Credit scoring and segmentation refer to the process of evaluating the creditworthiness of individuals or businesses and dividing them into distinct groups based on their credit profiles. It aims to assess the likelihood of borrowers repaying their debts and helps…
Customer Segmentation is used to divide a company’s customer base into distinct groups based on common characteristics. It helps companies better understand their customers. If you want to understand Customer Segmentation and how Data Science professionals use this technique, this…
App user segmentation is the task of grouping users based on how they engage with the app. It helps find retained users, find the user segment for a marketing campaign, and solve many other business problems where you need to…
Credit card clustering is the task of grouping credit card holders based on their buying habits, credit limits, and many other financial factors. If you want to learn how to use clustering analysis to group credit card holders, this article…
Clustering is a machine learning technique to group data points characterized by specific features. Clustering music genres is a task of grouping music based on the similarities in their audio characteristics. If you want to learn how to perform clustering…
Clustering is the task of identifying similar instances based on similar features and assigning them to clusters based on similar instances. It sounds like classification where each instance is also assigned to a group, but unlike classification, clustering is based…
The mean shift algorithm is a nonparametric clustering algorithm that does not require prior knowledge of the number of clusters. If you’ve never used the Mean Shift algorithm, this article is for you. In this article, I’ll take you through…
The Mini-batch K-means clustering algorithm is a version of the standard K-means algorithm in machine learning. It uses small, random, fixed-size batches of data to store in memory, and then with each iteration, a random sample of the data is…
Agglomerative clustering is based on hierarchical clustering which is used to form a hierarchy of clusters. It is one of the types of clustering algorithms in machine learning. Unlike the K-Means and DBSCAN clustering algorithms, it is not very common…
Clustering is a machine learning technique that is used to group unlabeled data points so that the data points present in a group are based on similar functionality. If you are looking for some data science case studies on clustering,…
In machine learning, customer segmentation is based on the problem of clustering which means finding clusters in a dataset with the same features. Customer segmentation can help a business focus on marketing strategies to increase profits and overall customer satisfaction.…
The BIRCH is a Clustering algorithm in machine learning. It stands for Balanced Reducing and Clustering using Hierarchies. In this article, I will take you through the concept of BIRCH Clustering in Machine Learning and its implementation using Python. BIRCH…
Cosine similarity is a method used in building machine learning applications such as recommender systems. It is a technique to find the similarities between the two documents. In this article, I’ll give you an introduction to Cosine Similarity in Machine…
In Machine Learning, clustering involves identifying similar instances and then assigning them to similar clusters or groups of instances. It is an unsupervised machine learning technique. In this article, I’ll give you an introduction to the most popular clustering algorithms…
Clustering is used to divide data into subsets, and classification is used to create a predictive model that can be used to categorize the values of future data points. In this article, I’ll walk you through the difference between clustering…
The K-Means Clustering is a clustering algorithm capable of clustering an unlabeled dataset quickly and efficiently in just a very few iterations. In this article, I will take you through the K-Means clustering in machine learning using Python. K-Means Clustering…
Collaborative filtering is a recommendation system method that is formed by the collaboration of multiple users. The idea behind it is to recommend products or services to a user that their peers have appreciated. In this article, I will introduce…
Most recommendation systems use content-based filtering and collaborative filtering to show recommendations to the user to provide a better user experience. Content-based filtering generates recommendations based on a user’s behaviour. In this article, I will walk you through what content-based…
If you want to find out who are your best customers, using an old technique RFM matrix principle is still the best in the business. RFM means – Recency, Frequency and Monetary. RFM is basically a customer segmentation technique that…