Sarangi Wijemannasarangiwijemanna.hashnode.dev·Dec 4, 2023ML - Day 19 | UNSUPERVISED LEARNING | Principal Component Analysis (PCA) 🌸😊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 ...DiscussMachine Learning
Rutvik Acharyadatamokotow.com·Jun 9, 2023Understanding Principal Component AnalysisIntroduction 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...DiscussData SciencePca
Md.Anisur Rahmananisurrahmansblog.hashnode.dev·May 7, 2023Principal Component Analysis in MLIn 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 ...Discuss·1 like·71 readsMachine Learning
Nevin Selbynevins-corner.hashnode.dev·Mar 10, 2023Feature selection methods for Data ScientistsFeature selection helps improve the performance of machine learning models by identifying the most relevant and informative features in a dataset. By only selecting the most relevant features, data scientists can reduce the risk of overfitting, which...Discuss·30 likes·35 readsData Science
Chinmaya Sahuchinmaya.hashnode.dev·Mar 9, 2023Principal Component Analysis - Why? What? How?Where?Why? While working with data, either in your traditional data science role, or performing exploratory data analysis for your machine learning task, visualizing it is an immensely important task. But creating visualizations for data that is expressed ...Discuss·1 like·147 readsPrincipal Component Analysis (PCA)
