def feature_evaluation(X: pd.DataFrame, y: pd.Series, output_path: str='.') -> NoReturn:
    '\n    Create scatter plot between each feature and the response.\n        - Plot title specifies feature name\n        - Plot title specifies Pearson Correlation between feature and response\n        - Plot saved under given folder with file name including feature name\n    Parameters\n    ----------\n    X : DataFrame of shape (n_samples, n_features)\n        Design matrix of regression problem\n\n    y : array-like of shape (n_samples, )\n        Response vector to evaluate against\n\n    output_path: str (default ".")\n        Path to folder in which plots are saved\n    '
    correlations = np.array(y.size)
    features = list(X)
    for feature in features:
        cov = np.cov(y, X[feature])
        std = (np.std(X[feature]) * np.std(y))
        pearson_correlation = (cov[0][1] / std)
        np.append(correlations, pearson_correlation)
        fig = go.Figure()
        fig.add_trace(go.Scatter(x=X[feature], y=y, mode='markers'))
        fig.update_layout(title=((feature + ' - Pearson Correlation  = ') + str(pearson_correlation)), xaxis_title=(feature + ' Feature values'), yaxis_title="House's Price")
        fig.write_image(f'{output_path}\{feature}.png', format='png')
Create scatter plot between each feature and the response.
    - Plot title specifies feature name
    - Plot title specifies Pearson Correlation between feature and response
    - Plot saved under given folder with file name including feature name
Parameters
----------
X : DataFrame of shape (n_samples, n_features)
    Design matrix of regression problem

y : array-like of shape (n_samples, )
    Response vector to evaluate against

output_path: str (default ".")
    Path to folder in which plots are saved
def feature_evaluation(X: pd.DataFrame, y: pd.Series, output_path: str='.') -> NoReturn:
    '\n    Create scatter plot between each feature and the response.\n        - Plot title specifies feature name\n        - Plot title specifies Pearson Correlation between feature and response\n        - Plot saved under given folder with file name including feature name\n    Parameters\n    ----------\n    X : DataFrame of shape (n_samples, n_features)\n        Design matrix of regression problem\n\n    y : array-like of shape (n_samples, )\n        Response vector to evaluate against\n\n    output_path: str (default ".")\n        Path to folder in which plots are saved\n    '
    correlations = np.array(y.size)
    features = list(X)
    for feature in features:
        cov = np.cov(y, X[feature])
        std = (np.std(X[feature]) * np.std(y))
        pearson_correlation = (cov[0][1] / std)
        np.append(correlations, pearson_correlation)
        fig = go.Figure()
        fig.add_trace(go.Scatter(x=X[feature], y=y, mode='markers'))
        fig.update_layout(title=((feature + ' - Pearson Correlation  = ') + str(pearson_correlation)), xaxis_title=(feature + ' Feature values'), yaxis_title="House's Price")
        fig.write_image(f'{output_path}\{feature}.png', format='png')