--->Implement the following machine learning algorithms in R and Python for the below prescribed data-sets for classification and regression (whichever is applicable).
---> Write few lines on each applications. Try to find R2(must), RMSE (if possible) for all regression models; and for classification models find accuracy(must), ROC/AUC curve, and confusion matrix (must). Also, try to normalize the data set before you apply the concerned method, if you feel it is needed (mention the reasons to normalize). Also, in all the cases try to print actual data and predicted data column-wise.
---> Remember, both R and Python code are to be used for each algorithm and data set.
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Decision tree
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Neural Network
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Support Vector Machine
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Logistic Regression
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Naïve Baye
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K-Nearest Neighbours
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Bagging
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Random Forest
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Boosting
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AdaBoost.
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https://archive.ics.uci.edu/ml/datasets/Congressional+Voting+Records
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https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
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https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29
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https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease