Getting Started

Follow the steps below to set up and run the project on your local machine.

1. Prerequisites

• Python 3.8 or later
• PostgreSQL
• pip (Python package installer)

2. Installation

Clone the project

• git clone https://github.com/eraybd/cnc-project.git

Install the required Python packages

• cd cnc-project
• pip install -r requirements.txt

3. Configuration

Create database

• Create database named as "anomaly_graph" (if a change is made, it must also be changed in the code as well)

Modify config.py

• Change the relevant part in the “config.py” according to your PostgreSQL setup
  
• SQLALCHEMY_DATABASE_URI = 'postgresql://username:password@localhost/anomaly_graph'

4. Running the Application

Run the project

• python run.py

5. Using the Application

Graph Generation

• On the home page, select the data source type and configure the number of rows and columns for displaying graphs.
• Choose the X and Y axes from the dropdown menus.
• Select the type of graph you want to generate (scatter, bar, line, or histogram).
• Click "Grafiği Göster" (Show the Graph) to generate the graph.
• Use the "Sıfırla" (Reset) button to clear the generated graphs.

Anomaly Detection

• The "Anomaly Tablosu" (Anomaly Table) section displays data from the database where anomalies are detected.
• The table can be sorted by clicking on the column headers.

Feature Comparison

• In the "Feature Seçimi" (Feature Selection) section, select the features you want to compare between train and test datasets.
• The graphs are updated dynamically based on the selected features and month.

Prediction vs Actual Comparison

• In the "Actual ve Prediction Değerleri" (Actual and Prediction Values) section, select features, model, and month to compare the actual vs. predicted data.
• The graphs update automatically based on your selections.

6. Project Screenshots