Title of dataset:- Haberman's survival dataset
Objective:- To find patterns within the dataset to gain further understanding of the data and leverage it to choose a machine learning algorithm for predicting the survival rates of patients who undergo the surgery.
About the data:-
The dataset contains cases from a study that was conducted between
1958 and 1970 at the University of Chicago's Billings Hospital on
the survival of patients who had undergone surgery for breast
cancer.
About the data attributes:-
Age of patient at time of operation (numerical)
Patient's year of operation (year - 1900, numerical)
Number of positive axillary nodes detected (numerical)
Survival status (class attribute)
1 = the patient survived 5 years or longer
2 = the patient died within 5 year
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session/kaggle/input/habermans-survival-data-set/haberman.csv
Loading the data into a dataframe:-
df = pd.read_csv("/kaggle/input/habermans-survival-data-set/haberman.csv")
df.shape(305, 4)The data has 305 rows and 4 columns