This Repository contains implementation of K_Means Algorithm from scratch in numpy.

Dataset Used : 50,000 Images from CIFAR 10 Dataset

1. Load the data from the following link: https://www.cs.toronto.edu/~kriz/cifar.html

Features(Generated for each CIFAR Image):

1. Every ML technique requires a carefully crafted features on which that particular algorithm is trained, Similarly our K_Means Algorithm will require some appropriate feature vector.
2. Feature Generation(For KMeans): a) Convert images to grayscale images.
   b) Find the histograms and get a 10-dimensional representation of each images
   c) For each image, find the histogram with bin size 25 (last bin of 30;i.e.225-255;giving you 10 bins)
   d) Data Matrix = Datapoints x Feature Size = 5000 x 10

Distance-Metric utilized here in K-Means Algorithm: L2-Distance

Value of K used for demonstration : 5 (5 Clusters)

Visulization : Principal Component Analysis (PCA)

Sample Ouput: