Financial Analysis & Fraud Prediction

A end-to-end data science project analyzing financial transactions, performing exploratory data analysis (EDA), and modeling fraud detection algorithms.

---

📌 Project Overview

Briefly describe the objective of this project in 2–3 sentences.

• Data Cleaning & Merging: Processing raw transaction data.
• Exploratory Data Analysis (EDA): Identifying spending trends and anomaly distributions.
• Financial Analysis: Deep dive into merchant categories and user behaviors.
• Fraud Prediction: Building machine learning models to identify fraudulent transactions.

---

🗂️ Repository Structure

│── notebooks/ │ ├── 01_data_cleaning_and_merging.ipynb │ ├── 02_exploratory_data_analysis.ipynb │ ├── 03_financial_transactions_analysis.ipynb │ └── 04_fraud_prediction_and_modeling.ipynb │── README.md

---

📊 Key Insights & Results

• Insight 1: Add 1–2 key highlights or metrics from your analysis here (e.g., Fraud model achieved 92% ROC-AUC score).
• Insight 2: High-risk transaction patterns identified during peak hours.

---

🛠️ Tech Stack & Tools

• Language: Python 3.x
• Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn
• Environment: Jupyter Notebooks