feat(anomaly): add project dependencies and module structure for anomaly detection system

Author: artemisTurintech

README.md

@@ -1,14 +1,58 @@
-# template-python
+# Anomaly Detection Project
 
-A simple Python template repository using modern tooling with `uv` and `pyproject.toml`.
+A Python-based anomaly detection system using machine learning algorithms to identify
+outliers in CSV data. Built with modern tooling using `uv` and `pyproject.toml`.
 
 ## Overview
 
-This template provides a minimal starting point for Python projects using:
+This project implements multiple anomaly detection algorithms using scikit-learn
+and provides visualization tools for analyzing results. It uses:
 - **uv**: Fast Python package manager and project manager
 - **pyproject.toml**: Modern Python project configuration (PEP 518/621)
 - Python 3.13+
 
+## Dependencies
+
+This project uses the following libraries:
+
+### Core ML and Data Processing
+
+- **scikit-learn** (>=1.3.0)
+  - **Purpose**: Provides machine learning algorithm implementations
+  - **Key Functionality**: 
+    - Isolation Forest algorithm for anomaly detection
+    - One-Class SVM for outlier detection
+    - Model training, prediction, and evaluation utilities
+  - **Version Constraint**: Requires 1.3.0+ for latest algorithm improvements
+
+- **pandas** (>=2.0.0)
+  - **Purpose**: CSV file handling and data manipulation
+  - **Key Functionality**:
+    - Efficient CSV file reading and writing
+    - DataFrame operations for data preprocessing
+    - Data cleaning, filtering, and transformation
+    - Handling missing values and data validation
+  - **Version Constraint**: Requires 2.0.0+ for improved performance and API consistency
+
+- **numpy** (>=1.24.0)
+  - **Purpose**: Numerical operations and array manipulations
+  - **Key Functionality**:
+    - Array operations for data processing
+    - Mathematical computations
+    - Required dependency for scikit-learn
+  - **Version Constraint**: Requires 1.24.0+ for compatibility with scikit-learn
+
+### Visualization
+
+- **matplotlib** (>=3.7.0)
+  - **Purpose**: Static visualization generation
+  - **Key Functionality**:
+    - Creating plots and charts for anomaly detection results
+    - Scatter plots with anomaly highlighting
+    - Time-series visualizations
+    - Exporting visualizations to various formats (PNG, PDF, SVG)
+  - **Version Constraint**: Requires 3.7.0+ for latest plotting features
+
 ## Prerequisites
 
 Install `uv` if you haven't already:
@@ -28,7 +72,7 @@ For more installation options, see the [uv documentation](https://docs.astral.sh
 
 ## Setup
 
-1. Clone or use this template:
+1. Clone or use this repository:
    ```bash
    git clone <repository-url>
    cd template-python
@@ -39,24 +83,45 @@ For more installation options, see the [uv documentation](https://docs.astral.sh
    uv sync
    ```
 
-## Running the Template
+## Running the Project
 
 Run the main script:
 
 ```bash
 uv run python main.py
 ```
 
-This will execute the simple example in `main.py` which prints a greeting.
+This will execute the anomaly detection pipeline defined in `main.py`.
 
 ## Project Structure
 
 ```
-template-python/
-├── .git/                 # Git repository
-├── .gitignore           # Python-specific ignore patterns
-├── .python-version      # Pinned Python version (3.13)
-├── pyproject.toml       # Project configuration and dependencies
-├── main.py              # Main entry point
-└── README.md            # This file
-```
+project_root/
+├── .git/                      # Git repository
+├── .gitignore                 # Python-specific ignore patterns
+├── .python-version            # Pinned Python version (3.13)
+├── pyproject.toml             # Project configuration and dependencies
+├── uv.lock                    # Locked dependency versions
+├── main.py                    # Main entry point
+├── README.md                  # This file
+└── src/                       # Source package
+    ├── __init__.py            # Package initialization
+    ├── data_loader.py         # CSV loading and preprocessing
+    ├── anomaly_detection.py   # ML algorithm implementations
+    └── visualization.py       # Chart generation
+```
+
+## Module Overview
+
+### `src/data_loader.py`
+Handles CSV file loading and data preprocessing using pandas. Provides utilities
+for data validation, cleaning, feature extraction, and normalization.
+
+### `src/anomaly_detection.py`
+Implements machine learning algorithms for anomaly detection:
+- **Isolation Forest**: Detects anomalies by isolating observations
+- **One-Class SVM**: Learns boundaries of normal data to identify outliers
+
+### `src/visualization.py`
+Generates static visualizations using matplotlib for anomaly detection results,
+including scatter plots, time-series charts, and comparison visualizations.

pyproject.toml

@@ -4,4 +4,9 @@ version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.13"
-dependencies = []
+dependencies = [
+    "scikit-learn>=1.3.0",
+    "pandas>=2.0.0",
+    "matplotlib>=3.7.0",
+    "numpy>=1.24.0",
+]

src/__init__.py

@@ -0,0 +1,14 @@
+"""
+Anomaly Detection Package
+
+This package provides tools for detecting anomalies in CSV data using
+machine learning algorithms. It includes modules for data loading,
+anomaly detection using various ML algorithms, and visualization of results.
+
+Modules:
+    - data_loader: CSV file loading and data preprocessing
+    - anomaly_detection: Machine learning algorithm implementations
+    - visualization: Chart generation for anomaly detection results
+"""
+
+__version__ = "0.1.0"

src/anomaly_detection.py

@@ -0,0 +1,14 @@
+"""
+Anomaly Detection Module
+
+This module implements machine learning algorithms for anomaly detection using
+scikit-learn. It provides implementations of:
+    - Isolation Forest: An ensemble method for detecting anomalies by isolating
+      observations through random feature selection and splitting
+    - One-Class SVM: A support vector machine algorithm that learns the boundary
+      of normal data points and identifies outliers
+    
+The module provides a unified interface for training models, making predictions,
+and evaluating anomaly detection performance. It leverages numpy for numerical
+operations and scikit-learn's robust ML implementations.
+"""

src/data_loader.py

@@ -0,0 +1,14 @@
+"""
+Data Loader Module
+
+This module handles CSV file loading and data preprocessing for anomaly detection.
+It uses pandas for efficient data manipulation and provides utilities for:
+    - Loading CSV files into pandas DataFrames
+    - Data validation and cleaning
+    - Feature extraction and preprocessing
+    - Handling missing values and data normalization
+    - Data transformation for ML algorithms
+
+The module ensures data is properly formatted and ready for anomaly detection
+algorithms to consume.
+"""

src/visualization.py

@@ -0,0 +1,14 @@
+"""
+Visualization Module
+
+This module handles the generation of static visualizations for anomaly detection
+results using matplotlib. It provides functionality for:
+    - Plotting anomaly scores and detection results
+    - Creating scatter plots with anomalies highlighted
+    - Generating time-series visualizations with anomaly markers
+    - Creating comparison charts for multiple detection algorithms
+    - Saving visualizations to various file formats (PNG, PDF, SVG)
+    
+The module uses matplotlib to create clear, publication-ready charts that help
+users understand and interpret anomaly detection results.
+"""