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Created 3 years ago
from google.colab import drive
drive.mount('/content/drive')Mounted at /content/drive
Visualizing High Dimensional MNIST Dataset with PCA and t_SNE
Introduction
Humans can't visualize more then three dimensions but many a times data is available in more then three dimensions so to visualize the data in 2 or 3 dimension which humans can visualize. we reduce the high dimensional data to 2/3 dimensions through Dimensionality reduction techniques like PCA and t-SNE.
Objective : we will take a data set of 784 dimensions and conver it in to 2 and 3 dimensions to visualize it.#
#importing library
import numpy as np
import pandas as pd
# for visualization purpose
import seaborn as sns
import matplotlib.pyplot as plt
# for preprocessing purpose
from sklearn.preprocessing import StandardScaler
# for doing PCA
from sklearn import decomposition
# for t-SNE implemention
from sklearn.manifold import TSNE