Data sets The data is a subset of the US4 Wind Turbines data set, containing about 70,000 wind turbines, with their characteristics. The data sets for training are X_train.pkl, y_train.pkl They contain 50,000 examples of the original dataset with some preprocessing.Specifically, small noise was added to a number of features, and other features have been removed. Each line in the inputs file represents the features of one turbine. Your task is to predict the turbine capacity from the available input features. The loss function is the squared error L_LS.
Note that the dataset is presented in a pickle format to reduce file sizes. To read these files and convert them into txt format (so that they are readable in R), please run the script unpack_files.py which is also posted in the same folder (the package numpy for Python is required for running this script). More instructions will come later. Use these data as you wish to obtain your predictor. You are encouraged to consult the original data web site for more information about wind turbines, or to see plots of data samples. But you are not allowed to download the original data or to try to compare the data sets provided with the original data. Later, we will post an unlabeled test set, with the same format, on which you will test the predictor you obtained.

Methods for prediction You will use the data made available to construct your predictor. You need to register (more instructions later) by Nov 22 end of day. Below is a list of possible predictors. find the list, with short clarifications for each model. No matter what method you choose, you are responsible for knowing how this method works, and for explaining how you chose parameters for training. Demonstrating that you understand how to use a predictor is the most important goal of this project.

List of Predictors
• Regression tree: single decision tree with branches predicting labels 0...9
• Bagged regression tree/Random Forest: an ensemble of decision trees obtained by either randomizing the construction of the trees, or by resampling the training set
• Neural net >=2 layers: a multilayer neural network
• K-NN: K-nearest neighbors [you can choose the distance]
• SVM - regression SVM with your choice of kernel
• Linear Least Squares regression -- you can include higher order terms
• Kernel regression: Nadaraya Watson, or higher order (e.g. local linear regression)

Software resources You are allowed to download software for this project. For any ML software you use, you must know intimately what the software is doing in the context of your project. You must also demonstrate by your project that you mastered various issues of the process of data analysis/prediction. You will be graded mostly (this will become more precise eventually) on your intellectual contribution to the project and only secondarily on the performance/sophistication of the methods borrowed from others.

Generic machine learning packages
• scikit-learn (Python)
• Weka (Java)
• pmtk3 (matlab)
• SVM packagages: SVM-torch, SVM-light, LibSVM
• TensorFlow (especially for neural nets, but other methods included) (Python)
• more TB posted

• for the ~15,000 examples in the test set, you will produce ~15,000 labels with values output by your Predictor. Create the file y.out with the following format:
  predicted_mean_square_error
  yhat_example_1
  yhat_example_2
  ....
  (15001 lines)
  E.g. y.out
  For the value of predicted_mean_square_error you should input your best guess of how your method will perform on the training set.
  We will use a script to download and evaluate your results, so please do not vary from these format or file name, lest your prediction error be distorted.
• Go to the Canvas dropbox. Upload the files.
• T.B. set upEnter also: the names of the method used by your Predictor.