Overview
Clustering Kmeans Algorithm Implementation Applications Geyser’s Eruptions Segmentation Image Compression Evaluation Methods Drawbacks Conclusion Clustering
Clustering is one of the most common exploratory data analysis technique used to get an intuition about the structure of the data. It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different.

Introduction
The two most critical questions in the lending industry are: 1) How risky is the borrower? 2) Given the borrower’s risk, should we lend him/her? The answer to the first question determines the interest rate the borrower would have. Interest rate measures among other things (such as time value of money) the riskness of the borrower, i.e. the riskier the borrower, the higher the interest rate. With interest rate in mind, we can then determine if the borrower is eligible for the loan.

Trajectory towards local minimum. Optimization refers to the task of minimizing/maximizing an objective function f(x) parameterized by x. In machine/deep learning terminology, it’s the task of minimizing the cost/loss function J(w) parameterized by the model’s parameters $w \in \mathbb{R}^d$. Optimization algorithms (in case of minimization) have one of the following goals:
Find the global minimum of the objective function. This is feasible if the objective function is convex, i.

Employee turnover refers to the percentage of workers who leave an organization and are replaced by new employees. It is very costly for organizations, where costs include but not limited to: separation, vacancy, recruitment, training and replacement. On average, organizations invest between four weeks and three months training new employees. This investment would be a loss for the company if the new employee decided to leave the first year.

© Imad Dabbura 2018 · Powered by the Academic theme for Hugo.