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+# ML-Project-Cancer-Prediction
+My Kaggle Project(Based on Medical Treatment)
+Kaggle Problem link: 
+Some libraries/Subpackages used are: https://www.kaggle.com/c/msk-redefining-cancer-treatment
+1.nltk
+2.sklearn.calibration
+3.sklearn.naive_bayes
+4.mlxtend.classifier
+5.sklearn.linear_model
+6.seaborn
+7.sklearn.metrics 
+
+Algorithm applied:
+1.Naive Bayes
+2.Random Forest(using oneHotEncoding)
+3.Random Forest(using ResponseEncoding)
+4.Logistic Regression
+4.Linear Support Vector Machine(Linear SVM)
+5.K Nearest Neighbours
+6.Stacking Model
+7.Maximum Voting classifier
+
+Evaluation is done on basis of multi log-loss
+1.Log-loss for Naive Bayes Model is: 1.2174351082980228
+2.Log-loss for Logistic Regression is: 1.0139465030649317
+3.Log-loss for KNN is : 0.9686092822627863
+4.Log-loss for LinearSVM is: 1.0518829636631724
+5.Log-loss for Random Forest Classifier is : 1.1440820641479814(using one hot encoding) and 1.220569827205813(using Response Encoding)
+6.Log-loss for Stacking Model is:[training_set:0.497983218669304,test_set:1.1751619600947567,cross_validation_set:1.09245098514981767
+7.Log-loss for Maximum Voting Classifier is:[training_set: 0.8677287779975493,test_set:1.2148355813599823,cross_validation_set:1.142669504]
+
+As per the Evaluation Logistic Regression is the best Model to be fitted in this problem .