Linear SVC on iris data set

linear_svc.py

import pandas as pd
import numpy as np

# Read in iris data set
iris = pd.read_csv("https://raw.githubusercontent.com/Thinkful-Ed/curric-data-001-data-sets/master/iris/iris.data.csv")

# Add column names
iris.columns = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'Species']

# Split data into features and target
X = iris.ix[:, 0:4]
y = iris.ix[:, 4:]

# Train, test split
X_train, X_test, y_train, y_test = train_test_split(columns, y, test_size=0.40, random_state=42)

from sklearn.svm import LinearSVC

# Build Linear Support Vector Classifier
clf = LinearSVC()
clf.fit(X_train, y_train)

# Make predictions on test set
predictions = svc.predict(X_test)

from sklearn.metrics import accuracy_score

# Assess model accuracy
result = accuracy_score(y_test, predictions, normalize=True)

print result