Bank Statement Analysis App

Overview

This Streamlit application enables users to upload their bank statements in PDF format and receive a comprehensive analysis of their transactions. The app extracts transaction data from the uploaded statement, categorizes the expenses, computes key financial metrics, and determines loan eligibility using a pre-trained Random Forest model. Additionally, it provides visualizations to help users understand their spending habits.

Features

• PDF Upload: Users can easily upload their bank statements in PDF format.
• PDF Parsing: Extracts text from the uploaded PDF.
• Transaction Processing: Identifies and processes transaction details including date, description, amount, and balance.
• Expense Categorization: Categorizes transactions into predefined categories like Credits, Payments, Bank Charges, etc.
• Key Metrics Calculation: Computes average daily expense, total expense, maximum expense, minimum expense, and the number of transactions.
• Loan Eligibility Prediction: Uses a Random Forest model to predict loan eligibility based on transaction history.
• Visualizations: Provides bar charts, pie charts, and line graphs to visualize expense distribution and trends.

How It Works

1. PDF Upload

   • Users upload their bank statement in PDF format using the file uploader interface.

2. PDF Parsing

   • The app extracts text from the PDF document using the pdfplumber library.

3. Transaction Processing

   • Extracted text is processed to identify individual transactions, capturing details such as date, description, amount, and balance.

4. Expense Categorization

   • Transactions are categorized based on their descriptions into predefined categories like Payments, Credits, Bank Charges, etc.

5. Key Metrics Calculation

   • The app calculates various financial metrics including average daily expense, total expense, maximum and minimum expense, and the number of transactions.

6. Loan Eligibility Prediction

   • A Random Forest model is used to predict loan eligibility based on features extracted from transaction data.

7. Visualizations

   • The app generates visualizations such as bar charts, pie charts, and line graphs to represent expense distribution and trends.

8. Hiding Streamlit Components

   • Customizes the UI by hiding default Streamlit components like the menu and footer for a cleaner look.

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)

Usage

1. Launch the app in your browser.
2. Upload a PDF bank statement.
3. Review the extracted transactions and categorized expenses.
4. View key metrics and visualizations.
5. Check loan eligibility based on the analyzed data.

Dependencies

• streamlit: For creating the web application interface.
• pdfplumber: For extracting text from PDF files.
• fitz (PyMuPDF): For additional PDF processing capabilities, such as extracting images or complex text layouts.
• re: For processing text and extracting transaction details.
• pandas: For data manipulation and analysis.
• plotly: For creating interactive visualizations.
• sklearn: For building and using the Random Forest model.

BLOG- https://link.medium.com/aoQxUW4gFMb