The Outlier Detection Tool identifies anomalies in datasets using statistical methods and visualization techniques. It helps analysts, researchers, and businesses detect unusual data points that may indicate errors, fraud, or significant deviations from the norm.

Key Features

✅ Supports Multiple Detection Methods – Identify outliers using Z-score, IQR (Interquartile Range), and Standard Deviation.
✅ Customizable Sensitivity Levels – Adjust detection thresholds to refine analysis.
✅ Graphical Visualization – Displays box plots, histograms, and scatter plots for easy interpretation.
✅ Bulk Data Processing – Upload large datasets for batch outlier detection.
✅ PDF Report Generation – Export detailed results, including identified outliers and visual reports.

Use Cases

📌 Data Cleaning & Preprocessing – Remove anomalies before statistical modeling or machine learning.
📌 Fraud Detection – Identify unusual transactions in financial data.
📌 Quality Control & Manufacturing – Detect defects and process variations.
📌 Medical & Scientific Research – Find extreme values in experimental data.

How It Works

1️⃣ Upload or Enter Data – Input a dataset manually or upload a CSV file.
2️⃣ Choose Detection Method – Select from Z-score, IQR, or standard deviation-based detection.
3️⃣ Run Analysis – The tool scans for outliers and highlights them in tables and charts.
4️⃣ Review & Interpret Results – Use visual plots and statistical summaries to analyze the anomalies.
5️⃣ Download PDF Report – Export a full analysis report with findings and recommendations.

This tool simplifies outlier detection, ensuring data integrity and improving analysis accuracy. 🚀