- [**K Means Clustering**](https://github.com/suubh/Machine-Learning-in-Python/blob/master/K-means/creditcard.ipynb) : K-Means clustering is used to find intrinsic groups within the unlabelled dataset and draw inferences.It is one of the most detailed projects, In this project, I implement K-Means Clustering on Credit Card Dataset to cluster different credit card users based on the features.I scaled the data using *StandardScaler* because normalizing(scale in range 0 to 1) will improves the convergence.I also implemented the [*Elbow Method*](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) to search for the best numbers of clusters.For visualizing the dataset I used [*PCA(Principal Component Analysis)*](https://en.wikipedia.org/wiki/Principal_component_analysis) for dimensionality reduction as the dataset features were large in number.In the end I used [*Silhouette Score*](https://en.wikipedia.org/wiki/Silhouette_(clustering)) which is used to calculate the performance of clustering . It ranges from -1 to 1 and I got a score of 0.203.