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can be time-consuming and prone to errors. To
address these challenges, the study proposes a
machine learning-based approach to automate the
process of expense tracking. The system utilizes
various supervised and unsupervised learning
techniques such as decision trees, neural networks,
and clustering algorithms. These techniques are used
to analyze historical data on personal expenses, such
as the amount spent, the category of expenses, and the
frequency of expenses. The analysis helps to identify
patterns and trends in the data, which can be used to
predict future expenses and provide personalized
insights into personal finance management (Lu et al
2019), (Mithun et al 2019). The study involves the
development and evaluation of a prototype system
that uses machine learning algorithms to categorize
and predict expenses based on past spending patterns
and other relevant features (Park and Lee 2020). The
system aims to provide a more accurate and efficient
means of tracking personal expenses while reducing
the manual effort required for data entry. The system
is evaluated using real-world data collected from a
sample of individuals, and the results of the study are
used to inform the development of more effective and
efficient tools for personal finance management. The
research is grounded in the principles of data mining,
statistical analysis, and machine learning, with a
focus on the application of these techniques to
personal finance management. The results of the
study can potentially contribute to the development of
more sophisticated and effective financial technology
tools that can help individuals better manage their
finances (Shim & Han 2019), (Wang et al. 2019).
5 RESEARCH METHODOLOGY
Data Collection
Collecting data from different sources, such as bank
statements, receipts, invoices, etc., helps to get a
comprehensive view of one's expenses.
APIs or web scraping tools can automate data
collection from online sources, reducing manual
effort and errors.
It is essential to ensure data privacy and security
while collecting data, such as using encryption or
anonymization techniques.
Preprocessing data during the collection stage,
such as standardizing date formats, can simplify later
stages of data cleaning and transformation. Regularly
collecting and updating data can improve the
accuracy and timeliness of expense tracking.
Data Preprocessing
Data preprocessing involves cleaning, transforming,
and preparing raw data for machine learning
algorithms. Techniques such as removing duplicates,
filling in missing values, and correcting errors can
improve the quality of data.
Normalizing and scaling the data features can
prevent bias and improve model performance.
Feature engineering involves extracting useful
features, such as transaction category, merchant
name, or date/time features, that can help classify
expenses accurately.
Exploratory data analysis can help identify
patterns, trends, and outliers in the data, which can
guide data preprocessing and feature engineering.
Feature Extraction
Feature extraction involves converting raw data into
numerical or categorical features that machine
learning algorithms can use. Techniques such as bag-
of-words, TF-IDF, or word embeddings can extract
features from text data, such as merchant names or
transaction descriptions.
Feature selection techniques such as mutual
information, chi-squared test, or PCA can reduce the
dimensionality of the feature space and improve
model performance.
Domain knowledge and user feedback can help
identify relevant features and refine feature extraction
techniques.
Feature extraction is an iterative process that can
benefit from feedback loops and continuous
improvement.
Model Selection
Model selection involves choosing a suitable machine
learning algorithm, such as logistic regression,
decision trees, or neural networks, based on the
problem's requirements and data characteristics.
Considerations such as model complexity,
interpretability, and generalization ability can guide
model selection. Cross-validation techniques such as
k-fold or leave-one-out can evaluate model
performance and prevent over fitting or under fitting.
Ensemble techniques such as bagging, boosting, or
stacking can combine multiple models to improve
performance.
Regularization techniques such as L1 or L2
regularization can prevent model over fitting and
improve model stability.
Model Training
Model training involves fitting the machine learning
algorithm to the training data to learn the underlying
patterns and relationships. Optimization algorithms
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