Prediction of Breast Cancer Data Science Project in Python

The Prediction of Breast Cancer is a data science project and its dataset includes the measurements from the digitized images of needle aspirate of breast mass tissue.

The data has 100 examples of cancer biopsies with 32 features. From these features, we can predict whether the tumors are benign or malignant.

The steps to be done are:

  • Data Preparation by checking the target variable and removing the ID variable.
  • Visualization of the frequency of the type variable.
  • Normalizing the variables by using min-max normalizations and z-score methods.
  • Bifurcation of the train and test sets.
  • Prediction of the type of tumors using the classification algorithm.
  • Improving the models using different values of k and other methods of normalizations.

Other data Science Projects using python below:

1) Marketing Campaigns Prediction of the clientele subscribing to services in Bank.
2) Market Basket Analysis for the creation of Online Recommender System for Grocery Supermarket.
3) Movie Review Analysis using Natural Language Processing (NLP).
4) Analysis of Most Connected Hubs in Socially Connected People.
5) Analysis of Loan Sanction for particular customers in a Bank.
6) Prediction of Medical Expenses of Incoming Patients in a Particular Hospital.
7) Cluster Analysis of Collection of People impacting a Social Networking Site.
8) Text Mining with Predictive Analysis of Spam Filtration in Incoming Mails.
9) Expectation Maximization Analysis for Digit Classification.
10) Exploration of Murders according to Investigation Team Uniform Crime Reports.

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.