Bhavya Shingariadvancedideamechanics.hashnode.dev·Dec 8, 2024Principal Component Analysis(PCA) in PythonYou 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...Discuss·10 likesPca
Arbash Hussaincckeh.hashnode.dev·Sep 9, 2024A Step by Step Guide to Principal Component Analysis (PCA) in Machine LearningIntroduction 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...Discuss·104 readsMachine LearningDimensionality Reduction
Gayathri Selvaganapathiaienthusiast.hashnode.dev·Aug 31, 2024Customer Segmentation Using Machine LearningTable 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...DiscussCustomer Segmentation, personalized experiences, technographic segmentation,
Md Shahriyar Al Mustakim Mitulmitul-shahriyar.hashnode.dev·Aug 8, 2024Machine Learning : Dimensionality Reduction Principal Component Analysis(PCA) - (Part 32)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, ...Discuss·2 likes·29 readsML From scratch to ExpertPrincipal Component Analysis (PCA)
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...Discuss·27 readsData 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 likeMachine 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·34 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·104 readsPrincipal Component Analysis (PCA)
Ayomide Oguntuaseayotuase.hashnode.dev·Jun 3, 2022Unsupervised Learning: Unlocking Hidden Patterns in DataUnsupervised learning is a fascinating area of machine learning where the algorithm is left to discover hidden structures in unlabeled data. Unlike supervised learning, which relies on labeled input-output pairs, unsupervised learning works without p...DiscussMachine Learning
