Evaluating Regression Models: Metrics and Loss Functions: Some visualisations | Saylor University

Some visualisations

Avocado Prices

# Checking for distributions
def dist_custom(dataset, columns_list, rows, cols, suptitle):
    fig, axs = plt.subplots(rows, cols,figsize=(16,16))
    fig.suptitle(suptitle,y=1, size=25)
    axs = axs.flatten()
    for i, data in enumerate(columns_list):
        sns.kdeplot(dataset[data], ax=axs[i], fill=True,  alpha=.5, linewidth=0)
        axs[i].set_title(data + ', skewness is '+str(round(dataset[data].skew(axis = 0, skipna = True),2)))

dist_custom(dataset=raw_df1, columns_list=numeric_columns, rows=3, cols=3, suptitle='Avocado Prices: distibution for each numeric variable')
plt.tight_layout()

Nine density plots visualizing avocado price and volume data. Each plot shows a skewed distribution with text labels.

Boston House Prices

dist_custom(dataset=raw_df2, columns_list=numeric_columns_boston, rows=4, cols=3, suptitle='Boston House Prices: distibution for each numeric variable')
plt.tight_layout()

Twelve density plots visualize Boston housing prices for each numeric value.