# 40% correlation is taken as the threshold of strong feature
crimeData_corr = crimeData.corr()['crmrte']
selected_features_list = crimeData_corr[abs(crimeData_corr) > 0.4].sort_values(ascending=False)

# To plot data and linear regression model fit.
fig, ax = plt.subplots(round(len(selected_features_list) / 3), 3, figsize = (18, 12))
features = list(selected_features_list.index)
for i, ax in enumerate(fig.axes):
    if i < len(features):
        sns.regplot(x=features[i],y='crmrte',
                        data=crimeData[features], ax=ax)