Expense Analysis

This repository contains a comprehensive analysis of personal expenses in 2023 using Python, pandas, and visualization libraries like matplotlib and seaborn.

Project Description

This project involves a comprehensive analysis of personal expenses for the year 2023 using Python and popular data analysis libraries. The purpose of this project is to gain insights into spending habits, identify areas for cost reduction, and visualize spending patterns through various data manipulation and visualization techniques.

Purpose and Problem Solved

Managing personal finances effectively requires a clear understanding of spending habits and trends. This project addresses the challenge of tracking and analyzing expenses by providing an automated, scalable solution that cleans, processes, and visualizes expense data. By transforming raw expense data into actionable insights, the project helps individuals make informed financial decisions, optimize their spending, and identify potential savings.

Key Features and Interesting Aspects

• Data Cleaning and Transformation: Efficiently cleans and transforms raw expense data for analysis.
• Statistical Analysis: Provides summary statistics, total, and average expenses by category.
• Outlier Detection: Identifies unusual spending patterns.
• Visualizations: Generates insightful visualizations, including bar plots, line plots, heatmaps, and pie charts.
• Cumulative Expense Tracking: Tracks cumulative expenses over time.

By automating the expense tracking process and generating detailed insights and visualizations, this project offers a robust framework for continuous financial monitoring and analysis, making it a valuable tool for personal finance management.

Technologies Used

• Python: Core programming language used for data manipulation and analysis.
• Pandas: Library used for data cleaning, transformation, and statistical analysis.
• Matplotlib: Library used for creating static, animated, and interactive visualizations.
• Seaborn: Library used for making statistical graphics in Python.
• Jupyter Notebook: Tool used for creating and sharing live code, equations, visualizations, and narrative text.

Project Structure

• first try/: Contains etl.py and pipeline.py which extracts data from "Justin Expenses 2023.csv", transforms and cleans the data and then loads it as a CSV file in the "end" directory .
• end/: Contains the results of the pipeline.
• 2nd stage/: Contains Jupyter notebooks for interactive analysis.
• README.md: Project documentation.