BSc IT Project Guide: Outlier Detection
1. Project Title
Outlier Detection to Identify and Handle Outliers in Data
2. Project Overview
Outlier Detection is essential for identifying anomalies or unusual observations that deviate significantly from the rest of the data. This project involves developing a system that applies statistical and machine learning methods to detect and manage outliers in datasets, improving data quality and ensuring better model accuracy.
3. Objectives
- Implement methods to detect outliers in various types of datasets.
- Apply statistical, clustering, and machine learning techniques for outlier detection.
- Visualize detected outliers using graphs and charts.
- Provide options for users to handle or remove detected outliers.
4. Modules
- Data Import Module
- Data Visualization Module
- Outlier Detection Algorithms (e.g., Z-score, IQR, DBSCAN, Isolation Forest)
- Outlier Handling Module (remove, replace, or flag)
- Report Generation Module
5. Software and Hardware Requirements
Software Requirements:
- Python, Jupyter Notebook
- Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn
- Web framework (optional): Flask or Streamlit
Hardware Requirements:
- 4 GB RAM minimum
- 100 GB Hard Disk
- Intel i3 processor or above
6. SDLC Model Used
Waterfall Model
7. Methodology
This project uses an exploratory data analysis approach to first visualize the data, followed by applying outlier detection techniques. Different algorithms will be compared to choose the most effective ones for given data types.
8. Future Scope
- Integration with automated data cleaning pipelines.
- Expansion to support real-time outlier detection.
- Incorporation of deep learning-based anomaly detection.