singhrajputvaishnavi26-ai/fraud-detection-online-transactions

# Fraud Detection in Online Transactions ## Project Overview ## Objectives * Analyze transaction data to identify fraud patterns * Detect suspicious and potentially fraudulent transactions * Perform data preprocessing and feature engineering * Build and evaluate machine learning classification models * Develop an interactive Streamlit application for fraud prediction * Generate actionable insights to support fraud-risk management ## Tools & Technologies * Python * Pandas * NumPy * Scikit-Learn * Matplotlib * Seaborn * Jupyter Notebook * Streamlit ## Project Workflow ### 1. Data Collection * Imported and examined transaction dataset ### 2. Data Cleaning & Preprocessing * Handled missing values and inconsistencies * Prepared data for machine learning analysis ### 3. Exploratory Data Analysis (EDA) * Analyzed transaction behavior and fraud distribution * Identified patterns and anomalies in the dataset ### 4. Feature Engineering * Selected and transformed relevant features * Prepared data for model training ### 5. Model Development * Trained machine learning models for fraud detection * Evaluated model performance using classification metrics ### 6. Streamlit Application Development * Built an interactive web application using Streamlit * Enabled users to enter transaction details and receive fraud predictions * Created a simple interface for real-time model interaction ### 7. Results & Insights * Evaluated model effectiveness in identifying fraudulent transactions * Generated insights to support fraud prevention strategies ## Key Features * Fraud prediction using Machine Learning * Interactive Streamlit web application * Data preprocessing and feature engineering * Exploratory Data Analysis (EDA) * Model evaluation and performance analysis * Real-time prediction interface ## Key Learning Outcomes * Data preprocessing techniques * Exploratory Data Analysis * Machine Learning model development * Fraud detection methodologies * Streamlit application development * Model evaluation and interpretation * Data-driven decision making ## Repository Contents * `app.py` – Streamlit application * `fraud_detection.ipynb` – Jupyter Notebook containing analysis and model development * `Fraud_Detection_Report.pdf` – Detailed project documentation * `dataset.csv` – Dataset used for analysis (if uploaded) * `README.md` – Project overview and documentation ## Author **Vaishnavi Singh Rajput**

标签：后端开发