🛡️ FinSecure: Transaction Anomaly Detection

📌 Project Overview

Digital financial security is a paramount challenge. This project implements a robust Machine Learning Pipeline using Logistic Regression to detect anomalies and fraudulent patterns within financial transaction datasets.

This work specifically demonstrates how predictive modeling can act as a critical security layer, which is directly relevant to high-security authentication systems like those managed by UIDAI (Aadhaar).

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🚀 Technical Implementation

1. Data Processing (Pandas & NumPy)

• Data Ingestion: Utilized Pandas to efficiently load and manage large-scale transactional records (CSV).
• Array Manipulation: Employed NumPy for sophisticated numerical analysis and mathematical operations on the dataset.

2. Machine Learning Pipeline (Scikit-learn)

• Train-Test Split: Used train_test_split to unbiasedly partition the data, ensuring robust model validation.
• Classification Modeling: Trained a LogisticRegression classifier to distinguish between legitimate transactions and potential fraud (anomalies).
• Performance Evaluation: Evaluated the model’s reliability using accuracy_score to determine its predictive success.

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🛠️ Tech Stack & Skills

Domain	Tools & Technologies
Language	Python 3.x
Libraries	Pandas (Data Manipulation), NumPy (Numerical Analysis), Scikit-Learn (Modeling & Metrics)
Platform	Google Colab, GitHub

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📂 Project Links

• Dataset/ Code:: https://colab.research.google.com/drive/1LTI8O69ChF-Bw_fTjB2y8FlCl-BJ_d1T?usp=sharing

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Developed by: Abhay Garg | B.Tech 3rd Year