26.05.2016       Выпуск 127 (23.05.2016 - 29.05.2016)       Статьи

Прогнозирование спроса с BigQuery и TensorFlow machine learning TensorFlow

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Machine Learning with Tensorflow

We'll use 80% of our dataset for training and 20% of the data for testing the model we have trained. Let's shuffle the rows of the Pandas dataframe so that this division is random. The predictor (or input) columns will be every column in the database other than the number-of-trips (which is our target, or what we want to predict).

The machine learning models that we will use -- linear regression and neural networks -- both require that the input variables are numeric in nature.

The day of the week, however, is a categorical variable (i.e. Tuesday is not really greater than Monday). So, we should create separate columns for whether it is a Monday (with values 0 or 1), Tuesday, etc.

Against that, we do have limited data (remember: the more columns you use as input features, the more rows you need to have in your training dataset), and it appears that there is a clear linear trend by day of the week. So, we will opt for simplicity here and use the data as-is. Try uncommenting the code that creates separate columns for the days of the week and re-run the notebook if you are curious about the impact of this simplification.






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