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Exploratory Data analysis using Python

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EDA on Breast Cancer Survival 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. Objective To analyse and find out the possibility of patients who will survive beyond 5 years after surgery based on the attributes like Age, Operation Year and Axillary nodes Dataset Information Study that was conducted between 1958 and 1970 Conducted at the University of Chicago's Billings Hospital Survival rate of patients who had undergone surgery for breast cancer. Patient entries count is 306 Exploratory Data Analysis Based on our analysis, we came up with below observations. Questions What is the overall survival rates? Which year has the maximum number of surgeries? Which year has lowest success rate in surgeries? Does the age factor decide the survival chance? Does the axillary nodes deci

What is Machine Learning

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Machine learning is mainly focused on the development of computer programs which can teach themselves to grow and change when exposed to new data. Machine learning studies algorithms for self-learning to do stuff. It can process massive data faster with the learning algorithm. For instance, it will be interested in learning to complete a task, make accurate predictions, or behave intelligently. Why we need Machine Learning:-  Data is growing day by day, and it is impossible to understand all of the data with higher speed and higher accuracy. More than 80% of the data is unstructured that is audios, videos, photos, documents, graphs, etc. Finding patterns in data on planet earth is impossible for human brains. The data has been very massive, the time taken to compute would increase, and this is where Machine Learning comes into action, to help people with significant data in minimum time. Machine Learning is a sub-field of AI. Applying AI, we wanted to build better and intelligen