utkarshchaurasia / haberman-cancer-analysis-svm-model

Analysis of Haberman Cancer Dataset

Haberman-Cancer-Analysis-And-ML Prediction-Model

Haberman’s Cancer Survival: Visual Exploratory Data Analysis using Python (SeaBorn ,Matplotlib)
Exploratory Data Analysis (EDA) is the series of asking questions and applying statistics and visualization techniques to answer those questions and to uncover the hidden insights from the data. A case study on the cancer survival data set is done to explore the most common EDA techniques in this Repository.

Machine Learning model is build by implementing SVM algorithm through Scikit learn and various accuracy scores such as F1 Score, Jaccard Score are calculated and Confusion Matrix is build.

Data Description :

The Haberman’s survival data set 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.

Attribute Information:
1.Age of patient at time of operation (numerical)
2.Patient’s year of operation (numerical)
3.Number of positive auxillary nodes detected (numerical)
4.Survival status (class attribute) 1 = the patient survived 2 = the patient died