Visualizing Multidimensional Financial Data: T-SNE and UMAP for Dimensionality Reduction
Analyzing and visualizing multidimensional data is crucial for making informed decisions. However, dealing with high-dimensional financial data can be challenging. In this tutorial, we will explore two powerful dimensionality reduction techniques, t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), to visualize complex financial data in a lower-dimensional space. We will use real financial data obtained from Yahoo Finance and demonstrate how to apply these techniques to gain insights into market trends and patterns.
Dimensionality reduction techniques like t-SNE and UMAP are essential for visualizing high-dimensional data in a lower-dimensional space while preserving the underlying structure and relationships. These techniques are widely used in various domains, including finance, to gain a deeper understanding of complex datasets.
Let’s begin by importing the necessary libraries and downloading real financial data using the yfinance library.
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.manifold import TSNE
import umap
