In this project I used concepts of Linear Regression for predicting the house price using the target variables acoording to various categories like:Area of house(in sr feet), age of house(in years), number of rooms e.t.c.
Customer churn prediction model made using Decicsion Tree concept of Machine Learning, can help you see which customers are about to leave your service so you can develop proper strategy to re-engage them before it is too late. This is a vital tool in a business’ arsenal when it comes to customer retention.The ability to predict with help of target variable that a particular customer is at a high risk of churning, while there is still time to do something about it, represents a huge additional potential revenue source for every online business. Besides the direct loss of revenue that results from a customer abandoning the business, the costs of initially acquiring that customer may not have already been covered by the customer’s spending to date.
This model is made using Classification and Regression Trees. It best suits both predictive and decision modeling problems. Since the global financial crisis, risk management in banks has to take a major role in shaping decision-making for banks. A major portion of risk management is the approval of loans to promising candidates. But the black-box nature of Machine learning algorithms makes many loan providers vary the result.The Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.
A marketing firm wants to launch a promotional campaign in different regions of the country. So, in order to do that, they need to understand which areas they should focus their resources in order to cover the entire region. We are provided with the population data based on different locations along with the demographics. The objective is to segregate the regions into different groups so that the marketing team can plan their resources accordingly. So we have to apply classification techniques in order to segregate the regions into different clusters which will help the marketing team.