Page 49 - Data Science Algorithms in a Week

Naive Bayes

return int(dic[key])
Input:

We save the data from the table in example Playing chess in the following CSV file:

source_code/2/naive_bayes/chess.csv
Temperature,Wind,Sunshine,Play
Cold,Strong,Cloudy,No
Warm,Strong,Cloudy,No
Warm,None,Sunny,Yes
Hot,None,Sunny,No
Hot,Breeze,Cloudy,Yes
Warm,Breeze,Sunny,Yes
Cold,Breeze,Cloudy,No
Cold,None,Sunny,Yes
Hot,Strong,Cloudy,Yes
Warm,None,Cloudy,Yes
Warm,Strong,Sunny,?
Output:

We provide the file chess.csv as the input to the Python program calculating the
probabilities of the data item (Temperature=Warm,Wind=Strong, Sunshine=Sunny) belonging
to the classes present in the file: Play=Yes and Play=No. As we found out earlier manually,
the data item belongs with a higher probability to the class Play=Yes. Therefore we classify
the data item into that class:

$ python naive_bayes.py chess.csv
[
['Warm', 'Strong', 'Sunny', {
'Yes': 0.6666666666666666,
'No': 0.33333333333333337
}]
]

Playing chess - dependent events

Suppose that we would like to find out again if our friend would like to play chess in the
park with us in a park in Cambridge, UK. But, this time, we are given different input data:

Temperature Wind Season Play
Cold Strong Winter No

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