Qsvc

Project Description

Qsvc is a powerful tool designed for efficient data processing and visualization. It provides robust functionalities to streamline data-related tasks, making it easier for users to manage and analyze their datasets.

Setup Instructions

1. Clone the repository:

   git clone https://github.com/dioskit/Qsvc.git
   cd Qsvc

2. Install the required dependencies:

   pip install -r requirements.txt

3. Prepare data: Ensure that you have the necessary datasets available in the data/ directory.

Dataset Documentation

The datasets used in this project are classified into different categories:

• Train Data: Data used for training the model.
• Test Data: Data used for testing the model's performance.

Each dataset has specific features outlined in the respective documentation files located in the data/docs/ directory.

Notebook Guides

The project includes Jupyter notebooks that provide hands-on experience:

• notebook1.ipynb: A guide to exploring the dataset.
• notebook2.ipynb: A guide to training the model.

To run the notebooks, launch Jupyter Lab:

jupyter lab

Preprocessing Steps

The data preprocessing involves the following key steps:

1. Data Cleaning: Remove missing or incorrect values.
2. Feature Engineering: Create new features based on existing data.
3. Data Normalization: Normalize the data for better performance.

Refer to notebooks/preprocessing.ipynb for a detailed walkthrough of this process.

Results

The model's results are evaluated based on:

• Accuracy
• Precision
• Recall

Results are compiled in results/ folder with visualizations available in the notebooks.

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Note: This README is subject to updates as the project evolves.