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...

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...

The Artificial Neural Network (ANN) is inspired by the animal brain. Even though currently the ANNs are not as powerful as the brain yet, these are one of the most powerful learning models in the field of machine learning. Understanding the Human Brain and ANNs Let's understand first how the human brain works and how it influences the ANNs. The biological neuron receives signals through its dendrites which are either amplified or inhibited as they pass through the axons to the dendrites of other neurons. ANNs are also based on a similar concept, where ANNs are a collection of a large number of simple devices called Artificial Neurons. The network learns  to perform certain tasks ( like identifying a car ) by training the neurons to fire an action when a particular input ( like an image of a car) is provided. Perceptron Perceptron is one of the earlier proposed models, which takes a weighted sum of the inputs and applies an activation function to it...

Photo by Chris Liverani This is the first post in the series Statistics Terms for Data Science. In this series , we are going to understand the Statistics Concepts which are used in Data Science. Probability: Probability is a measure of the chances of an event happening. You can't predict many events with complete certainty. We can only predict the chance of an event occurring i.e. how likely it will occur, using it. Probability will vary from 0 to 1, where 0 means that the event is impossible, and 1 implies a certain event. Random Variables: Random Variables , generally denoted by X, provides the association between the outcome of the experiment to something measurable.  For e.g. For a loan re-payment analysis, we come to know below outcome 1. Salary < 2 Lakhs ,fails to repay 2. Salary > 2 Lakhs , can repay So here we have two outcomes, fails to repay and can repay , so we can assign a value to each outcome i.e. we can define the Random Variable X as: This is useful in ...