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Personal Finance Tracker with AI Insights

Track your daily expenses from the command line using C++ with built-in AI insights, and visualize monthly spending through an interactive Python dashboard.

📌 Features

🖥️ C++ CLI Application

  • Add expenses with date, category, amount, and monthly budget
  • View all expenses
  • Filter by category
  • View total spending vs. budget
  • Delete incorrect entries
  • Stores data in expenses.csv (CSV format)

🤖 AI-Powered Analytics (C++ - from scratch)

  • 📈 Linear Regression – Predict next month's expense based on past spending
  • 📊 K-Means Clustering – Classify your spending behavior as Frugal, Balanced, or Overspender
  • 🧠 Naive Bayes Classifier – Predict the most likely category for a new expense based on amount
  • 📉 Statistical Summary – Calculate and display mean, variance, and standard deviation of expenses
  • 📂 Category Probability Distribution – Visualize how frequently each category is used

📊 Python Dashboard

  • Reads data from the CSV file
  • Displays interactive bar and pie charts using Dash & Plotly
  • Launches a local server (localhost:8050) for real-time insights

📁 Project Structure

ExpenseTracker/
│
├── cpp/
│   └── expense_entry.cpp       # C++ source file (main CLI logic)
│
├── python/
│   └── dashboard.py               # Dash dashboard for visualization
│
├── data/
│   └── expenses.csv               # CSV file storing expenses

📸 CLI Working Screenshots

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➕ Add Expense

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📋 View All Expenses

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🧾 Spending Summary

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🔍 Filter By Category

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❌ Delete Expenses

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🔍 Predict Next Month Expenses (AI Analytics)

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📊 Show Category Probabilities (AI Analytics)

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📈 Show Expense Statistics (AI Analytics)

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🤖 Classify Spending Habit (AI Analytics)

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🧠 Predict Category using Naive Bayes

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❌ Exit

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📊 Dashboard Preview

🖥️ Preview for April-25

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🖥️ Preview for May-25

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🖥️ Preview for June-25

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About

A personal finance tracker using C++ for CLI-based expense logging and Python Dash for interactive monthly visualizations. Stores data in CSV format and displays spending insights via bar and pie charts, combining core programming with data analytics.

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