A python script to collect the reviews of worst restaurants in Delhi using zomato's api and cluster them to figure why they were worst using K Means Machine Learning algorithm.
K-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.
In this project I hve called zomato api and then clustered all the worst restaurants of Delhi NCR on different parameters by Kmeans clustering .The results are shown below graphically.
Bilbiography Wikipedia and NPTEL Youtube Channel (Machine Learning Course by IIT Kharagpur.)