Introduction Performance appraisal is a regular review of an employee’s job performance and overall contribution to a company. It evaluates an employee’s attitude, knowledge, skills, commitment, achievements, etc. Organizations use performance apprai...
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...
HR Analytics What gets measured gets managed. What gets managed gets executed. -Peter Drucker Global organizations with workforce analytics and planning outperform all other organizations by 30% more sales per employee. -CedarCrestone Research Surve...
You will learn about PCA and how it can be leveraged to extract information from the data without any supervision using two popular datasets: Breast Cancer and CIFAR-10. Principal component analysis (PCA) is a linear dimensionality reduction techniqu...
Introduction Welcome back to the eighth blog post in our Machine Learning series! Today, we're diving into Principal Component Analysis (PCA), a powerful tool for dimensionality reduction. PCA simplifies complex datasets while keeping as much informa...
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...
Dimensionality reduction is a fundamental technique in machine learning (ML) that simplifies datasets by reducing the number of input variables or features. This simplification is crucial for enhancing computational efficiency and model performance, ...
Clustering Plant Iris Using PCA🌷🌼 Step 1: Find Problem 🔎 Categorizing Iris Data into 'setosa' 'versicolor' 'virginica' Step 2: Collect Dataset 🛒 Leaf Iris data analysis and segregate data into different categories. Import the iris dataset ...
Introduction Hey there fellow data enthusiasts! Have you ever struggled with datasets that have too many variables? Fear not, because dimensionality reduction is here to save the day! Simply put, dimensionality reduction is the process of reducing th...
In short,PCA(Principal Component Analysis) is a dimensional reduction technique used for high dimensional dataset(i.e many columns) , it's basically taking a snap shot of the dimension of data from an angle so that we can capture the max varience of ...
