bank marketing assignment upgrad

Bank Telemarketing Campaign Case Study.

In this case study you’ll be learning Exploratory Data Analytics with the help of a case study on "Bank marketing campaign". This will enable you to understand why EDA is a most important step in the process of Machine Learning.

Problem Statement:

The bank provides financial services/products such as savings accounts, current accounts, debit cards, etc. to its customers. In order to increase its overall revenue, the bank conducts various marketing campaigns for its financial products such as credit cards, term deposits, loans, etc. These campaigns are intended for the bank’s existing customers. However, the marketing campaigns need to be cost-efficient so that the bank not only increases their overall revenues but also the total profit. You need to apply your knowledge of EDA on the given dataset to analyse the patterns and provide inferences/solutions for the future marketing campaign.

The bank conducted a telemarketing campaign for one of its financial products ‘Term Deposits’ to help foster long-term relationships with existing customers. The dataset contains information about all the customers who were contacted during a particular year to open term deposit accounts.

What is the term Deposit?

Term deposits also called fixed deposits, are the cash investments made for a specific time period ranging from 1 month to 5 years for predetermined fixed interest rates. The fixed interest rates offered for term deposits are higher than the regular interest rates for savings accounts. The customers receive the total amount (investment plus the interest) at the end of the maturity period. Also, the money can only be withdrawn at the end of the maturity period. Withdrawing money before that will result in an added penalty associated, and the customer will not receive any interest returns.

Your target is to do end to end EDA on this bank telemarketing campaign data set to infer knowledge that where bank has to put more effort to improve it's positive response rate.

Importing the libraries.

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Introduction This case study aims to give you an idea of applying EDA in a real business scenario. In this case study, apart from applying the techniques that you have learnt in the EDA module, you will also develop a basic understanding of risk analytics in banking and financial services and understand how data is used to minimise the risk of losing money while lending to customers.

Business Understanding The loan providing companies find it hard to give loans to the people due to their insufficient or non-existent credit history. Because of that, some consumers use it as their advantage by becoming a defaulter. Suppose you work for a consumer finance company which specialises in lending various types of loans to urban customers. You have to use EDA to analyse the patterns present in the data. This will ensure that the applicants capable of repaying the loan are not rejected.

When the company receives a loan application, the company has to decide 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.

The data given below contains the information about the loan application at the time of applying for the loan. It contains two types of scenarios:

The client with payment difficulties: he/she had late payment more than X days on at least one of the first Y instalments of the loan in our sample,

All other cases: All other cases when the payment is paid on time.

When a client applies for a loan, there are four types of decisions that could be taken by the client/company):

Approved: The Company has approved loan Application

Cancelled: The client cancelled the application sometime during approval. Either the client changed her/his mind about the loan or in some cases due to a higher risk of the client he received worse pricing which he did not want.

Refused: The company had rejected the loan (because the client does not meet their requirements etc.).

Unused offer: Loan has been cancelled by the client but on different stages of the process.

In this case study, you will use EDA to understand how consumer attributes and loan attributes influence the tendency of default.

Business Objectives This case study aims to identify patterns which indicate if a client has difficulty paying their installments which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc. This will ensure that the consumers capable of repaying the loan are not rejected. Identification of such applicants using EDA is the aim of this case study.

In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilise this knowledge for its portfolio and risk assessment.

To develop your understanding of the domain, you are advised to independently research a little about risk analytics - understanding the types of variables and their significance should be enough).

Data Understanding Download the dataset from below.

Dataset Download This dataset has 3 files as explained below:

'application_data.csv' contains all the information of the client at the time of application. The data is about whether a client has payment difficulties.

'previous_application.csv' contains information about the client’s previous loan data. It contains the data whether the previous application had been Approved, Cancelled, Refused or Unused offer.

'columns_description.csv' is data dictionary which describes the meaning of the variables.

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    In addition, banks also hold better chance to persuade term deposit clients into buying other products such as funds or insurance to further increase their revenues. You are provided a dataset containing details of marketing campaigns done via phone with various details for customers such as demographics, last campaign details etc.

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    Assignment 1 Bank Marketing Case Study: loading and merging data . Ques 1: Load data from customer_banking_info_promo.xslx define the library name "mylib" and specify its location using libname Use proc import DATAFILE to import customer_banking_info_promo.xlsx into a sas dataset named customer_banking_info_promo under mylib Print ...

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    PK !¿Ý §K ` [Content_Types].xml ¢ ( Ì™]oÛ †ï'í?XÜN6I·um § û¸ÚG¥v?€ÙÇ š H-üûa;i½*MÜ »‰ >çå! ¼ {v½iêd Úp)r2Í&$ QÈ'‹EN~Þ}I/Hb, %«¥€œlÁ ëùëW³»­ "¸lar²´V]QjŠ%4ÌdR pW*© f]S/¨bÅo¶z6™œÓB ¦¶Õ óÙ'¨Øª¶Éç ëîI*aKf I>ö±íp9aJÕ¼`Ö…ÐMZÉ] =(¡ÄâQ:oZ„¶ÿp††Ú q-ÊGSKwÓÊ\f c-\™7.à‰ Ú+G¦Ôçýp÷ ...

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  23. GitHub

    1 2 forks No releases published No packages published Contribute to AnkurNapa/upGrad_EDA_Assignment development by creating an account on GitHub.