Analyzing Banking Customer Transaction History using Streamlit and AWS Cloud

As a banking institution, analyzing customer transaction history can provide valuable insights into their spending behavior, allowing for personalized financial advice and product recommendations. In this article, we will explore how to build a web application using Streamlit and deploy it to AWS Cloud to analyze customer transaction history.

Sample Dataset

We will use a sample dataset containing transactional data for 1000 customers over a period of 6 months. The dataset includes the customer ID, transaction date, transaction amount, and transaction type.

import pandas as pd 

#Load the dataset

df = pd.read_csv('customer_transactions.csv')

# Print the first 5 rows of the dataset

print(df.head())

Building the Streamlit Web Application

Streamlit is a powerful Python library that allows for easy and interactive data exploration. We can use it to build a web application that visualizes the patterns and trends in customer transaction history.

First, let's install Streamlit using pip:

!pip install streamlit

Next, we can create a Python script that uses Streamlit to build the web application:

import streamlit as st

# Load the dataset

df = pd.read_csv('customer_transactions.csv')

# Add a title to the web application

st.title('Customer Transaction History Analysis')

# Add a sidebar with filters

st.sidebar.title('Filters')

customer_ids = st.sidebar.multiselect('Select Customer IDs', df['Customer ID'].unique())

transaction_types = st.sidebar.multiselect('Select Transaction Types', df['Transaction Type'].unique())

# Filter the dataset based on the selected filters

filtered_df = df[(df['Customer ID'].isin(customer_ids)) & (df['Transaction Type'].isin(transaction_types))]

# Display the filtered dataset

st.write('### Transaction History')

st.write(filtered_df)

This code will create a web application with a title and a sidebar that allows users to select filters based on customer IDs and transaction types. The dataset will be filtered based on the selected filters, and the filtered dataset will be displayed.

Deploying to AWS Cloud

Now that we have built our Streamlit web application, we can deploy it to AWS Cloud using Amazon Elastic Beanstalk.

First, we need to create an Elastic Beanstalk environment. We can do this using the AWS Management Console or the AWS CLI.

Once we have created our Elastic Beanstalk environment, we can deploy our Streamlit web application using the AWS CLI:

!eb init

!eb create

!eb deploy

This code will initialize our Elastic Beanstalk environment, create a new application version, and deploy the application to the environment.

Using Machine Learning for Customer Transaction History Analysis

In addition to using Streamlit and AWS Cloud for customer transaction history analysis, we can also use machine learning to gain deeper insights into customer spending behavior.

For example, we can use clustering algorithms such as k-means to group customers based on their spending behavior. This can help us identify segments of customers with similar spending patterns, allowing for more targeted financial advice and product recommendations.

We can also use predictive modeling techniques such as regression analysis to predict future spending behavior based on past transaction history. This can help us anticipate customer needs and proactively offer relevant financial products and services.

Conclusion

In this article, we learned how to build a web application using Streamlit and deploy it to AWS Cloud to analyze customer transaction history. By visualizing the patterns and trends in customer spending behavior, banking institutions can provide personalized financial advice and product recommendations to their customers. Additionally, by leveraging machine learning techniques, we can gain deeper insights into customer spending behavior and offer more targeted financial products and services.