🌟 Fraud Detection in Application 🌟 Through Isolation Forest and K-Means Clustering, the project detects suspicious patterns like inconsistent income, duplicate entries, and unrealistic employment data. This end-to-end workflow transforms raw data into actionable fraud insights — enhancing trust and accuracy.

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This repository offers comprehensive projects on business analytics using Python, including big data analysis, data cleaning, importing/exporting, integration, quality assurance, time series analysis, EDA with Tableau, forecasting, regression, and sentiment analysis. Ideal for both beginners and experts.

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🔍 Detect fraud in application data using machine learning and data visualization to uncover anomalies and enhance digital integrity.