14 Dimensionality Reduction Ipynb Colab

There was an error while loading. Please reload this page. Master dimensionality reduction in Python with this complete Colab guide. Step-by-step tutorial with PCA, t-SNE, UMAP, and code examples. Dimensionality reduction is one of the most powerful techniques in machine learning. It reduces large, complex datasets into simpler, smaller ones while keeping the most important patterns.

In this step-by-step Python dimensionality reduction guide, you’ll learn how to set up your environment, load datasets, preprocess data, and apply algorithms like PCA, t-SNE, and UMAP. We’ll use Google Colab for hands-on coding. If you follow along, you’ll be able to apply dimensionality reduction in Python to real-world data. Dimensionality reduction is a preprocessing technique that simplifies datasets by reducing the number of features while keeping the most important patterns intact. Instead of analyzing hundreds of dimensions, you can project the data into fewer, more meaningful dimensions. Unlike feature selection, which only removes variables, dimensionality reduction usually works through feature extraction, creating new simplified features.

Dimensionality reduction is important because it simplifies models, speeds up training, reduces noise, and enables visualization of complex datasets. This repository provides a comprehensive exploration of dimensionality reduction techniques, applied to both image and tabular datasets, with implementations across Google Colab and Databricks. It is designed as a practical reference for data scientists, researchers, and students who want to compare linear and non-linear dimensionality reduction methods, evaluate their effectiveness on diverse datasets, and generate insightful visualizations. The project covers classic algorithms (e.g., PCA, Factor Analysis, MDS) alongside modern methods (e.g., t-SNE, UMAP, Autoencoders), and includes interactive visualizations to enhance interpretability. These datasets allow evaluation of how dimensionality reduction techniques behave across high-dimensional image data and structured tabular data. Dataset: Olivetti Faces (Scikit-learn) or MNIST (Keras).

Techniques: Apply all supported dimensionality reduction methods. Visualization: Interactive 2D and 3D embeddings via Plotly and Matplotlib. Insights: Dataset: Iris and medical datasets (e.g., diabetes). Techniques: Same set of linear and non-linear approaches. Visualization: 2D/3D scatter plots to highlight latent clusters.

Insights: Dataset: Larger medical/tabular datasets (e.g., Kaggle). Techniques: Scalable dimensionality reduction workflows. Focus: Processing speed, memory efficiency, and scalability. Comparison: Google Colab vs. Databricks performance on large-scale data.

Dimensionality Reduction Techniques – Overview Dimensionality reduction is another important unsupervised learning problem with many applications. We will start by defining the problem and providing some examples. We have a dataset without labels. Our goal is to learn something interesting about the structure of the data: Outliers: particularly unusual and/or interesting datapoints.

Useful signal hidden in noise, e.g. human speech over a noisy phone. It is a technique used in machine learning and data science to reduce the number of input variables (features) in a dataset while retaining as much meaningful information as possible. It is essential when dealing with high-dimensional data (i.e., when there are many features) to improve computational efficiency, reduce overfitting, and visualize data more easily. a) Image data set Colab Link: https://colab.research.google.com/drive/1IVwTCiaOXS87xwHSCdG1tbBNZ1fyoIxa#scrollTo=8Ojlz34ENO0I b) Tabular data set Colab Link: https://colab.research.google.com/drive/14Dt3Kyuo_3HQ_hMjC5sX_ORTwixLp9p9#scrollTo=WlS0w3p7oTwK

c) Datbricks Colab Link : https://colab.research.google.com/drive/1X0jWRiOoJpiK4KiGW6JAtACdjSL-gmDv Youtube link: https://youtu.be/ilbMDqOuZX8

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