📊 Personal Expense Tracker Pro

A full-stack Python-based Personal Finance Management System with authentication, SQLite database, and interactive Streamlit dashboard for tracking income, expenses, savings, and financial trends.

🚀 Project Overview

The Personal Expense Tracker Pro helps users track their monthly income and expenses across multiple categories, visualize spending patterns, and analyze savings trends over time.

It is designed as a real-world data analytics + finance project using Python and SQLite.

🎯 Problem Statement

Most individuals:

• Do not track daily/monthly expenses properly
• Do not know where their money is spent
• Lack financial insights and budgeting tools

This system solves that by providing:

• Structured expense tracking
• Category-wise spending analysis
• Monthly financial dashboard
• Login-based personal data security

💡 Features

✔ User Registration & Login System
✔ Secure SQLite Database Storage
✔ Monthly Income Tracking
✔ Category-wise Expense Tracking
✔ Savings Calculation
✔ Monthly Dashboard View
✔ Expense Distribution Pie Chart
✔ Savings Trend Line Chart
✔ Highest Spending Category Detection
✔ Personal Financial Insights

🛠️ Tech Stack

• Python 🐍
• Streamlit 🎯
• SQLite 🗄️
• Pandas 📊
• Matplotlib 📈

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🗂️ Project Structure

Personal_Expense_Tracker/ │ ├── app.py # Streamlit frontend ├── database.py # SQLite backend logic ├── expense_tracker.db # Database file │ ├── requirements.txt # Dependencies ├── README.md # Documentation

⚙️ Installation & Setup

1. Clone Repository

git clone https://github.com/your-username/expense-tracker.git
cd expense-tracker

2. Create Virtual Environment
python -m venv venv

Activate:
Windows:venv\Scripts\activate
Mac/Linux:source venv/bin/activate

3. Install Dependencies
pip install streamlit pandas matplotlib

4. Run Application
streamlit run app.py

📊 How It Works
User registers/login
Enters monthly income & expenses
Data is stored in SQLite database
System calculates:
Total income
Total expense
Savings
Dashboard displays:
Charts
Tables
Insights

📈 Sample Insights
Highest spending category detection
Monthly savings trend
Expense distribution breakdown
Financial health status

🧠 Learning Outcomes
This project demonstrates:

CRUD operations in SQLite
User authentication system
Data visualization with Matplotlib
Streamlit dashboard development
Financial data analysis
Real-world project structuring

📸 Screenshots
Login Page
Dashboard
Pie Chart
Monthly Trend

🔥 Future Improvements
Budget limit alerts
Export to Excel/PDF
AI-based spending prediction
Mobile app version
Cloud database integration

👨‍💻 Author
Anjali Meshram


⭐ If you like this project
Give a ⭐ on GitHub and share it!