Naive Data

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 ...

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...

  Photo by  Jens Lelie  on  Unsplash This is the second post in In Bytes series. Refer this link to read about the first post. Random Forest: Random forest is an ensemble made by the combination of a large number of decision trees. Ensemble: An ensemble means a group of things viewed as a whole rather than individually. In ensembles, a collection of models is used to make predictions, rather than individual models. In principle, ensembles can be made by combining all types of models. An ensemble can have a logistic regression, a neural network, and few decision trees working in unison. While choosing the model , we need to check for two things- Diversity and Acceptability. Diversity ensures that the models serve complementary purposes, which means that the individual models make predictions independent of each other. The advantages of this are different depending on the type of ensemble. Diversity ensures that even if some trees overfit, the other trees in the ensemb...

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...

  Photo by Fabrice Villard on Unsplash This the first post in Byte Series, where we are going to have discuss on overall concept but in brief to act a refresher. Decision Trees: As the name goes, a decision tree uses a tree-like model to make predictions. It resembles an upside-down tree. Decision trees are easy to interpret; you can always go back and identify the various factors leading to a decision. A decision tree requires you to perform tests on attributes in order to split the data into multiple partitions. Types: Single and Multiway class:                If a test splits the data into more than two partitions, this is called a multiway decision tree. Classification and Regression:              In regression problems, a decision tree splits the data into multiple subsets. The difference                          ...