Naive Data

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 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                          ...

Photo by  Arseny Togulev This is the second part of the series on the Basics of Machine Learning. Please refer to this  link  for part 1. Assumptions of Simple Linear Regression Below are the main assumptions of linear regression: There is a  linear  relationship between X and Y. Error terms are  normally  distributed. Error terms are  independent  of each other. Change in one error term should not impact the other error terms. Error terms have  constant variance  i.e. variance shouldn’t increase or decrease as the error values change. Also, they shouldn’t follow any pattern. If the variance is not constant or homoscedastic then the inferences made on the model would be unreliable. Checking the Model Fit: We should always make sure that the model fit is really a good fit and is not by a chance. There are many ways to test it. T-Test: The t-distribution is also a kind of normal distribution; it is also symmetric and single-peaked, bu...

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 ...