Ai-Art-Trends-Analysis

Overview:

This project delves into the emerging trends in AI-generated art, analyzing a dataset that captures various aspects of this rapidly growing field. The analysis covers art styles, tools used, popularity across regions, and platforms where AI art is most prevalent. The goal is to identify key trends and patterns within AI-generated artwork, offering insights into the evolution of art through artificial intelligence.

Key Features:

1. Data Analysis: In-depth examination of the AI art trends dataset to uncover significant trends and patterns.
2. Data Visualization: Visualization of key insights through various charts and graphs, providing a clear understanding of AI art trends.
3. Machine Learning: Application of machine learning techniques to predict future trends and identify correlations within the dataset.

Installation and Usage:

To run the analysis, follow these steps:

1. Clone the repository:

        git clone https://github.com/yourusername/ai-art-trends-analysis.git
   

2. Navigate to the project directory:

        cd ai-art-trends-analysis
   

3. Install the required dependencies:

        pip install -r requirements.txt
   

4. Run the Jupyter notebook or script:

        jupyter notebook ai_art_analysis.ipynb
   

or

        python analysis.py

Dependencies:

1. Python 3.x
2. Jupyter Notebook
3. Pandas
4. Matplotlib
5. Seaborn
6. NumPy
7. Scikit-learn

Dataset:

The dataset used in this project includes various attributes related to AI-generated art, such as artwork style, tools used, popularity score, and regions where these artworks are trending. It provides a comprehensive view of the growing influence of AI in the art world.

Dataset Link: https://www.kaggle.com/datasets/waqi786/ai-generated-art-trends

Uploaded Date:

9/3/2024