import csv
import sys
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
TEST_SIZE = 0.4
def main():
# Check command-line arguments
if len(sys.argv) != 2:
sys.exit("Usage: python shopping.py data")
# Load data from spreadsheet and split into train and test sets
evidence, labels = load_data(sys.argv[1])
X_train, X_test, y_train, y_test = train_test_split(
evidence, labels, test_size=TEST_SIZE
)
# Train model and make predictions
model = train_model(X_train, y_train)
predictions = model.predict(X_test)
sensitivity, specificity = evaluate(y_test, predictions)
# Print results
print(f"Correct: {(y_test == predictions).sum()}")
print(f"Incorrect: {(y_test != predictions).sum()}")
print(f"True Positive Rate: {100 * sensitivity:.2f}%")
print(f"True Negative Rate: {100 * specificity:.2f}%")
def load_data(filename):
"""
Load shopping data from a CSV file `filename` and convert into a list of
evidence lists and a list of labels. Return a tuple (evidence, labels).
evidence should be a list of lists, where each list contains the
following values, in order:
- Administrative, an integer
- Administrative_Duration, a floating point number
- Informational, an integer
- Informational_Duration, a floating point number
- ProductRelated, an integer
- ProductRelated_Duration, a floating point number
- BounceRates, a floating point number
- ExitRates, a floating point number
- PageValues, a floating point number
- SpecialDay, a floating point number
- Month, an index from 0 (January) to 11 (December)
- OperatingSystems, an integer
- Browser, an integer
- Region, an integer
- TrafficType, an integer
- VisitorType, an integer 0 (not returning) or 1 (returning)
- Weekend, an integer 0 (if false) or 1 (if true)
labels should be the corresponding list of labels, where each label
is 1 if Revenue is true, and 0 otherwise.
"""
evidence = []
labels = []
# Dictionary of months to their corresponding numerical values
months = {
'Jan': 0,
'Feb': 1,
'Mar': 2,
'Apr': 3,
'May': 4,
'June': 5,
'Jul': 6,
'Aug': 7,
'Sep': 8,
'Oct': 9,
'Nov': 10,
'Dec': 11
}
# Visitor types as integers
visitors = {'Returning_Visitor': 1, 'New_Visitor': 0, 'Other': 0}
bools = {'TRUE': 1, 'FALSE': 0}
# CSV file is loaded as dictionary
with open(filename, 'r') as f:
csv_reader = csv.DictReader(f, delimiter=',')
for row_user in csv_reader:
evidence.append([
int(row_user["Administrative"]),
float(row_user["Administrative_Duration"]),
int(row_user["Informational"]),
float(row_user['Informational_Duration']),
int(row_user['ProductRelated']),
float(row_user['ProductRelated_Duration']),
float(row_user['BounceRates']),
float(row_user['ExitRates']),
float(row_user['PageValues']),
float(row_user['SpecialDay']),
months[row_user['Month']],
int(row_user['OperatingSystems']),
int(row_user['Browser']),
int(row_user['Region']),
int(row_user['TrafficType']),
visitors[row_user['VisitorType']],
bools[row_user['Weekend']]
])
labels.append(1 if row_user["Revenue"] == "TRUE" else 0)
# Return data only when successful
if len(evidence) == len(labels):
return evidence, labels
def train_model(evidence, labels):
"""
Given a list of evidence lists and a list of labels, return a
fitted k-nearest neighbor model (k=1) trained on the data.
"""
model = KNeighborsClassifier(n_neighbors=1)
model.fit(evidence, labels)
return model
def evaluate(labels, predictions):
"""
Given a list of actual labels and a list of predicted labels,
return a tuple (sensitivity, specificty).
Assume each label is either a 1 (positive) or 0 (negative).
`sensitivity` should be a floating-point value from 0 to 1
representing the "true positive rate": the proportion of
actual positive labels that were accurately identified.
`specificity` should be a floating-point value from 0 to 1
representing the "true negative rate": the proportion of
actual negative labels that were accurately identified.
"""
# total size
size = len(labels)
# initiating neg, pos, true neg and true pos values
n = 0
p = 0
tp = 0
tn = 0
for i in range(len(labels)):
# if negative
if labels[i] == 0:
n += 1
# if the actual label matches the predicted label
if labels[i] == predictions[i]:
tn += 1
# if positive
elif labels[i] == 1:
p += 1
if labels[i] == predictions[i]:
tp += 1
sensitivity = tp / p
specificity = tn / n
return sensitivity, specificity
if __name__ == "__main__":
main()
