Using Big data build a decision tree from the Covetype dataset: Covtype.data and output a precision.
Budget: $10 – $30 USD
Input: covetype.data
Output: a precision value from your decision tree.
Ideas: You first load the CSV dataset, parse, and convert non-numeric data to numeric. The covtype.info file says that four of the columns are actually a one-hot encoding of a single categorical feature, called Wilderness_Type, with four values. Likewise, 40 of the columns are really one Soil_Type categorical feature. The target itself is a categorical value encoded as the values 1 to 7.
To start, the data will be used as is. The DecisionTree implementation, like several in Spark MLlib
Output: a precision value from your decision tree.
Ideas: You first load the CSV dataset, parse, and convert non-numeric data to numeric. The covtype.info file says that four of the columns are actually a one-hot encoding of a single categorical feature, called Wilderness_Type, with four values. Likewise, 40 of the columns are really one Soil_Type categorical feature. The target itself is a categorical value encoded as the values 1 to 7.
To start, the data will be used as is. The DecisionTree implementation, like several in Spark MLlib
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Python
Data Processing
Data Mining
Big Data