Amount Withdrawn Model Part 2

Univariate visualization

features = exploration_dict['possible_continuos_features']
dataset[features].hist(figsize=(20, 20));

dataset[features].plot(kind='density', subplots=True, layout=(4, 3), 
                  sharex=False, figsize=(20, 20));

# Sometimes you can analyze an ordinal variable just as numerical one

fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(30, 30))
for idx, feat in enumerate(features):
    ax = axes[int(idx / 4), idx % 4]
    sns.boxplot(x=feat, data=dataset, ax=ax)
    ax.set_xlabel(feat)
    ax.set_ylabel('')
fig.tight_layout();

from pandas.plotting import scatter_matrix
%config InlineBackend.figure_format = 'png'
style.use('tableau-colorblind10')

MultiVariate Visualizations

scatter_matrix(dataset[features],figsize=[40,40]);

sns.set(style="white")
corr = dataset[features].corr()
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
f, ax = plt.subplots(figsize=(20, 11))
cmap = sns.diverging_palette(220, 10, as_cmap=True)
sns.heatmap(corr, mask=mask,cmap=cmap,  vmax=.3, center=0,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})

<matplotlib.axes._subplots.AxesSubplot at 0x7f6c973e3ef0>

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Amount Withdrawn Model Part 1

Image
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