build classification system for breast cancer with accuracy 97-98

I am adept at using machine learning algorithms like random forest, xgboost, ada boost and neural networks in R. If you can send me a sample data set, I can help you with this. Would love to work on this with you.

Hello, Thanks for your invitation, I am a Statistician -Data Analyst-Researcher, involved in United Nations Projects in Geneva. I have done and written a lot of reports on Classification & clustering Data analysis. I will be ready to share with you some report I have already done on classification. I have already worked on Breast Cancer Data using classification Algorithm. I am available to discuss now, Hear from you soon, Best Regards, Fouad

Hello, I have real life experience with developing machine learning algorithms and solving data science problems. Have strong knowledge of Python (+ numpy, scipy, matplotlib) and Matlab.

Hi. I'm new here in Freelancer. I propose a lower price because my goal is to found good status here. So, I' m graduated in physics and studied several time about machine learning. I surelly can do this work for you, with minimal Milestone payment. Only pay if the job were done in your way. If you have any doubt, please answer me.

Hi, This seems general supervised learning problem. I've worked with supervised learning algorithms before. and this accuracy is achievable ( depends on the quality of training data set). Further more we can blend different approaches to get optimal accuracy and F score. I'll provide you with confusion matrix, it'll give detailed information about the kind of error we are producing in classifying certain classes and also it will look more mature in thesis . I'll code in python and use scikit-learn library (comfortable) for this project or I may use Matlab depending on the problem and your requirement. Code will be clean and fully documented. So don't worry which ever language I use I'll make sure its easy to understand the code. Looking forward to work with you on this project. cheers !

I'm experienced on using machine learning to predict and improve process to minimize the unwanted stuff (i.e. defectives in industry). For example I can reduce the 30% defectives to only 2% defectives by my skill on algorithms.

I am an experienced Data Scientist working in a MNC, have done various analytical projects including classification, regression etc.

Sounds like a semantic segmentation problem which I am very familiar with. Images can be segmented first and then feature extraction followed by classifier

97-98% accuracy in model is impossible in real world . we're talking about the building Machine learning model which would be robust and stable. the model should work efficiently if tried on different test data sets. 97% accurate model would certainly be overfit. In my experience in various online competitions like crowdanalytix and Kaggle ,85% accuracy on model sounds stable and fit for real world problems. Please let me know if we can work on building ~85% model accuracy.

Regular contestant of machine learning competition/challenge platform Kaggle and crowdanalytix & top ranked. Independently worked on many machine learning projects mainly in recommendation system, Forecasting, text mining, web mining, predictive modeling, Association mining, Natural Language Processing, Information Retrieval. Machine learning language and tool having expertise in R, Python, Java, Weka, Mahout, Hadoop, Hive, Hbase