Fraud Detection Project 🕵️

This project focuses on using machine learning algorithms to detect fraud in financial transactions.

Overview 📋

The repository contains code for training and evaluating different models to detect fraudulent activities in transaction data. It aims to provide accurate fraud detection capabilities using supervised learning techniques.

Features 🛠️

•	Data Preprocessing🛠️: Cleaning, transforming, and preparing the dataset for analysis.

•	Model Training📚: Training classifiers including Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbors.

•	Model Evaluation📊: Assessing model performance using accuracy score, ROC curve, Precision-Recall curve, and confusion matrix.

•	Visualization 📊: Visualizing model comparisons, feature importance, and evaluation metrics using matplotlib and seaborn.

•	Deployment🚀: Options for deploying trained models in production environments for real-time fraud detection.

Files 📁

•	fraud_detection.ipynb: Jupyter Notebook with code for data preprocessing, model training, evaluation, and visualization.

•	data.csv: Sample dataset used for training and testing.

•	README.md: This file providing an overview of the project.

Results 📈

•	RandomForest 🌲: Accuracy - 98%

•	DecisionTree 🌳: Accuracy - 97%

•	Logistic Regression 📈: Accuracy - 98%

•	KNN 📏: Accuracy - 98%

Additional Notes 📝

•	The dataset used in this project contains simulated transaction data.

•	The models are trained and evaluated on a balanced dataset with synthetic fraud cases.