How to reshape for a Linear SVC
<p>I currently have a list of 35 features which we are using to predict and outcome. I understand the nature of the depreciation error, and my understanding of how to solve is by reshaping the np array to account for each feature:</p>

<pre><code>X = X.reshape(len(FEATURES), -1)
</code></pre>

<p>However, I am unsure if this should be included in the Build_Data_Set function or in the Analysis function prior to the for loop?</p>

<pre><code>FEATURES = [...#list of 35 columns for the below dataframe]

def Build_Data_Set():
    data_df = pd.read_csv('{}{}'.format(path, key_stats_csv))


    #  data[features], means we take all of the 35 features of data, convert them to just those values

    X = np.array(data_df[FEATURES].values)
    y = (data_df['status'].replace('underperform', 0).replace('outperform', 1)
                                                     .values.tolist())

    X = preprocessing.scale(X)
    return X, y


def Analysis():

    test_size = 1000

    X, y = Build_Data_Set()

    print(len(X))

    clf = svm.SVC(kernel='linear', C=1.0)
    clf.fit(X[:test_size], y[:test_size])

    correct_count = 0

    for x in range(1, test_size + 1):
        if clf.predict(X[-x])[0] == y[-x]:
            correct_count += 1

    print('Accuray: {}'.format((correct_count / test_size) * 100))


Analysis()
</code></pre>

<p>Error:</p>

<pre><code>DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17     
and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or 
X.reshape(1, -1) if it contains a single sample.
</code></pre>