustering comes in. Be sure to take a look at our Unsupervised Learning in Python course.

Clustering is the task of grouping together a set of objects in a way that objects in the same cluster are more similar to each other than to objects in other clusters.

Similarity is a metric that reflects the strength of relationship between two data objects. Clustering is mainly used for exploratory data mining. It has manifold usage in many fields such as machine learning, pattern recognition, image analysis, information retrieval, bio-informatics, data compression, and computer graphics. However, this post tries to unravel the inner workings of K-Means, a very popular clustering technique.

There's also a very good DataCamp post on K-Means, which explains the types of clustering hard and soft clusteringtypes of clustering methods connectivity, centroid, distribution and density with a case study. The algorithm will help you to tackle unlabeled datasets i.

## 4. Clustering Exercises

K-Means falls under the category of centroid-based clustering. A centroid is a data point imaginary or real at the center of a cluster. In centroid-based clustering, clusters are represented by a central vector or a centroid.

This centroid might not necessarily be a member of the dataset. Centroid-based clustering is an iterative algorithm in which the notion of similarity is derived by how close a data point is to the centroid of the cluster.

The sample dataset contains 8 objects with their X, Y and Z coordinates. Your task is to cluster these objects into two clusters here you define the value of K of K-Means in essence to be 2.

This is also known as the Taxicab distance or Manhattan distancewhere d is distance measurement between two objects, x1,y1,z1 and x2,y2,z2 are the X, Y and Z coordinates of any two objects taken for distance measurement. Feel free to check out other distance measurement functions like Euclidean Distance, Cosine Distance etc.

The following table shows the calculation of distances using the above distance measurement function between the objects and centroids OB-2 and OB-6 :. The objects are clustered based on their distances between the centroids. An object which has a shorter distance between a centroid say C1 than the other centroid say C2 will fall into the cluster of C1. After the initial pass of clustering, the clustered objects will look something like the following:.

Now the algorithm will continue updating cluster centroids i. The updation takes place in the following manner:. After this, the algorithm again starts finding the distances between the data points and newly derived cluster centroids. So the new distances will be like following:. The new assignments of the objects with respect to the updated clusters will be:.

Because there is no change in the current cluster formation, it is the same as the previous formation. Now when, you are done with the cluster formation with K-Means you may apply it to some data the algorithm has not seen before what you call a Test set. Any application of an algorithm is incomplete if one is not sure about its performance.

**Machine learning with Python and sklearn - Hierarchical Clustering (E-commerce dataset example)**

Now, in order to know how well the K-Means algorithm is performing there are certain metrics to consider. Some of these metrics are:.In the context of customer segmentationcluster analysis is the use of a mathematical model to discover groups of similar customers based on finding the smallest variations among customers within each group.

The goal of cluster analysis in marketing is to accurately segment customers in order to achieve more effective customer marketing via personalization. A common cluster analysis method is a mathematical algorithm known as k-means cluster analysissometimes referred to as scientific segmentation. The clusters that result assist in better customer modeling and predictive analyticsand are also are used to target customers with offers and incentives personalized to their wants, needs and preferences.

The process is not based on any predetermined thresholds or rules. Rather, the data itself reveals the customer prototypes that inherently exist within the population of customers. In threshold or rule-based segmentation approaches, the marketer selects a priori thresholds, typically in two dimensions, and divides the customers accordingly. The following example illustrates why this segmentation approach is weak. In the following diagram, we see that cluster analysis identified five distinct customer personas in the same data set as above the dots representing customers in each persona are colored differently.

The customers within in each persona are very similar to one another and significantly different than those in other personas. In other words, each persona tells a different customer story. The following chart shows the results of a three-dimension cluster analysis performed on the customer base of an e-commerce site.

This analysis resulted in the discovery of four customer personas. In other words, the distinct customer personas discovered by cluster analysis allow marketers to model their customers and personalize marketing efforts for much greater effectiveness. Because customer behavior changes frequently, performing cluster-based segmentation only once in a while is not sufficient.

Ideally, it should be performed daily, taking advantage of all the latest customer behavioral and transactional data.

### Clustering stocks using KMeans

For most online businesses, this means identifying dozens or hundreds of different personas that can be independently targeted by marketers. This, of course, is not something that can be easily done manually; rather, an automated system should be employed to ensure that the entire customer base is accurately segmented into relevant personas every day.

The next ingredient is connecting the discovered customer personas with the most relevant marketing interactions for each one. These interactions should cater to the specific wants, needs and preferences of each small, homogeneous group of customers represented by each persona.

Marketing creativity must be mated with an automated multi-channel marketing execution system that will allow marketers to address any number of different personas with any number of different marketing campaigns, every single day. Finally, there needs to be a measurement and optimization cycle in place.

By scientifically measuring the results of each campaign in terms of monetary uplift, marketers can know which campaigns are working well and which ones need improvement. The end result will be highly relevant marketing communications — leaving no customer behind — that generate long-term customer loyalty, improved brand perception and maximum customer value.

Interested in seeing a demonstration of an all-in-one Relationship Marketing Hub that does all of the above?

Request a one-on-one demo of Optimove, today! Dive straight into the feedback! Login below and you can start commenting using your own user instantly. Customer Segmentation via Cluster Analysis Cluster analysis uses mathematical models to discover groups of similar customers based on the smallest variations among customers within each group.

Learn more about Optimove Let us show you how our Relationship Marketing Hub can help improve your bottom line. Related Articles. Customer Segmentation. Customer Micro-Segmentation. Cohort Analysis. See All Learning Center Articles.Kaggle is one of the most popular data science competitions hub. Which offers a wide range of real-world data science problems to challenge each and every data scientist in the world. Every data science enthusiastic dreams to get top in kaggle leaderboard.

As the world is filled with some top mined data scientist. It will also offer freedom to data science beginners a way to learn how to solve the data science problems. So in this post, we were interested in sharing most popular kaggle competition solutions. This post will sure become your favourite one. This is the most recommend challenge for data science beginners. The problem statement for this challenge is to predict passenger survival or not survival.

You can find the solution for this problem in python and as well as in R programming language. All state purchase Prediction challenge is a tricky prediction problem. The features are like customer Id, information about the customer and the information about the policy and the cost. In detail, this challenge is to classify the morphologies of distant galaxies in our universe.

To solve this challenge we need understanding the distribution, location and different types of galaxies their shape, size, and color. Bird classification challenge is a 3-year-old problem but worth practicing. This is because of it mainly a voice-related problem where we have to predict the bird species from a given an audio clip with the length of 10 seconds.

Some of the trick challenges include in solving this problem were multiple simultaneously vocalizing birds, other sources of non-bird sound e. Image Credit: researchgate. Wikipedia has created this very large dataset. The dataset is multi-class, multi-label and hierarchical.

The numbers of categories were somewhere aroundand the numbers documents size is 2, This challenge builds upon a series of successful challenges on large-scale hierarchical text classification. I hope you like this post. If you have any questions then feel free to comment below. If you want me to write on one specific topic then do tell it to me in the comments below.

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