📊 Fraud Detection Project

📍 Overview

Welcome to the Fraud Detection Project! This project focuses on identifying fraudulent transactions using data science techniques. Conducted during my studies at Gomycode in Yaba, Nigeria, this project involved building a robust fraud detection system to accurately identify fraudulent activities from a dataset of financial transactions.

🛠️ Skills and Technologies Used

• Python 🐍: Primary programming language used for data manipulation, analysis, and model building.
• Pandas 📚: Data manipulation and analysis library.
• NumPy 🔢: Numerical computing library for handling arrays and mathematical operations.
• Plotly 🌈: Visualization of plots
• Scikit-Learn ⚙️: Machine learning library used for building and evaluating models.
• Matplotlib 📈 & Seaborn 🌈: Visualization libraries for creating charts and plots.
• Jupyter Notebook 📓: Interactive environment for developing and documenting code.
• Colab
• Vscode

🔍 Project Description

🚀 Objective

The main objective of this project was to detect fraudulent transactions using a machine learning model. Fraudulent transactions can cause significant financial losses, and detecting them accurately is crucial for financial institutions.

📊 Dataset The dataset used for this project included various features of financial transactions. Key attributes included:

Transaction Amount 💵

• Transaction Date 📅
• Transaction Location 📍
• Account Information 🏦
• Transaction Type 🔄
• and more

⚠️ Challenge: Data Imbalance

One of the primary challenges encountered was data imbalance. In fraud detection, fraudulent transactions are much rarer compared to legitimate ones. This imbalance can lead to a model that is biased towards predicting non-fraudulent transactions, reducing its effectiveness in detecting fraud.

⚙️ Solution: Undersampling

To address the data imbalance issue, we employed undersampling techniques. Here's how it was implemented:

Identify Majority and Minority Classes: The dataset was split into fraudulent and non-fraudulent transactions. Undersample Majority Class: The number of non-fraudulent transactions was reduced to match the number of fraudulent transactions, creating a balanced dataset.

Model Training: The balanced dataset was used to train various machine learning models.

🏗️ Methodology

• Data Preprocessing:
• Cleaning: Handled missing values and outliers.
• Feature Engineering: Created new features to enhance model performance.
• Normalization: Scaled numerical features for consistent model input.
• Exploratory Data Analysis (EDA):
• Visualization: Created histograms, scatter plots, and correlation matrices to understand data distributions and relationships.
• Model Building:
• Algorithms: Tested various algorithms including Logistic Regression, Decision Trees, and Random Forests.
• Evaluation Metrics: Used metrics such as Precision, Recall, F1-Score, and ROC-AUC to evaluate model performance.

Model Evaluation:

• Cross-Validation: Performed cross-validation to ensure the model generalizes well to unseen data.
• Hyperparameter Tuning: Optimized model parameters to improve accuracy.

Results:

Achieved a balanced model performance with significant improvement in detecting fraudulent transactions compared to the baseline model. s, from data preprocessing to model evaluation.

📈 Results and Insights

• Model Accuracy: Achieved an accuracy of X% with the undersampled dataset.
• Fraud Detection Rate: Improved detection of fraudulent transactions with a recall rate of Y%.