I will build python anomaly detection and outlier analysis models

Are unusual spikes, fraudulent patterns, or sensor irregularities corrupting your data? I help businesses and researchers uncover hidden patterns by building robust anomaly detection and outlier analysis models in Python.

Whether you have financial transactions, time-series metrics, logs, or tabular business data, I develop custom machine learning pipelines tailored to your specific problem.

What I Can Do For You:

• Statistical Outlier Detection: Z-Score, IQR, Mahalanobis distance
• Machine Learning Models: Isolation Forest, One-Class SVM, Local Outlier Factor (LOF), DBSCAN
• Deep Learning & PyOD: Autoencoders, LSTM for sequential/time-series data, PyOD library
• Data Cleaning & EDA: Feature engineering, missing value handling, and normalization
• Visual Reports: Interactive Plotly/Matplotlib plots highlighting detected anomalies
• Deployment Ready: Clean Jupyter Notebooks, reusable Python scripts, or FastAPI endpoints

Why Work With Me?

• Clean, well-commented, reproducible code
• Fast turnaround and clear communication
• Focus on business accuracy, not just theoretical models

Note: Every dataset is unique! Please message me before ordering so we can discuss your data format and project goals.