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https://github.com/robinmillford/analyzing-banking-trends-customer-transactionns-and-regional-impact

In this project, I embarked on a journey to analyze banking trends, customer transactions, and regional impacts on a financial dataset. My goal was to gain valuable insights into customer behavior, regional distribution, and transaction patterns
https://github.com/robinmillford/analyzing-banking-trends-customer-transactionns-and-regional-impact

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In this project, I embarked on a journey to analyze banking trends, customer transactions, and regional impacts on a financial dataset. My goal was to gain valuable insights into customer behavior, regional distribution, and transaction patterns

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# Analyzing-Banking-Trends-Customer-Transactions-and-Regional-Impact

**Project Summary**



In this project, I embarked on a journey to analyze banking trends, customer transactions, and regional impacts on a financial dataset. My goal was to gain valuable insights into customer behavior, regional distribution, and transaction patterns. Here's what I discovered throughout this project:



1. **Data Cleaning and Preprocessing:**

   - I began by cleaning and preprocessing the raw data. This involved removing duplicate records, handling missing values, and renaming columns to improve clarity and consistency.



2. **Customer Transactions:**

   - I analyzed customer transactions, focusing on deposit amounts. I identified the user who made the largest deposit in the "Australia" region.



3. **Regional Impact:**

   - I explored the regional impact by calculating the total amount deposited for each region. This allowed me to understand the financial dynamics in different geographical areas.



4. **Customer Insights:**

   - I calculated the total number of transactions for each region, providing insights into transaction volumes across different areas.



5. **User and Region Relationships:**

   - I examined the relationship between users and regions, finding out how many consumers are allocated to each region and the regions with the highest number of assigned nodes.



6. **Transaction Types:**

   - I delved into transaction types, discovering the unique count and total amount for each transaction type, with a particular focus on 'Deposit' transactions.



7. **Customer Engagement:**

   - I determined the total number of users who made more than 5 transactions, shedding light on customer engagement and activity levels.



8. **Top Customers:**

   - I identified the top 5 consumers who made the highest total transaction amounts, recognizing valuable customers based on their financial contributions.



**Tableau Visualizations:**



To provide a visual dimension to the project insights, I also created interactive Tableau visualizations. These visualizations included:

- Bar charts illustrating transaction-type statistics.

- Pie charts depicting the distribution of customer engagement levels.



Tableau- https://public.tableau.com/app/profile/yamin3547/viz/AnalyzingBankingTrendsCustomerTransactionnsandRegionalImpact/Dashboard1



These visualizations not only enhanced the project's storytelling but also made it easier to communicate complex data-driven insights to stakeholders.



This project provided comprehensive insights into the banking dataset, allowing me to understand customer behavior, regional disparities, and transaction patterns. These insights can be invaluable for decision-making, customer segmentation, and strategic planning within the financial sector.