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This document contains code snippets in Python for analyzing stock market data, normalizing features, performing grid search for an SVM classifier, and using the classifier to make predictions and tweet the results. The snippets load CSV data, sort values by date, normalize features, tune an SVM using grid search, make predictions, and tweet the prediction using the Twitter API.
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? SVM
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clf = svm.SVC()
tuned_parameters = {'kernel': ['rbf'], 'gamma': [0.2,0.1,0.08,0.07,0.06,0.04,0.03,0.02],
'C': [10,20,30,40,50]}
grid = grid_search.GridSearchCV(estimator=clf, param_grid=tuned_parameters)
grid.fit(features, labels)
print (grid.grid_scores_)](https://image.slidesharecdn.com/bot-160709134006/85/Bot-7-320.jpg)
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