Building Profit Function model based on transaction Data o The goal of this project was to develop methods and architectures for proactive data replication management for shopping Mall, based on brand survival, loyalty models and on the prediction of future purchase behavior of customer using SAS and Excel.

The aim of analysis is to identify the root cause of the problem (i.e. cancellation and non-availability of cars) and recommend ways to improve the situation. Result of our analysis, we have presented to the client with root cause(s) and possible hypotheses of the problem(s) and recommend ways to improve them using Excel and R.

A lot has been said during the past several years about how precision medicine and, more concretely, how genetic testing is going to disrupt the way diseases like cancer are treated.

But this is only partially happening due to the huge amount of manual work still required. Once sequenced, a cancer tumor can have thousands of genetic mutations. But the challenge is distinguishing the mutations that contribute to tumor growth (drivers) from the neutral mutations (passengers).

Currently this interpretation of genetic mutations is being done manually. This is a very time-consuming task where a clinical pathologist has to manually review and classify every single genetic mutation based on evidence from text-based clinical literature.

We need to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classifies genetic variations.

This problem was a competition posted on Kaggle with a award of $15,000. This was launched by Memorial Sloan Kettering Cancer Center (MSKCC), accepted by NIPS 2017 Competition Track, because we need your help to take personalized medicine to its full potential.

You can check all details about the competition from following link :https://www.kaggle.com/c/msk-redefining-cancer-treatment

In order to get the dataset please create a login account to Kaggle and go to this problem statement page(given above) and download 2 dataset

training_variants.zip and training_text.zip