Skip to content

Latest commit

 

History

History
66 lines (46 loc) · 3.21 KB

README.md

File metadata and controls

66 lines (46 loc) · 3.21 KB
Raw

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