Pig E.

Bank

Predictive analysis of customer retention

Business Requirement

Analytical support will be provided to the company's anti-money-laundering compliance department. This entails conducting various datarelated projects to assess client and transaction risk, while also reporting on relevant metrics.

Additionally, contributions will be made to building and optimizing models aimed at enhancing the efficiency of the bank's compliance program.

Skills

  • Proficiency in handling Big Date

  • Understanding of Data Ethics

  • Expertise in Data Mining

  • Capability in Predictive Analysis

  • Competence in Time Series Analysis and Forecasting

  • Proficient use of GitHub

Tools

PowerPoint

GitHub

Excel

Analysis Process

  1. Clean and integrate the dataset.

  2. Analyze the retention rates for different categories of users.

  3. Analyze which variables have the greatest impact on retention rates.

  4. To build a decision tree

Analysis

Analyze the retention rates

Using pivot table to analyze the retention rates for different categories of users.

Analyze the descriptive statistics for retained and departed customers.

Determine most impactful variables

Use the difference between Exited Customer and Total in each items for comparison. The four factors with the largest differences will be the top 4 factors that leading factors that contribute to client loss.

Most impactful variables are:

1. If customer is active member.

2.If number of products = 1

3.If customer is female.

4.If customer is from Germany

Build a decision tree