Personal Loan Campaign - Problem Statement
Description
Background and Context
AllLife Bank is a US bank that has a growing customer base. The majority of these customers are liability customers (depositors) with varying sizes of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors).
A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio.
You as a Data scientist at AllLife bank have to build a model that will help the marketing department to identify the potential customers who have a higher probability of purchasing the loan.
Objective
Data Dictionary
* ID: Customer ID
* Age: Customer’s age in completed years
* Experience: #years of professional experience
* Income: Annual income of the customer (in thousand dollars)
* ZIP Code: Home Address ZIP code.
* Family: the Family size of the customer
* CCAvg: Average spending on credit cards per month (in thousand dollars)
* Education: Education Level. 1: Undergrad; 2: Graduate;3: Advanced/Professional
* Mortgage: Value of house mortgage if any. (in thousand dollars)
* Personal_Loan: Did this customer accept the personal loan offered in the last campaign?
* Securities_Account: Does the customer have securities account with the bank?
* CD_Account: Does the customer have a certificate of deposit (CD) account with the bank?
* Online: Do customers use internet banking facilities?
* CreditCard: Does the customer use a credit card issued by any other Bank (excluding All life Bank)?
Best Practices for Notebook :
Like in real-world projects, the ultimate destination of any project or work is generally an executive or decision-making meeting, where you are supposed to present your solution to the business problem, based on the project/work you have done. The purpose of this presentation is to simulate that kind of experience and to draw the attention of your audience (a business a leader like CMO, COO, CFO, or CEO) to the key points of your project, which are
Please keep the following points in mind while making the presentation:
Submission Guidelines :
Happy Learning!!
Scoring guide (Rubric) - Personal Loan Campaign Modelling
Criteria |
Points |
Perform an Exploratory Data Analysis on the data - Univariate analysis - Bivariate analysis - Use appropriate visualizations to identify the patterns and insights - Any other exploratory deep dive |
5 |
Illustrate the insights based on EDA Key meaningful observations on the relationship between variables |
5 |
Data Pre-processing Prepare the data for analysis - Missing value Treatment, Outlier Detection(treat, if needed), Feature Engineering, Prepare data for modeling and check the split |
4 |
Model building - Logistic Regression - Build the model and comment on the model statistics - Check assumptions of Logistic Regression - Comment on variables that have a strong relationship with the dependent variable |
10 |
Model performance evaluation and improvement - Comment on which the metric is right for model performance evaluation and why? - Comment on model performance - Can model performance be improved? if yes then do it |
5 |
Model building - Decision Tree - Build the model and comment on the model statistics - Identify the key variables that have a strong relationship with the dependent variable |
5 |
Model performance evaluation and improvement - Evaluate the model on appropriate metric - Comment on model performance - Can model performance be improved? if yes then do it |
5 |
Actionable Insights & Recommendations - Compare decision tree and Logistic regression - Conclude with the key takeaways for the marketing team - what would your advice be on how to do this campaign? |
5 |
Perform an Exploratory Data Analysis on the incorrectly predicted data - Do an analysis of all the incorrectly predicted or misclassified samples ( Eg: original label = 0 but model predicted it as 1) - Perform EDA on such points to see if there is a pattern |
4 |
Presentation - Overall quality - Structure and flow - Crispness - Visual appeal - Key insights and recommendations |
8 |
Notebook - Overall - Conclude with the key takeaways for the business - What would your advice be to grow the business? |
4 |
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