Art Auction Houses Data Science Project in Python

Art Auction Case Study:

Art auction application provides buying and selling of artworks. The normal way of sale of an artwork is auction places buy artworks from the art owners and sell it to the customers who bid more. The bidder bids a maximum price based on the art’s Naturality, quality, and artist value.
The dataset provided contains details of art auctions from various art houses and the details about the art piece auctioned and the other details about the auction


We would like you to perform an exploratory analysis on the dataset provided. Please take a shot at analyzing this dataset using any tool of your choice and create a summary of key insights and analysis charts.
The questions to be solved are:
a) How many art pieces were auctioned in zip codes mentioned?
b) Which country has contributed most in the auction?
c) Highlight the states which have sold most of the paintings?
d) How many paintings were made between the year 1947-1950?
e) Which artist has sold the most number of paintings?
f) Which dimensions of the art were used the most?
g) Categorize according to the sales of art in terms of year in which they were made?
h) List out the top 10 buyers.
i) Distribution of Acquisition price of art over the country.
j) How many arts are insured above the average minimum insurance price?
k) How much difference is there for actual cost and buyers cost?
l) How many arts were sold online according to year?
m) Which artist got the highest rating below 5?
n) In which year most of the arts were the good purchase?
o) Design out a predictive model to show whether the purchases were good purchases or not?
You can use your own way of visualization and Modelling.

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