Naive Data

In this post, we are going to perform Exploratory Data Analysis to understand how data is used to minimize the risk of losing money while lending to customers. We are going to have a look at the dataset for one of the financial institution called LandingClub. Background LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California. It enables borrowers to create unsecured personal loans between $1,000 and $40,000. The standard loan period is three years. Investors can search and browse the loan listings on LendingClub website and select loans that they want to invest in based on the information supplied about the borrower, amount of loan, loan grade, and loan purpose. Investors make money from interest. LendingClub makes money by charging borrowers an origination fee and investors a service fee. Business Understanding When a loan application is received by the company, the company must make a decision to approve the lo...

Photo by Franck V.   In this  post series we are going to learn the basics of Machine Learning. Let's starts with what is Machine Learning: “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”  Below are the top practical definitions from different sources: “Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” – Nvidia  “Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinsey & Co. “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”...

In today's world, in any industry, customers have multiple options to choose from like in Telecom, we have AT&T, Verizon, T- mobile, etc, in Media, we have Netflix, Amazon Prime, Apple TV, etc.  and similarly for others. In this highly competitive market, the industries face a large number of customer churn. Considering that obtaining a new customer costs 5-10 times more than maintaining a current one, hence,  retention of customers has now become even more important than acquiring a customer. Here we are going to take data from one of the telecom company, to understand the customer churn. What is the customer churn? If a client or subscriber ceases to do business with a company or service, its called Customer Churn.  Now let's take a deep dive to understand why the Customer Churn happens and how to predict it before happening. Understanding the data Before working on any model, we need to understand the data and try to a...

Linear regression is one of the widely used statistical techniques for analysis. It provides a relationship between the target and the predictors. The below graph shows the linear relationship between the “dependent” variable Y, whose values we wish to predict and the “independent” variable X from which we wish to predict it. There are two types of linear regression: 1. Simple Linear Regression : It denotes the relationship between one variable and the target variable. The above graph represents the Simple Linear Regression. 2. Multiple Linear Regression :     It denotes the relationship between 2 or more independent variables and the corresponding dependent variable. The independent variables can be continuous or categorical. The below graph shows the multiple linear regression. Properties of Linear Regression Below are the main properties of linear regression: There is a linear relationship between X a...