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
- To predict whether a liability customer will buy a personal loan or not.
- Which variables are most significant.
- Which segment of customers should be targeted more.
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 :
- The notebook should be well-documented, with inline comments explaining the functionality of code and markdown cells containing comments on the observations and insights.
- The notebook should be run from start to finish in a sequential manner before submission.
- It is preferable to remove all warnings and errors before submission.
- The notebook should be submitted as an HTML file (.html) and NOT as a notebook file (.ipynb)
Best Practices for Presentation :
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 leader like CMO, COO, CFO, or CEO) to the key points of your project, which are
- Business Overview of the problem and solution approach
- Key findings and insights which can drive business decisions
- Model overview and performance summary
- Business recommendations
Please keep the following points in mind while making the presentation:
- Focus on explaining the takeaways in an easy-to-understand manner.
- The inclusion of the potential benefits of implementing the solution will give you the edge.
- Copying and pasting from the notebook is not a good idea, and it is better to avoid showing codes unless they are the focal point of your presentation.
- Please submit the presentation in PDF format only.
Submission Guidelines :
- There are two parts to the submission:
- A well commented Jupyter notebook [format - .html]
- A presentation as you would present to the top management/business leaders [format - .pdf ](you have to export/save the .pptx file as .pdf)
- Any assignment found copied/ plagiarized with other groups will not be graded and awarded zero marks
- Please ensure timely submission as any submission post-deadlinewill not be accepted for evaluation
- Submission will not be evaluated if,
- it is submitted post-deadline, or,
- more than 2 files are submitted
Happy Learning!!
Scoring guide (Rubric) -My bank
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 EDAKey meaningful observations on the relationship between variables |
5 |
|---|
Data Pre-processingPrepare the data for analysis: - Missing value Treatment (if needed) - Outlier Detection(treat, if needed) - Feature Engineering - Data split |
4 |
|---|
Model building - Logistic Regression- Build the model and comment on the model statistics - Test assumptions - Filter out key variables that have a strong relationship with the dependent variable |
12 |
|---|
Model performance evaluation and improvement- Comment on which 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 |
8 |
|---|
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? |
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? |
|---|