Objective is to perform exploratory data analysis on "Haberman’s Cancer Survival data set" and note the observations on data in plain crispy language.
Perform a analysis on this data set with the following sections:
- High level statistics of the data set: number of points, number of features, number of classes, data-points per class.
- Perform Univaraite analysis(PDF, CDF, Boxplot, Voilin plots) to understand which features are useful towards classification.
- Perform Bi-variate analysis (scatter plots, pair-plots) to see if combinations of features are useful in classfication.
- Write your observations in english as crisply and unambigously as possible. Always quantify your results.
Click here for more details about data set.
Solution: (Python code below)
Observations:
- Number of data points are 305 and number of features are 4 as patient age, operation_year, axillary_node_count and survival_status
- Almost 73% of the patient survived beyond 5 years and almost 27% of the patients died within 5 years. 224 number of data points found for class, patients survived beyond 5 years. 81 number of data points found for class, patients died within 5 years . This is imbalanced data set
- Objective : Our main objective is to find out featues and conbibation of features which are useful towords classification
- Survival satus is only the feature useful towords classification
- No combination of feature found towards classification of data
- Quantifiable other observations are provided above below the plotted graphs.
Python Code Jupyter Notebook on Github - Click here
