Business understanding:

CredX is a leading credit card provider that gets thousands of credit card applicants every year. But in the past few years, it has experienced an increase in credit loss. The CEO believes that the best strategy to mitigate credit risk is to ‘acquire the right customers’.

In this project, We will help CredX identify the right customers using predictive models. Using past data of the bank’s applicants, you need to determine the factors affecting credit risk, create strategies to mitigate the acquisition risk and assess the financial benefit of your project.

Goals:

• Help the business to identify the key risk…

Objective

Build a response model based on the clients, campaign and economic information provided by the Portuguese bank.

Models Used

We made use of Logistic Regression and Random Forest.

Conclusion

• We can conclude that our target is the 80.79% or 81% of the customers from the gain chart.
• The Average cost per call for the targeted 81% customers is 9.7745728
• The Avearage duration of xall for the targeted 81% customers is 271.95674
• The total cost for all the 81% targeted customers will come up to be approximately 2 lakhs which is better than the random model.

The R Code

https://gist.github.com/15Nik/1f59bd143984de9f403e4d094ccdeba8

Abstract

Aim: To forecast sales and demand for next 6 months.

Problem Statement:

• Global Mart is an online store super giant having worldwide operations. It takes orders and delivers across the globe and deals with all the major product categories consumer, corporate & home office.
• It wants to finalize the plan for the next 6 months. So, it wants to forecast the sales and the demand for the next 6 months, that would help you manage the revenue and inventory accordingly.

Approach:

• We make use of R, a statistical computing program to perform various operations and hence come up with the…

Business Understanding

A classic problem in the field of pattern recognition is that of handwritten digit recognition. Suppose that you have an image of a digit submitted by a user via a scanner, a tablet, or other digital devices. The goal is to develop a model that can correctly identify the digit (between 0–9) written in an image.

Objective

You are required to develop a model using Support Vector Machine which should correctly classify the handwritten digits based on the pixel values given as features.

Dataset

For this problem, we use the MNIST data which is a large database of handwritten digits where we…

Problem Statement

A large company named XYZ, employs, at any given point of time, around 4000 employees. However, every year, around 15% of its employees leave the company and need to be replaced with the talent pool available in the job market. The management believes that this level of attrition (employees leaving, either on their own or because they got fired) is bad for the company, because of the following reasons -

1. The former employees’ projects get delayed, which makes it difficult to meet timelines, resulting in a reputation loss among consumers and partners
2. A sizeable department has to be maintained, for…

Problem Statement

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.

They have contracted an automobile consulting company to understand the factors on which the pricing of a car depends. Specifically, they want to understand the factors affecting the pricing of cars in the American marketing, since those may be very different from the Chinese market. Essentially, the company wants to know:

• Which variables are significant in predicting the price of a car
• How well those variables describe the…

Business Understanding

A consumer finance company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision:

• If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company
• If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company

Abstract

Aim…