Introduction K-Means Clustering is one of the simplest and most widely used unsupervised learning algorithms for partitioning a dataset into K distinct non-overlapping clusters. It groups data points based on their similarity, aiming to minimize the ...
1. Introduction Customer segmentation is essential for businesses to understand their diverse customer base and offer personalized experiences. By dividing customers into distinct groups based on purchasing behavior, demographics, and preferences, co...
Introduction Absenteeism is a habitual pattern of absence from a duty or obligation. Employee absenteeism is expensive and incurs a significant loss to an organization. It is essential to identify employees' characteristics according to their level o...
1. Introduction In the competitive banking industry, understanding customers' behavior and needs is critical for delivering personalized services, enhancing customer satisfaction, and improving profitability. By leveraging machine learning techniques...
Clustering is an important component in vector search, and the k-means algorithm remains one of the most widely used techniques. However, its simplicity comes with a significant drawback: resource-intensive computation can render it impractical for l...
The computer scientist Yann LeCun famously said that if intelligence was a cake, unsupervised learning would be the cake , supervised learning would be the icing and reinforcement learning would be the cherry on the cake. So, here we are dealing with...
Congrats for completing regression and classification. I’m so proud of you. In this blog we will learn clustering. Clustering is similar to classification but the basis is different. In clustering we don’t know what we are looking for but we are tryi...
import numpy as np import matplotlib.pyplot as plt def kmeans(data, k, max_iter=100): # Randomly initialize centroids np.random.seed(0) # For reproducibility centroids = data[np.random.choice(data.shape[0], k, replace=False)] for _...
Imagine you're standing in front of a massive, chaotic pile of colorful marbles. Your task? Organize them into distinct groups based on their similarities. Sounds daunting, right? Now, picture an intelligent algorithm that can do this for you, not ju...
Table of Contents Introduction Understanding the Dataset Data Wrangling and Cleaning Exploratory Data Analysis (EDA) Unsupervised Learning Techniques K-Means Clustering Principal Component Analysis (PCA) Autoencoders 6. Visualizing Custom...
