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# Define X and y | |
X = df.drop('y', axis=1) | |
y = df.y | |
# Loop to select random columns to be used in classifier | |
random_cols = [] | |
for i in range(4): | |
rand_col = 'X' + str(random.randint(1,178)) |
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X = X[random_cols] | |
# Train-test-split | |
X_train, X_test, y_train, y_test = train_test_split(X, y, | |
test_size=0.25, | |
random_state=42) | |
# Perform feature scaling | |
scaler = StandardScaler() | |
X_train = scaler.fit_transform(X_train) |
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plot_confusion_matrix(knn, X_test, y_test, display_labels=['No Seizure', | |
'Seizure'], | |
normalize='true', cmap='Blues'); |
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import requests | |
import pandas as pd | |
import json |
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{“api_key”: “paste_your_actual_api_key_here”} |
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def get_keys(path): | |
with open(path) as f: | |
return json.load(f) |
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keys = get_keys("/Users/{your_username}/.secret/api_football.json") | |
api_key = keys['api_key'] |
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url = "https://api-football-v1.p.rapidapi.com/v2/leagues/seasonsAvailable/524" | |
headers = { | |
'x-rapidapi-host': "api-football-v1.p.rapidapi.com", | |
'x-rapidapi-key': api_key | |
} | |
resp = requests.request("GET", url, headers=headers) |
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resp.status_code == requests.codes.ok |
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print(resp.text) |