Building an Expense Tracker with Automatic Categorization Using Machine Learning | by Tanisha Rathore | Aug, 2024

Introduction

Managing personal finances effectively is crucial for everyone. With a multitude of transactions happening daily, categorizing expenses can become a tedious task. This project aims to automate the categorization of expenses using machine learning. We’ll build a Streamlit app that allows users to upload their expense data, train a model, and then use it to predict the category of new transactions in real-time.

Tech Stack Used

This project utilizes the following technologies:

• Python: The core programming language used for data manipulation, model training, and building the web application.
• Pandas: A powerful data manipulation library used to load, clean, and preprocess the data.
• Scikit-learn: A machine learning library that provides simple and efficient tools for data mining and data analysis, used here to train the Random Forest classifier.
• Streamlit: A fast and simple framework for creating data applications in Python, used to build the interactive web app.
• NumPy: Used for numerical operations and handling arrays in Python.
• Jupyter Notebook (optional): Used during the development phase for experimenting with data preprocessing and model training.

Project Overview

In this project, we’ll walk through the following steps:

1. Data Loading and Cleaning
2. Feature Engineering
3. Model Training
4. Building the Streamlit App for Real-Time Predictions

Step 1: Data Loading and Cleaning

We’ll start by loading the dataset and performing some basic cleaning tasks. The dataset is expected to be in CSV format.

Step 2: Feature Engineering

Feature engineering is a critical step in preparing the data for machine learning. We’ll extract useful features like the month and year from the Date column and encode categorical variables using one-hot encoding.

Step 3: Model Training

Now that our data is clean and well-prepared, we can proceed to train a machine learning model. We’ll use a Random Forest classifier to categorize expenses.

Step 4: Building the Streamlit App

The final step is to create an interactive web application using Streamlit. This app will allow users to upload their expense data, train a model, and make predictions in real-time.

Conclusion

This project demonstrates how to leverage machine learning to automate the categorization of expenses. By using a Random Forest classifier, we achieved a reliable prediction model that can be integrated into a user-friendly web application using Streamlit.

This application not only streamlines the process of managing expenses but also provides a hands-on experience in building and deploying a machine learning model.

Call to Action

If you have any questions or want to see my code, feel free to check out my GitHub repo “100DaysofBytewise” and connect with me on LinkedIn.

Stay tuned for more updates from my fellowship journey!