Auction Data Analysis

Project Overview

The AuctionDataAnalysis class is used for preprocessing auction records and generating a data analysis report to facilitate understanding trends and data patterns within the auction records.

Class Methods:

1. __init__(file_path): Initializes the class with the specified dataset file path.
2. preprocess_data(): Preprocesses the dataset, including standardizing price formats and organizing movement orders.
3. get_author_name(): Retrieves the name of the report's author.
4. get_record_num(): Provides the count of auction records in the dataset.
5. get_top_artists(): Presents the total count of unique artists and lists the top ten contributing artists.
6. get_top_titles(): Displays the count of unique titles and highlights the most frequent five titles in the dataset.
7. visualize_period_statistics(): Generates a table detailing the Combined Price Statistics for Each Period (sorted in descending order by counts) and provides visual insights into Mean Price and Counts for Each Period.
8. visualize_movement_statistics(): Produces a table outlining the Combined Price Statistics for Each Movement (sorted in ascending order based on the historical timeline of movements) and showcases Mean Price and Counts for Each Movement.

Additional Information:

The file includes an appendix explaining the Historical Timeline of Movements.

Dataset Information:

The class is tested using the Sotheby's art price dataset from Kaggle.

Citation: Fl.Kuhm. (2022). Art Price Dataset [Dataset]. Kaggle. DOI

The dataset comprises artworks and sculptures listed for sale on Sothebys, encompassing artist names, prices, associated time periods, and art movements.