# Import statements
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Read the data.
data = np.asarray(pd.read_csv('data.csv', header=None))
# Assign the features to the variable X, and the labels to the variable y.
X = data[:, 0:2]
y = data[:, 2]


# TODO: Create the model and assign it to the variable model.
# Find the right parameters for this model to achieve 100% accuracy
# on the dataset.

model = SVC(kernel='rbf', gamma=27)

# TODO: Fit the model.

model.fit(X, y)

# TODO: Make predictions. Store them in the variable y_pred.

y_pred = model.predict(X)

# TODO: Calculate the accuracy and assign it to the variable acc.
acc = accuracy_score(y, y_pred)
