💸 Personal Expense Analytics Dashboard

An interactive, data-driven web dashboard built in Python to track, manage, and visualize personal expenses.

📌 Project Overview

This project was developed as a comprehensive exercise in Exploratory Data Analysis (EDA), Object-Oriented Programming (OOP) in Python, and Interactive Data Visualization. It takes raw user financial inputs and transforms them into actionable insights using industry-standard Data Science libraries.

The application natively handles data persistence via CSV and dynamically visualizes the data using a combination of Seaborn, Matplotlib, and Streamlit.

🚀 Key Features

• Object-Oriented Data Pipeline: Features a robust, modular ExpenseTracker backend (built with Pandas) to handle data ingestion, automated cleaning, and persistent storage.
• Exploratory Data Analysis (EDA): Programmatically groups, filters, and extracts key financial metrics (e.g., categorical spending distributions).
• Advanced Data Visualization: Leverages Matplotlib and Seaborn to render beautiful, responsive charts (Bar charts for relative spending, Pie/Donut charts for distributions).
• Interactive Web Interface: A sleek, user-friendly frontend built natively in Python using Streamlit, requiring no HTML/CSS background.
• One-Click Reset Mechanism: Safely clear all records and re-initialize the entire DataFrame state with robust error handling.

🛠️ Technology Stack

• Language: Core Python (OOP)
• Data Engineering & Manipulation: Pandas, NumPy
• Data Visualization: Matplotlib, Seaborn
• Web UI & Dashboarding: Streamlit

⚙️ How to Run Locally

1. Clone the repository:

   git clone https://github.com/Lipranj14/Expense-Analytics-Dashboard.git
   cd Expense-Analytics-Dashboard

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Streamlit application:

   streamlit run app.py

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Developed by Lipranj Daharwal