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Expense Tracker Dashboard

A Streamlit app that ingests messy transactions, cleans them, and visualizes spend so people make better money choices.

Tools: Python • Streamlit • Pandas • Plotly Role: Solo builder Users: Friends managing personal and small-business expenses

Problem

People track spending across banks and formats. Importing and cleaning that data is tedious and error-prone, which means insights arrive too late to change behavior.

Solution

I built a Streamlit app that imports CSV/Excel/JSON/PDF, standardizes columns, de-duplicates, and categorizes transactions with Pandas. Plotly charts highlight trends, categories, and outliers; the UI focuses on quick filters and summary KPIs.

Impact

Early users consolidated months of transactions in minutes and identified 2–3 categories to cut immediately. Duplicate detection reduced manual fixes and improved confidence in the numbers.

Role & Context

Solo project completed over ~3 weeks. Tested with friends who manage personal and small-business expenses. Tech: Python, Streamlit, Pandas, Plotly.

KEY FEATURES

  • Multi-Format Import: Supports CSV, Excel, JSON, TXT, Parquet, PDF, and ZIP formats
  • Automated Data Cleaning: Fuzzy matching for column mapping and data standardization
  • Interactive Dashboards: Visualizations for income vs. expenses, category breakdowns, and trends
  • Anomaly Detection: Identifies duplicate and anomalous transactions automatically
  • User Authentication: Secure user management with password hashing
  • Admin Panel: Comprehensive admin interface for user and data management
  • Data Profiling: Automated data quality reports using YData-Profiling and Sweetviz
  • Export Capabilities: Export cleaned data and reports in multiple formats

RESULTS AND INSIGHTS

  • Identified top spending categories and peak expense periods
  • Detected duplicate and anomalous transactions, reducing data errors
  • Visualized monthly savings trends, highlighting opportunities for budget optimization
  • Provided interactive dashboards for income vs. expenses analysis
  • Generated category breakdowns through pie charts and time series plots

SKILLS DEMONSTRATED

Data Cleaning Data Preprocessing Fuzzy Matching Multi-Format Data Import Data Profiling Anomaly Detection Time Series Analysis Data Visualization Interactive Dashboards User Authentication PDF Data Extraction Streamlit Development Data Validation Feature Engineering

TECHNOLOGIES USED

Python Streamlit Pandas NumPy Plotly Altair Openpyxl PyArrow PDFPlumber RapidFuzz YData-Profiling Sweetviz Passlib

Project Video

Gallery

Expense Tracker landing page showing Streamlit financial dashboard with transaction import and analysis features
Expense Tracker dashboard displaying income vs expenses visualization with spending categories
Financial dashboard showing expense trends, category breakdowns, and monthly spending patterns
Expense analysis dashboard with interactive charts for spending by category and time period
Financial insights dashboard showing savings trends and expense forecasting
Multi-format file upload interface supporting CSV, Excel, JSON, and PDF transaction files
Data import wizard with fuzzy matching for automatic column mapping and data cleaning
Transaction import process with data validation and preview before processing
Admin panel interface for managing users and monitoring expense tracker application
Administrative dashboard showing user management and system statistics
Admin control panel with data management and user activity monitoring
Admin interface displaying transaction data and user analytics
Administrative tools for data profiling and quality reports using YData-Profiling

CHALLENGES & SOLUTIONS

Challenge: Data quality issues with inconsistent formats and missing values in user-uploaded files
Solution: Implemented automated column mapping with fuzzy matching and robust data validation to handle diverse file formats and missing data.
Challenge: PDF extraction variability due to different bank statement layouts
Solution: Used multiple PDF extraction libraries (PDFPlumber and Tabula-py) with fallback mechanisms for better extraction accuracy.
Challenge: Performance degradation with very large datasets
Solution: Optimized data processing with chunk-based operations and efficient Pandas operations for scalability.

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